Jean-Phillippe Dominique

Jean Arnauld

Mariano, Wasim, Sophie -Lopez

Maria, Slm, Saby, Zaineb, Marilou, Megane, Irene Sebastian Melanie

Dr Vincent Jallu

Dr Sophie Abby

Prof N Srinivasan

Dr Salwa Karboune

Dr Pierrick Craveur

Madam M Matthieu Gouget

Madam Sali Anies

Sarah Kaddah

Madam Soubika

Dr Nandita Bacchhawat

Dr Srikrishna Subramanian

M Gabriel

M Kareem

M Habib

M Aziz

M Jamal

M Nabeel

Madame Sali

Keywords: Flexibilité de la structure des protéines, allostérie, Blocs Protéiques, fragments de double personnalité, modification post-translationnelle, Intégrine αIIbβ3, Thrombasthénie de Glanzmann, thrombocytopénie alloimmune foetale / néonatale, paludisme à Plasmodium vivax, récepteurs des chimiokines Protein structural flexibility, Allostery, Protein Blocks, Dual personality fragments, Post-translational modifications, Integrin αIIbβ3, Glanzmann Thrombasthenia, Fetal/neonatal Alloimmune Thrombocytopenia, Plasmodium vivax malaria, Duffy Antigen Chemokine Receptors, Molecular phylogenetics

Dynamique des structures protéiques et son impact sur les comportements structuraux locaux Les structures protéiques sont de nature hautement dynamique contrairement à leur représentation dans les structures cristallines. Une composante majeure de la dynamique structurelle est la flexibilité des protéines inhérentes. L'objectif principal de cette thèse est de comprendre le rôle de la dynamique inhérente dans les structures protéiques et leur propagation. La flexibilité des protéines est analysée à différents niveaux de complexité structurelle, du niveau d'organisation primaire au niveau quaternaire. Chacun des cinq premiers chapitres traite un niveau différent d'organisation structurelle locale avec le premier chapitre traitant des structures secondaires classiques tandis que le second analyse la même chose en utilisant un alphabet structurel -les blocs protéiques. Le troisième chapitre se concentre sur l'impact d'événements physiologiques spéciaux comme les modifications post-traductionnelles et le désordre sur les transitions d'ordre sur la flexibilité des protéines. Ces trois chapitres indiquent une mise en oeuvre dépendante du contexte de la flexibilité structurelle dans leur environnement local. Dans les chapitres suivants, des structures plus complexes sont prises en compte. Le chapitre 4 traite de l'intégrine αIIbβ3 impliquée dans des troubles génétiques rares. L'impact des mutations pathologiques sur la flexibilité locale est étudié dans deux domaines rigides de l'intégrine αIIbβ3 ectodomaine. La flexibilité inhérente dans ces domaines est montrée pour moduler l'impact des mutations vers les boucles. Le chapitre 5 traite de la modélisation structurelle et de la dynamique d'une structure protéique plus complexe du récepteur des chimiokines des antigènes du groupe Duffy incorporé dans un système de membrane mimétique érythrocytaire. Le modèle est soutenu par l'analyse phylogénétique la plus complète sur les récepteurs de chimiokines jusqu'à ce jour, comme expliqué dans le dernier chapitre de la thèse.

 310-helix................................................................................................52 1.7 ................................................................................................85 2.9 Evolution of clusters for PB b.................................................................................................86 2.10 Evolution of clusters for PB f................................................................................................87 2.11 Evolution of clusters for PB g...............................................................................................88 2.12Protein are the mediators of information content stored in the DNA and perform myriad of functions inside the cell. From being signalling molecules [START_REF] Nishizuka | The role of protein kinase C in cell surface signal transduction and tumour promotion[END_REF] to enzymes [START_REF] Agarwal | Role of Protein Dynamics in Reaction Rate Enhancement by Enzymes[END_REF] to cellular receptors [START_REF] Gilman | G Proteins: Transducers of Receptor-Generated Signals[END_REF] to forming cytoskeleton [4], they are involved in regulating the cellular function at every step.

Genetic information stored in the DNA is transcribed by RNA polymerase into an mRNA transcript which is then used by ribosomes to translate a protein biopolymer. Such sequential transfer of information from DNA to RNA to Protein is known as the central dogma of molecular biology [5]. For a protein to mediate its function, it is required to fold correctly and therefore the relation between protein structure and function is quintessential [6].

I.1.1 Protein sequence

Proteins are made up of amino-acids attached to one another via peptide bonds [START_REF] Jalkanen | Amino Acids and Small Peptides as Building Blocks for Proteins: Comparative Theoretical and Spectroscopic Studies[END_REF]. Amino acids are the organic molecules containing amine (-NH2) and carboxyl (-COOH) groups as well as an R (side-chain) group which is specific to each amino acid (Fig I.1a). A presence of the amine and carboxyl group states that between pH 2.2 -9.4, the amino acids contain a negative carboxylate as well as positive ammonium group, hence existing as Zwitterions. Based on the R group, the amino acids can be classified into polar/nonpolar or aliphatic/aromatic or charge/uncharged residues (Fig I.1b). Generally, the non-polar residues form the core of the protein structures while the polar residues are present on the surface to carry out important functions such as catalysis [START_REF] Dill | Dominant forces in protein folding[END_REF]. The structure of a protein is dictated by its sequence. Anfinsen, in early 70s conducted a very elegant experiment to prove this characteristic point [START_REF] Anfinsen | Principles that Govern the Folding of Protein Chains[END_REF]. The paradigm of sequence-structurefunction relationship holds true for majority of the proteins. It states that if two proteins have similar sequences, then it is highly likely that they have similar structures which in turn implies that they should have similar function [START_REF] Koonin | Beyond complete genomes: from sequence to structure and function[END_REF][START_REF] Sadowski | The sequence-structure relationship and protein function prediction[END_REF]. This underlying principle has been extensively used to predict function for a given protein [START_REF] Fetrow | Method for prediction of protein function from sequence using the sequence-to-structure-to-function paradigm with application to Glutaredoxins/Thioredoxins and T 1 Ribonucleases 1 1Edited by F. Cohen[END_REF][START_REF] Lee | Predicting protein function from sequence and structure[END_REF]. In order to classify protein structures, it is important to understand the recurring patterns in their 3D arrangement. Ramachandran et al used a model system of two-linked peptide units to identify those conformations which displayed no shortcontact between any two atoms [START_REF] Ramachandran | Stereochemistry of polypeptide chain configurations[END_REF]. The conformations were selected based on the accessible rotations around N-Cα and C-Cα single bonds called phi (Φ) and psi (Ψ) dihedral angles respectively (Fig I.2).

The values obtained for each accessible Φ and Ψ were plotted as what is famously known today as Ramachandran map. It was observed that the polypeptide backbone can take up certain conformations which are allowed according to the Ramachandran map confirming the occurrence of regular structures in proteins.

I.2.1 Primary structure

An organisation of amino-acids in a linear fashion, next to one another, depicts the primary structure of the protein (Fig I.3). Sequential arrangement of amino acids in a polypeptide chain refers to its primary structure. A protein generally adopts this conformation during the translation process, when the peptide is being polymerized from the 'P' site of the ribosome.

I.2.2 Secondary structure

The second level of structural organisation in a protein is defined by secondary structures and is governed by a highly regular, local sub-structural arrangement of polypeptide backbone. Traditionally, these include α-helices and β-sheets (Fig I .3) which are identified by a fixed intra and inter-chain hydrogen-bonding pattern. These secondary structures have a fixed geometry defined by their backbone dihedral angles (Φ and Ψ) and are known to occupy definite location in the Ramachandran map. Example, α-helices occupy the Φ, Ψ position -57, -47 while β-sheets typically occupy position -140, 130. Apart from α-helices, the most prominent helices occurring in the protein, 310 and π-helices are other types of helices that occur in proteins. These types of 3 helices are distinguished by the H-bonding pattern between them. The intra-chain H-bond is formed between i and i+4 residue in the α-helix, between i and i+3 residue in the 310-helix and i and i+5 residue in the π-helix [START_REF] Bolin | α and 310: The Split Personality of Polypeptide Helices[END_REF][START_REF] Low | THE π HELIX-A HYDROGEN BONDED CONFIGURATION OF THE POLYPEPTIDE CHAIN[END_REF]. The Φ and Ψ values for these helices are given in Table I Apart from these, proteins can have also kinks in helices which is often introduced due to a proline residue in the middle of α-helix [START_REF] Von Heijne | Proline kinks in transmembrane α-helices[END_REF]. Separately, polyproline helices and collagen triple helix enjoy status of being special cases of secondary structure elements which occur at different Φ, Ψ values than the regular helices and sheets [START_REF] Craveur | Protein flexibility in the light of structural alphabets[END_REF][START_REF] Ramachandran | Interchain hydrogen bonds via bound water molecules in the collagen triple helix[END_REF]. β-sheets are formed by arrangement of βstrands in register with each other. They are also classified further into antiparallel and parallel βsheets depending upon the direction of the strands in register which in turn decides the geometry of H-bond. In an antiparallel arrangement, the consecutive β-strands are in opposite direction to each other such that the N-terminus of one strand is adjacent to the C-terminus of the next (Fig I.4a). This results in a planar H-bond arrangement which deems most suitable for the stability of the β-sheets. The backbone dihedral angles Φ and Ψ for antiparallel sheets are -140°, 135°. On the other hand, parallel β-sheets have the two strands running in same direction making the inter-strand H-bond slightly out-of-plane (Fig I.4b), hence lowering the stability of parallel sheets when compared to antiparallel. The dihedral angles Φ and Ψ are -120°, 115° for the parallel sheets.

Strands are rarely long, maximum 15 residues in length and most β-sheets contain less than 6 strands. Side chains from adjacent residues of a strand in a β-sheet are found on opposite sides of the sheet and do not interact with one another. Therefore, like α-helices, β-sheets have the potential for amphiphilicity with one face being polar and the other being non-polar. It has also been noted that parallel sheets are generally buried inside while antiparallel sheets have one side exposed to the solution [START_REF] Baker | Hydrogen bonding in globular proteins[END_REF]. Like the kinks in helices, β-sheets are known to have β-bulges which are caused by interruption in the hydrogen bonding [START_REF] Richardson | The beta bulge: a common small unit of nonrepetitive protein structure[END_REF]. A β-bulge is a region between two consecutive β-type hydrogen bonds which includes two residues (positions 1 and 2) on one strand opposite a single residue (position x) on the other strand. Two common types of β -bulges are known viz. the "classical" β-bulge and the "G1" β-bulge. The main functional attribute of β-bulges is to accommodate for any single residue insertion or deletion within a β-structure [START_REF] Richardson | The beta bulge: a common small unit of nonrepetitive protein structure[END_REF].

Besides helices and sheets exist a third kind of structural element called turns which help in reversing the direction of a polypeptide chain. Turns are mainly found on the protein surface and hence contain polar or charged residues and were first identified in protein structures by

Venkatachalam [START_REF] Venkatachalam | Stereochemical criteria for polypeptides and proteins. V. Conformation of a system of three linked peptide units[END_REF]. Turns are further classified into α-, β-and γ-turns, out of which β-turns are the most common ones consisting of a sequence of four residues They were defined as linked by a 1-4 (310-type) hydrogen bond between the -C=O of the first residue and the -NH of the fourth residue [START_REF] Baker | Hydrogen bonding in globular proteins[END_REF].

α-helices and β-sheets are composed of repetetive units of particular hydrogen bonding patterns as shown in Table I. 1 and Fig. I.4. These repetetive units are classified based on their hydrogen bonding and length. A β-bridge is a singelton hydrogen bond observed in isolation with a length of 3 to 4 residues. When multiple bridges exist together, they form a β-sheet [START_REF] Richardson | The beta bulge: a common small unit of nonrepetitive protein structure[END_REF]. If such hydrogen bonding is missing but the local curvature around the Cα atoms has an angle of 70, it is classified as a bend. A bend is the only secondary structure element whose principle identification is not done by hydrogen bonding pattern [START_REF] Richardson | The beta bulge: a common small unit of nonrepetitive protein structure[END_REF]. The structural examples of bend and bridges can be seen in Fig. 1.5

I.2.3 Super secondary structures

The secondary structural elements can come together in more than one ways to form some higher order structures. Their structural complexity is smaller than the tertiary structures and denote topological arrangement of helices, sheets and turns. These are called as super-secondary structures and usually occur as small yet functionally important structural motifs. These motifs are generally involved in either biological or structural functions. Some examples include the helixturn-helix motif which is known to bind DNA [START_REF] Harrison | DNA Recognition By Proteins With The Helix-Turn-Helix Motif[END_REF], the EF-hand motif known to bind Ca 2+ [START_REF] Lewit-Bentley | EF-hand calcium-binding proteins[END_REF] and β-hairpin motif which plays a structural role and connects two antiparallel β-strands [START_REF] Sibanda | β-Hairpin families in globular proteins[END_REF].

I.2.4 Tertiary and quaternary level of protein folding

Tertiary structure denotes the 3D arrangement of the secondary structure elements to form a well-folded functional polypeptide (Fig I .3). Tertiary structure is generally defined for a single polypeptide chain where the interior of the folded protein is known as core and is formed by hydrophobic amino acids. The concept of protein domains can be defined at this level. Protein domains are the compact globular modules that are capable of folding and functioning independently of the rest of the protein. Within a protein, different domains can be identified e.g. ligand-binding domain, DNA-binding domain etc.

In many cases, two or more tertiary structures join together to constitute the functional state of a protein. Such organisation of two or more tertiary structures is called quaternary structure of a protein (Fig I.3). Each polypeptide chain in the quaternary structure is termed as a subunit. In other words, quaternary structure can be a homomer, formed from the self-assembly of repeated copies of a single subunit. On the other hand, heteromeric complexes are composed of multiple distinct protein subunits, usually encoded by different genes [START_REF] Teichmann | Evolution and dynamics of protein complexes[END_REF]. Classical example of proteins with similar structure, but one fully functional in tertiary state and the other in quaternary is

Myoglobin and Haemoglobin where the former is functional as a single chain while the latter requires association of 4 chains to form a functional molecule [START_REF] Fanelli | Hemoglobin and Myoglobin[END_REF].

I.3 Structural and functional classification of proteins

Proteins can be classified into various groups depending upon sequence or structural similarity.

The classification of proteins becomes important to propose function for a novel protein. One of the ways to classify proteins is to group them into families and superfamilies. A protein family consists of a set of proteins that are evolutionarily related by virtue of similarities in sequence or structure and function. The families can be arranged into hierarchy where the proteins having a common ancestor are grouped into smaller subgroups, indicating more closely related members, called subfamilies. A superfamily consists of different families of proteins where the members within the superfamily are distantly related [START_REF] Dayhoff | The origin and evolution of protein superfamilies[END_REF]. A schema of classifying proteins into families is shown in Figure I.5. According to SCOP [START_REF] Hubbard | SCOP: a Structural Classification of Proteins database[END_REF], the protein structures can be classified into following categories depending upon similarities between protein domains:

I.4 Protein types based on cellular environment

Protein structures have to correctly fold to perform correct function. Depending on the nature of cellular environment and functional requirements proteins can be either globular, fibrous or membrane proteins.

I.4.1 Globular proteins

Globular proteins are those polypeptide chains that fold into a compact shape. They are the most common protein types. These proteins have a well-defined hydrophobic core such that the apolar residues face towards protein interior while the polar residues face outwards. Functionally these proteins can be enzymes, regulatory proteins, messengers, and transporters etc. Many different folds are associated with globular proteins.

I.4.2 Fibrous proteins

On the other hand, fibrous proteins are generally elongated and are mostly involved in cellular support and structural functions. They are more stable than globular proteins. Some very well characterized fibrous proteins include collagen, actin, myosin, and keratin etc.

I.4.3 Membrane proteins

As the name suggests, membrane proteins are the ones that interact with phospholipid membrane. They can be integral membrane proteins which can be permanently attached to the membrane or peripheral membrane proteins which are temporarily attached to the lipid bilayer [START_REF] Johnson | Amphitropic proteins: regulation by reversible membrane interactions (Review)[END_REF]. The integral membrane proteins are transmembrane proteins which span across the membranes. It can either be single pass (passing the membrane only once) or multi-pass membrane Coordinates for 139717 structures have been deposited in the protein data bank (PDB) as of April 29, 2018. Three main experimental methods exist to determine the structure of a protein. These are X-ray crystallography, nuclear magnetic resonance (NMR) Spectroscopy and cryo-electron microscopy (cryoEM). Out of these, X-ray crystallography has been the method of choice for solving majority of the structures. Though, in the recent years, cryoEM is becoming a popular method to solve the protein structures, especially macromolecular assemblies.

I.5.1 X-ray Crystallography

X-ray crystallography is a technique used to determine the atomic structure of a protein.

The technique itself is more than a 100 years old but became a popular method of choice for protein structure determination since 1950s when Sir John Kendrew first solved the structure of sperm whale myoglobin [START_REF] Kendrew | A three-dimensional model of the myoglobin molecule obtained by X-ray analysis[END_REF]. Since then, 123230 structures solved by crystallography have been deposited in PDB so far. Following structure determination of the lysozyme from bacteriophage T4 (T4 lysozyme) [START_REF] Matthews | The Three Dimensional Structure of the Lysozyme from Bacteriophage T4[END_REF], it became a prototype for the study of protein folding and thermodynamics [START_REF] Baase | Lessons from the lysozyme of phage T4[END_REF].

Briefly, crystallography requires protein crystallisationa process of forming protein crystals. A unit cell is the crystal repeating unit which defines the smallest group of atoms which has the overall symmetry of a crystal, and from which the entire lattice can be built up by repetition in three dimensions. The protein atoms systematically arrange themselves in three dimension in a unit cell. The protein crystal is then exposed to X-rays causing diffraction according to the Bragg's law; which states that a constructive interference happens when the condition 'nλ=2dsinθ' is satisfied, where d is the distance between two planes, θ is the angle of incidence and λ is the wavelength of X-ray beam (Fig I A three-dimensional picture of the electron density within the crystal is produced by measuring the angles and intensities of these diffracted beams. The main challenge in generating the electron density from the diffraction pattern is deciphering the phases which are lost while collecting the diffraction data. This is the notorious phase problem in crystallography, which is the problem of loss of information concerning the phase that can occur when making a physical measurement [START_REF] Cowtan | Phase Problem in X-ray Crystallography, and Its Solution[END_REF][START_REF] Hauptman | The Phase Problem of X-ray Crystallography: Overview[END_REF]. The phases in crystallography can be obtained by various methods such as Besides, the uncertainty value induced by phase problem there lies another concern with the X-ray technique-The packing of the structure in the crystal. [START_REF] Kelly | Crystallography and Crystal Defects[END_REF]. Although X-ray crystallography gives detailed atomic information about the structure, all the interactions observed in the packed crystals may not be biologically relevant. Many of them may be an artifact of crystal packing and may not be observed in solution. Differentiating the true interactions from such non-specific interaction may become a daunting task [START_REF] Bernauer | DiMoVo: a Voronoi tessellation-based method for discriminating crystallographic and biological protein-protein interactions[END_REF][START_REF] Krissinel | Inference of Macromolecular Assemblies from Crystalline State[END_REF]. An analysis of general interface properties has revealed some features to distinguish specific vs non-specific interactions within crystals [START_REF] Janin | Protein-protein interaction at crystal contacts[END_REF][START_REF] Carugo | Protein-protein crystal-packing contacts[END_REF]. These properties include interface area, composition of the interface, spatial distribution of the interface residues, secondary structure, core interface conservation and the space group to which they belong. A recent study has shown that many of these properties are indistinguishable for the specific and non-specific interactions [START_REF] Luo | A structural dissection of large protein-protein crystal packing contacts[END_REF] and hence one has to be cautious while analysing proteinprotein contacts obtained from the crystal structures.

I.5.2 Nuclear Magnetic Resonance-NMR

NMR is the second most common method to determine protein structures. Most NMR structures consist of a single type of polypeptide chain and a majority of the structures solved using NMR are monomers [START_REF] Marsh | Structure, dynamics, assembly, and evolution of protein complexes[END_REF]. This is because smaller proteins are easily characterized using NMR than the larger proteins and hence the proteins that tend to exist as huge oligomers are not amenable to structure determination using NMR. It has been shown that since 2005, the number of structures determined using NMR in PDB has sharply decreased showing a fall in the popularity of the method [START_REF] Marsh | Structure, dynamics, assembly, and evolution of protein complexes[END_REF]. Even though NMR spectroscopy is usually limited to proteins smaller than 35 kDa, it is often the only method to study the conformational heterogeneity and intrinsically disordered nature of proteins.

NMR exploits the quantum mechanical properties of the central core ("nucleus") of the atom. These properties depend on the local environment of the molecules and their measurement provides a map of how the atoms are chemically linked, how close are they in space, and how rapidly they move with respect to each other. Principle behind obtaining NMR spectra is that each distinct nucleus in a protein experiences a distinct electronic environment and thus has a distinct chemical shift by which it can be recognized. A resonance assignment is obtained for the protein to find out the chemical shift corresponding to each atom. To perform structure calculations, a number of experimentally determined restraints are generated like distance restraints and angle restraints. These restraints are used as an input to generate multiple structures satisfying these restraints. Hence, NMR generates an ensemble of structures while X-ray crystallography provides one structure which generally is a space and time-averaged structural snapshot.

I.5.3 Cryo-electron Microscopy

Though X-ray crystallography is considered as the gold standard for providing atomic resolution structures, it suffers from the drawback of providing a static snapshot which may be far from the physiological structure. Also, many proteins resist crystal formation and a lot of time and effort has to be invested to solve the structure using X-ray crystallography. NMR on the other hand, though being capable of elucidating dynamics information is limited by the size considerations. Hence, cryoEM can provide solutions to these limitations such that it can be used for bigger protein complexes and can image complexes in their physiological environment [START_REF] Bai | How cryo-EM is revolutionizing structural biology[END_REF].

Although the use of cryoEM technique has been limited to medium to low (5-15 Å) resolution range yet structures with resolution better that 3 Å are getting published thus making the technique tractable [START_REF] Bartesaghi | 2 A resolution cryo-EM structure of beta-galactosidase in complex with a cell-permeant inhibitor[END_REF]. CryoEM is becoming the most sought after technique to extract structural information about the macromolecular complexes not amenable to either X-ray or NMR. After procuring a density map, the most important task is to obtain a high confident pseudo-atomic model for the same. The structures to be fitted into the electron density map can either be the crystal structures of a subcomponent or can be a homology modeled structure. If the density map resolution is better than 4 Å, de-novo modeling can be used to calculate the pseudo-atomic model.

CryoEM is a type of Transmission Electron Microscopy (TEM), in which the sample is studied at cryogenic temperatures. The information obtained is invaluable in understanding the macromolecular assembly at physiological conditions. CryoEM techniques can be classified into three types: a) Electron Crystallography, b) Single particle analysis, and c) Cryo-electron tomography. Out of these single-particle analysis or Single particle cryoEM is emerging as a technique of choice to determine 3D structure of proteins with an increasingly advancing electron beams, detectors and ability to analyse isolated complexes ("Single particles") under native conditions [START_REF] Nogales | The development of cryo-EM into a mainstream structural biology technique[END_REF]. Its emergence can be judged from the number of EM maps being released every year as well as the improvement in their resolution ranges (Fig I.9). The basic principle behind electron microscopy is the deflection of electrons in an electromagnetic field. An EM consists of an electron source, a series of lenses, and an image detecting system, which currently are high-end digital cameras [START_REF] Orlova | Structural Analysis of Macromolecular Assemblies by Electron Microscopy[END_REF]. As the electrons from the source hit the condenser lens, they are converged and fall on the object as a parallel beam. The aperture at the back focal plane of objective lens filters out the electrons scattered at very high angles, hence preventing them from reaching the image plane. Image magnification is provided by the objective lens and the projector lens. Once a good quality image is obtained, next task is to generate 3D reconstruction of the 2D projections of the objects using the phase information present in the image itself. The next important task after obtaining 3D electron density map is to calculate Hence, it becomes increasingly important to resort to the computational methods to predict three-dimensional structure of a given protein. The history of theoretically predicting the structural elements dates back to 1970s when Chou and Fasman calculated the propensities of amino acids in α-helices, β-sheets and turns [START_REF] Chou | Prediction of protein conformation[END_REF]. Since then various methods have been developed to predict the secondary structures from the sequence [START_REF] Drozdetskiy | JPred4: a protein secondary structure prediction server[END_REF][START_REF] Mcguffin | The PSIPRED protein structure prediction server[END_REF][START_REF] Heffernan | Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins[END_REF]. Protein secondary structure prediction refers to the prediction of the conformational state of each amino acid residue of a protein sequence as one of the three possible states, namely, helices, strands, or coils, denoted as H, E, and C, respectively.

Another important application of computational methods is their ability to predict tertiary structure of proteins. Three main approaches are employed in computational 3D prediction are: Given their fundamental importance in protein structures, it is important to define and characterize secondary structure elements for a given protein structure. Various standard methods are available for this purpose. Several assignment methods can be used like, DSSP [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF], STRIDE [START_REF] Heinig | STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins[END_REF] and predefined libraries of secondary structure can also be used.

I.6.1.1 DSSP -Define secondary structure of proteins

The DSSP algorithm is a standard method for assigning secondary structure to the amino acids of a protein using the coordinates of the structure. between DSSP and STRIDE since their inception. It has been shown than DSSP and STRIDE agree for 95% of the cases [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF]. I should be noted that it has been shown than both DSSP and STRIDE under-represent π-helix [START_REF] Fodje | Occurrence, conformational features and amino acid propensities for the π-helix[END_REF].

I.6.2 Secondary structure prediction

The prediction of secondary structures is based on the regular arrangement of amino acids in the secondary structures which are stabilized by hydrogen bonding patterns. The structural regularity serves the foundation for these prediction algorithms. Protein secondary structure prediction with high accuracy is not a trivial task. It has remained a very difficult problem for decades. Specifically, because protein secondary structure elements are context dependent. The formation of α-helices is determined by short-range interactions, whereas the formation of βstrands is strongly influenced by long-range interactions. Prediction for long-range interactions is theoretically difficult. Albeit, after more than three decades of effort, prediction accuracies have only been improved from about 50% to about 82%. There are many methods available for secondary structure prediction. Out of these PSIPRED is the most popular one [START_REF] Mcguffin | The PSIPRED protein structure prediction server[END_REF].

PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/) is a web-based program that predicts protein secondary structures using a combination of evolutionary information and neural networks.

PSIPRED incorporates two feed-forward neural networks which performs an analysis on output obtained from PSI-BLAST. A profile is extracted from the multiple sequence alignment generated from three rounds of the PSI-BLAST. This profile is then used as input for a neural network prediction. To achieve higher accuracy, a unique filtering algorithm is implemented to filter out unrelated PSI-BLAST hits during profile construction. A schematic of PSIPRED is shown in

+

Taken from [START_REF] Marsh | Structure, dynamics, assembly, and evolution of protein complexes[END_REF].

I.6.3 Protein Blocks: A comprehensive structural alphabet

A structural alphabet (SA) is a library of N structural prototypes (the letters). Each prototype is representative of a backbone local structure of l-residues length. The combination of those structural prototypes is assumed to approximate any given protein structure. One of the most developed and comprehensive SA is the Protein Blocks (PBs) [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF].

PBs are a structural alphabet composed of a set of 16 local prototypes each of 5 residues length, labeled from a to p (see Fig I.14 Bottom). They are described as series of eight Φ, Ψ dihedral angles. An unsupervised classifier similar to Kohonen Maps [START_REF] Kohonen | Self-Organizing Maps[END_REF][START_REF] Kohonen | Self-organized formation of topologically correct feature maps[END_REF] and Hidden Markov Models [START_REF] Rabiner | A tutorial on hidden Markov models and selected applications in speech recognition[END_REF] was used to define PBs. Therefore, they approximate all the local regions of a protein structure with an average RMSD of 0.41 Å [START_REF] Etchebest | A structural alphabet for local protein structures: improved prediction methods[END_REF]. The PBs m and d can be roughly described as + Adapted from [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF] and [START_REF] Etchebest | A structural alphabet for local protein structures: improved prediction methods[END_REF].

PB Assignment: For each "n th " position of the structure, 8 dihedrals ψ (n -2), φ (n -1), ψ

(n -1), φ (n), ψ (n), φ (n + 1), ψ (n + 1), φ (n + 2) are compared to the dihedrals of each of the 16

PBs. The comparison is made by a least squares approach to match the RMSDA criteria (Root mean square Deviation on Angular Values) [START_REF] Schuchhardt | Local structural motifs of protein backbones are classified by selforganizing neural networks[END_REF].

𝑅𝑀𝑆𝐷𝐴 (𝑉 1 , 𝑉 2 ) = √ 1 2(𝑀-1) ∑ 𝑀-1 𝑖=1 [𝜓 𝑖 (𝑉 1 ) -𝜓 𝑖 (𝑉 2 )] 2 + [𝜙 𝑖+1 (𝑉 1 ) -𝜙 𝑖+1 (𝑉 2 )] 2
where, V1 is the 8 dihedrals vector extracted from the 5 residues long window; V2 is the 8 dihedrals vector corresponding to the compared PBs. PB, which gets lowest RMSDA is chosen as the representing conformation observed in the window.

Applications:

PBs have been used to address various problems including, protein superimposition [START_REF] Gelly | iPBA: a tool for protein structure comparison using sequence alignment strategies[END_REF][START_REF] Joseph | Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies[END_REF], general analyses of flexibility [START_REF] Dudev | Discovering structural motifs using a structural alphabet: application to magnesium-binding sites[END_REF][START_REF] Wu | A structural-alphabet-based strategy for finding structural motifs across protein families[END_REF] and prediction of structure and flexibility [START_REF] Zimmermann | LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach[END_REF][START_REF] Rangwala | svmPRAT: SVM-based protein residue annotation toolkit[END_REF][START_REF] Suresh | A protein block based fold recognition method for the annotation of twilight zone sequences[END_REF][START_REF] Joseph | From local structure to a global framework: recognition of protein folds[END_REF] and protein binding sites, and structural analysis of β-bulges [START_REF] Craveur | β-bulges: Extensive structural analyses of β-sheets irregularities[END_REF]. PBs can be assigned to a given structure or an ensemble with valid coordinates using PBxplore [START_REF] Barnoud | PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks[END_REF]. The structural analysis of different structural dataset is assisted by two statistical measures derived from the assigned PBs.

Neq: Quantification of the structural flexibility at a given position n, can be obtained by calculating the average number of PBs across a set of conformers at position n. This is called the "equivalent number" of PBs or Neq. Neq is based on a statistical metric similar to Shannon entropy [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF]. It is calculated as:

𝑁𝑒𝑞 = 𝑒𝑥𝑝 (-∑ 16 𝑖=1 𝑓 𝑥 . 𝑙𝑛(𝑓 𝑥 ))
where fx is the frequency of PB 'x'. The value of x can be any PB from a to p. An Neq value of 1 will indicate that only one type of PB is observed at position n while Neq value of 16

will denote a random and propotional (1:16) distribution.

I.6.4 Tertiary structure prediction

The tertiary structure of the proteins is predicted either using ab-initio methods or based on a template identified through homology. The latter is the more common, reliable, less timeconsuming method and is based on the paradigm that similar sequences have similar structures and hence similar functions [START_REF] Koonin | Beyond complete genomes: from sequence to structure and function[END_REF], [START_REF] Sadowski | The sequence-structure relationship and protein function prediction[END_REF]. Homology modelling starts with identification of a suitable template which shares homologous relationship with the sequence of interest. Using elegant computational algorithms, the coordinates of the backbone of the template are copied to the query and the side chains are optimised. Modeller is the most popular program to perform molecular modelling and is described in brief herein [START_REF] Sali | Comparative protein modelling by satisfaction of spatial restraints[END_REF].

I.6.4.1 Modeller

Modeller is a computer program that models three-dimensional structures of proteins and their assemblies by satisfaction of spatial restraints (Fig I.15). The initial step before starting the modelling procedure is to identify a suitable template. This forms the foundation for rest of the modelling process. The template selection involves searching for a homologous structure in PDB using either BLAST [START_REF] Altschul | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs[END_REF] or any other fold recognition tool such as Phyre2 [START_REF] Kelley | The Phyre2 web portal for protein modeling, prediction and analysis[END_REF] or HHpred [START_REF] Hildebrand | Fast and accurate automatic structure prediction with HHpred[END_REF]. Generally, the structures with sequence identity greater than 30% are considered safely as homologous to the query protein. Once the structure of suitable confidence is identified as a template, an alignment is performed between the query and the template. This can be achieved either using scripts from Modeller or using a suitable alignment tool. This alignment, in PIR format, is the input to the Modeller program. From its alignment with template 3D structures, Cα-Cα distances, hydrogen bonds and dihedral angle restraints for the target sequence are calculated by

Modeller. The form of these restraints has been obtained from a systematic statistical analysis of the relationships between many pairs of homologous structures [START_REF] Sali | Derivation of rules for comparative protein modeling from a database of protein structure alignments[END_REF]. The spatial restraints are obtained empirically, from a database of protein structure alignments. These restraints are expressed as probability density functions (pdfs) for the features to be restrained. For example, the probabilities for main-chain conformation of an equivalent residue in a related protein are expressed as a function of the local similarity between the two sequences. A smoothening procedure has been employed in the derivation of these relationships to minimise the problem of sparse database. Next, these spatial restraints and Charmm energy terms enforcing proper stereochemistry are combined into an objective function [START_REF] Webb | Comparative Protein Structure Modeling Using MODELLER[END_REF]. The output is a 3D model for the target sequence containing all main-chain and side-chain non-hydrogen atoms which ensures a minimal deviation from the input restraints. The final model is then optimised using variable target function methods employing methods of conjugate gradients and molecular dynamics with simulated annealing. Several slightly different models can be calculated by varying the initial structure. The variability among these models can be used to estimate the errors in the corresponding regions of the fold. Also, the loops are further refined using different protocols for loop modelling. Side chains are further optimised using the rotamer libraries, which are favoured side chain torsion angles extracted from known protein crystal structures. The accuracy of comparative models depends on the extent of the sequence identity between the query and the template [START_REF] Swapna | Comparison of tertiary structures of proteins in protein-protein complexes with unbound forms suggests prevalence of allostery in signalling proteins[END_REF]. Usually, errors are expected to be more in the structurally variable region than in the structurally conserved region. The CASP (Critical Assessment of protein structure prediction) assessments happen every two years to test the ability of different structure prediction methods to accurately model the query proteins. It has generally been seen that many of the models show higher RMSD to the true native structure than the one selected by a structural alignment to be the best available template.

I.6.6 Dynamic nature of protein structures

Proteins may exist in multiple conformations and are always in motion under cellular environment. The paradigm of sequence-structure-function also includes dynamics before function these days. Thus redefining the paradigm as; proteins with similar sequences share similar structures which in turn share similar dynamics and hence give rise to similar functions. While experimental techniques such as NMR and cryoEM help understanding the underlying dynamics behind these proteins, computational methods also provide insights into the same. Due to their high efficiency, molecular dynamics simulation and normal mode analysis are the methods of choice in majority of the cases to understand the dynamics associated with a protein in a simulated cellular environment.

I.6.7 Molecular dynamics

Molecular dynamics (MD) is a computer simulation method for studying the physical movements of atoms and molecules. In molecular dynamics, successive configurations of the system are generated by integrating Newton's laws of motion. The result is a trajectory that specifies how the positions and velocities of the particles in the system vary as a function of time.

The trajectory is obtained by solving the differential equations in the form of Newton's second law (F = ma):

𝛿 2 𝑥 𝑖 𝛿𝑡 2 = 𝐹 𝑥 𝑚 𝑖
This equation describes the motion of a particle of mass m; along one coordinate (x,) with

Fx. being the force on the particle in that direction. MD simulation is based on an assumption that system follows ergodicity which means that all accessible microstates are equally probable over a long period of time.

Briefly, molecular dynamics simulation begins by defining the initial coordinates for the system of interest. A small time-step Δt is chosen such that the next coordinates can be evolved.

Next atom positions are predicted and the velocities are updated. The forces are calculated for the new set of positions and the positions are further adjusted. Periodic boundary conditions are employed and then the next iteration follows. This procedure is repeated until the given time. Few concepts important for understanding the MD theory are described below.

Force fields:

In order to calculate the potential energy of the system, mathematical functional forms and parameters have been defined, called force fields. Potential energy is further used to calculate the forces on the atoms. The force field is a collection of equations and associated constants designed to reproduce molecular geometry and selected properties of tested structures.

The parameters for energy functions have been derived from physical or chemical experiments or from quantum mechanical calculations. The equation for the calculation of potential energy in molecular mechanics include interaction terms from bonded and non-bonded interactions. The specific parameters for the interactions vary between force fields, but a general expression for total potential energy can be written as:

𝐸 𝑡𝑜𝑡𝑎𝑙 = 𝐸 𝑏𝑜𝑛𝑑𝑒𝑑 + 𝐸 𝑛𝑜𝑛-𝑏𝑜𝑛𝑑𝑒𝑑
Where 𝐸 𝑏𝑜𝑛𝑑𝑒𝑑 = 𝐸 𝑏𝑜𝑛𝑑 + 𝐸 𝑎𝑛𝑔𝑙𝑒 + 𝐸 𝑑𝑖ℎ𝑒𝑑𝑟𝑎𝑙 and 𝐸 𝑛𝑜𝑛-𝑏𝑜𝑛𝑑𝑒𝑑 = 𝐸 𝑒𝑙𝑒𝑐𝑡𝑟𝑜𝑠𝑡𝑎𝑡𝑖𝑐 + 𝐸 𝑣𝑎𝑛 𝑑𝑒𝑟 𝑤𝑎𝑎𝑙𝑠

The bond and angle terms are modelled as quadratic energy functions and non-bonded terms are modelled as Lennard-Jones and Coulombs potential. The detailed energy function is calculated as:

𝑈(𝑅) = ∑ 𝑘 𝑖 𝑏𝑜𝑛𝑑 (𝑟 𝑖 -𝑟 0 𝑏𝑜𝑛𝑑𝑠 ) 2 + ∑ 𝑘 𝑖 𝑎𝑛𝑔𝑙𝑒 (𝜃 𝑖 -𝜃 0 𝑎𝑛𝑔𝑙𝑒𝑠 )

Non-bonded

Different force-fields are designed for different purposes. e.g. AMBER [START_REF] Case | The Amber biomolecular simulation programs[END_REF] force-field is used majorly for simulating DNA and proteins. CHARMM [START_REF] Brooks | CHARMM: A program for macromolecular energy, minimization, and dynamics calculations[END_REF] can be used for both small molecules and macromolecules. GROMOS is a general purpose force-field for the study of biomolecules [START_REF] Christen | The GROMOS software for biomolecular simulation: GROMOS05[END_REF].

Energy minimisation: Classical MD simulations try to explore all possible conformations of a protein in a given energy well assuming that the protein structure indeed lies at the energy minima. Crystal structures may not always be trapped in their minimum energy conformation while crystallisation. Hence, before proceeding with the simulations, it is mandatory to find minima for the protein structure. Mathematically, minima occur when the when the first derivative of potential is zero and when the second derivative is positive. There are two commonly use methods to perform energy minimization, steepest descent [START_REF] Cristianini | Gradient Descent (Steepest Descent Method)[END_REF] and conjugate gradient [START_REF] Steihaug | Conjugate Direction Methods in Optimization[END_REF].

Steepest descent is the simplest method to use for performing energy minimization. It follows the fastest decrease of the potential "U" opposite of the gradient. It is the fastest method from a poor starting geometry but can converge very slowly near energy minima. This is due to the fact that it can oscillate back and forth across a minimum. Conjugate gradient on the other hand, adds history to the steepest descent method to gather second derivative information and guides the search where the derivative determines the pathway.

Solvation: Since the biomolecules have to be simulated in a cellular environment, the protein has to be solvated. Two types of solvation methods are available: Implicit solvation [START_REF] Cramer | Implicit Solvation Models: Equilibria, Structure, Spectra, and Dynamics[END_REF] and explicit solvation [START_REF] Levy | COMPUTER SIMULATIONS WITH EXPLICIT SOLVENT: Recent Progress in the Thermodynamic Decomposition of Free Energies and in Modeling Electrostatic Effects[END_REF]. In implicit solvation model, the solvent is defined as a continuum of homogenous polarizable medium which possess properties equivalent to the solvent. While in an explicit solvent model, the coordinates for the solvent molecules are explicitly defined. This solvation method is more realistic and can give a true picture of interaction between the solute and solvent. The protein molecule is solvated in a box before performing an energy minimisation step and then the main MD run is performed.

Periodic boundary conditions (PBC):

In majority of the simulations, the simulation box should be large enough to circumvent the boundary artefacts. Such scenario can be avoided by employing the periodic boundary conditions, where one side of the simulation comes back from the opposite side, mimicking a bulk phase [START_REF] Makov | Periodic boundary conditions inab initiocalculations[END_REF]. For PBCs, particles are enclosed in a box, and the box is replicated to infinity by rigid translation in all the three Cartesian directions, completely filling the space. The basic idea behind the PBC is that if an atom moves in the original simulation box, all its images move in a concerted manner by the same amount and in the same fashion. When PBCs are applied, there is a chance that the number of interacting pairs increase enormously. The reason is that there is an interaction not only interacts with other particles in the simulation box, but also with their images. Such a problem is avoided by choosing a finite range potential within the criteria of minimum image convention [START_REF] Makov | Periodic boundary conditions inab initiocalculations[END_REF]. The essence of the minimum image criteria is that it allows only the nearest neighbors of particle images to interact.

MD Ensembles:

In molecular mechanics, the ensembles are the statistical entities that are used to represent the possible states of a system. Different ensembles utilised in MD simulation are canonical ensemble, isothermal-isobaric ensemble and microcanonical ensemble. Canonical ensemble conserves the number of molecules (N), volume (V) and temperature (T) of the system, hence also called as NVT ensemble. The temperature is maintained through the association of a thermostat. In isothermal-isobaric ensemble, the number of molecules (N), pressure (P) and temperature (T) of a system is conserved, hence popularly known as NPT ensemble. In microcanonical ensemble, the system's energy (E) is conserved along with number of molecules (N) and volume (V), hence called NVE ensemble.

I.6.8 Normal mode analysis (NMA)

Classical molecular dynamics simulations generally provide information on the dynamics happening at the μs time-scales which mostly includes the side chain motions or at most some loop motions if the energy barrier between the different states are within a difference of few KBT.

Bigger conformational changes such as domain motions can be accessed using advance sampling techniques in MD, which requires more computational power and an expert level understanding of the field and parameters. In such a scenario, a simpler, yet powerful, network-based technique, normal mode analysis (NMA) can be used. NMA is purely a geometry-based approach where a protein is modelled as a network of mass and springs. Generally, the Cα atoms of a protein are defined as nodes and a spring is defined for the edges connecting these Cα atoms within a certain cut-off distance. The movement of each node is expressed in terms of squared fluctuations i.e. displacement of nodes from their mean positions. Collective motion of many such nodes in a certain direction defines the global motions which are biologically relevant and correspond to the domain motions [START_REF] Bahar | Normal Mode Analysis of Biomolecular Structures: Functional Mechanisms of Membrane Proteins[END_REF]. Such a simplified approach to study protein dynamics has been shown to successfully reproduce biologically relevant motions [START_REF] Hinsen | Analysis of domain motions by approximate normal mode calculations[END_REF].

Two types of NMA can be implemented; Gaussian network model (GNM) or Anisotropic network model (ANM). In GNM, the squared fluctuations are assumed to be isotropic while in ANM the fluctuations are anisotropic. An adjacency matrix is diagonalized in GNM while in ANM a hessian matrix is diagonalized to calculate the Eigen vectors and Eigen values. These values correspond to the direction of motion in ANM. Hessian matrix consists of the double derivative of the hooks potential defined for the system.

I.7 Biomolecular interactions

Proteins seldom work in isolation. Multiple interactions within a cell viz. protein-protein, proteinligand and protein-DNA are key to proper functioning of a cell. Besides the experimental methods to study biomolecular interactions, various computational methods are also available. Two biomolecular entities can be computationally docked to study their binding modes. Protein can be docked with another protein using HADDOCK [START_REF] De Vries | The HADDOCK web server for data-driven biomolecular docking[END_REF].

HADDOCK (High Ambiguity Driven biomolecular DOCKing) is an information-driven flexible docking approach for the modelling of biomolecular complexes [START_REF] Dominguez | HADDOCK: A Protein-Protein Docking Approach Based on Biochemical or Biophysical Information[END_REF]. Docking is defined as the modelling of the structure of a complex based on the known three-dimensional structures of its constituents. HADDOCK incorporates a wide variety of experimental and/or bioinformatics data to drive the modelling. This allows focusing the search to relevant portions of the interaction space using a more sophisticated treatment of conformational flexibility.

AutoDock [START_REF] Morris | AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility[END_REF] is used to predict the binding mode of a protein with a ligand. It is a freely available, open-source software which practically is a suit for automatic docking tools. AutoDock has been widely-used and there are many examples of its successful application in the literature [START_REF] Minke | The role of waters in docking strategies with incremental flexibility for carbohydrate derivatives: heat-labile enterotoxin, a multivalent test case[END_REF][START_REF] Sotriffer | Automated docking of ligands to antibodies: methods and applications[END_REF]. It is very fast, provides high quality predictions of ligand conformations, and good correlations between predicted inhibition constants and experimental ones. AutoDock has also been shown to be useful in blind docking, where the location of the binding site is not known.

I.8 Molecular phylogenetics

Genetic changes due to mutations or recombination gets accumulated in each generation of an organism or population. In subsequent generations, these accumulations may exhibit phenotypic changes in the organism thus leading to its evolution. The rate of such genetic changes is fundamental to understand the evolution of a given species or taxonomic group. In molecular biology, the rate of change of particular biomolecules like, DNA (nucleotides), RNA (gene) or amino acids (protein) is of interest. The rate of accumulation of changes in these biomolecules studied along with the evolution of a species in tree of life is called Molecular phylogenetics. Due to the degeneracy of the genetic code, substitutions in the DNA or RNA may not affect the amino acid sequence and thus the protein function will be unaltered. Such synonymous substitutions are called silent mutations at amino acid level. However, silent mutations can accumulate over generations and put selective pressure on a specific codon for an amino acid, altering the extent of protein expression. Besides, there are missense (non-synonymous) and nonsense mutations (stop codon) that can lead to change in expression and loss of expression or truncated expression, respectively. Due to failure of DNA repair machinery mostly due to external factors there can also be an insertion or deletion of a nucleotide base that can lead to frameshift mutation causing adverse effects in codon reading by ribosome. I.8.2 Substitution rates: Depending on the type of molecule, the rate of selective substitutions differ. For instance, in DNA and RNA, the minimum possibility of a mutation is 1 in 4, i.e 25% while it goes down to 5% (1 in 20) in case of proteins. Therefore, separate substitution models, sensitive to the type of biomolecules is required while studying their evolution. With the advancement of molecular phylogenetics, there are distinguished substitution models available for varied kinds of molecular analysis. More details on practical use of different matrices is provided in chapter 6, section 6.2.4. I.8.3 SNP or mutation: When considered in regards to an individual organism, the nucleotide or amino acid substitution is termed as a point mutation. However, when a population or species is considered, environment factors and genetic events like genetic drift, geographic displacement can lead to more than one kind of substitution at the same position. Therefore, in context of a population, it is termed as a single nucleotide polymorphism (SNP) or amino acid variant [START_REF] Rimoin | Emery and Rimoin's Principles and Practice of Medical Genetics[END_REF]. An example is shown in Figure I.17 I.8.4 Phylogenetic tree generation: A phylogenetic tree is generated by using the selective substitution rates on a biomolecule and the variations induced by those substitutions in the sequence of the biomolecule in different species. Therefore, quintessential for generating a phylogram is the sequences of all possible homologs (based on local identity) of a given protein/DNA or RNA. All the sequences are then globally aligned to identify the conservation sites as well as highly mutated sites. The multiple sequence alignment (MSA) can be generated by two prominent methods:

Progressive or hierarchical method [START_REF] Katoh | MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform[END_REF]:

A crude MSA is generated by first aligning the most similar sequences and subsequently adding less related sequences or groups to the alignment. The inclusion of new sequences is carried out until the entire query set has been incorporated into the solution. The initial tree describing the sequence relatedness is based on pairwise comparisons that may include heuristic pairwise alignment methods. Thus, the alignment results are dependent on the choice of "most related" sequences and therefore can be sensitive to inaccuracies in the initial pairwise alignments. Iterative method [START_REF] Katoh | MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform[END_REF][START_REF] Khan | MEGA -Core of Phylogenetic Analysis in Molecular Evolutionary Genetics[END_REF]:

It optimizes an objective function based on a selected alignment scoring method by assigning an initial global alignment and then realigning sequence subsets according to the scoring method. The realigned subsets are then themselves aligned to produce the next iteration of MSA. The iterations are continued each of the query sequence is aligned at least twice. Therefore, an improvement over iterative method removes the bias of an initial alignment. However, since it usually takes multiple iterations to achieve a final MSA, the method is computationally exhaustive.

I.8.5 Types of phylogenetic trees:

The goal of a molecular phylogenetics is to construct a tree topology that best explains the evolutionary history of the given sequences. There are four approaches to analyze the generated MSA and define a tree topology. The most primitive and basic ones are: distance based methods and parsimony based methods. Distance based methods uses the substitution models to estimate pairwise evolutionary distances among each sequence of MSA.

The distance matrix is then analyzed by hierarchical clustering type methods such as neighborjoining (single linkage clustering) or unweighted pair-group with arithmetic mean (average clustering) [START_REF] Khan | MEGA -Core of Phylogenetic Analysis in Molecular Evolutionary Genetics[END_REF]. In the parsimony approach, the goal is to identify a topology that requires the fewest necessary changes to explain the differences among the observed sequences. Both of these methods works better in very closely related sequences but often fail to work with datasets comprising of distant homologs. In case of highly diversified homologs, the two character based methods are highly useful as they employ probabilities and thus ignore initial bias.

A) Maximum likelihood based [START_REF] Felsenstein | PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP[END_REF]:

An initial tree is first built using a fast but suboptimal method such as Neighbor-Joining. A likelihood function is calculated based on the substitution model. The branch lengths of the NJ tree are then adjusted to maximize the likelihood of the data set under the given substitution model. Then variants of the topology are created using the NNI (nearest neighbor Interchange) method to search for topologies that fit the data better. Maximum-Likelihood branch lengths are computed for these variant tree topologies and the greatest likelihood is retained as the best choice. The search continues until no greater likelihoods are found [START_REF] Yang | Molecular phylogenetics: principles and practice[END_REF]. B) Bayesian inference based [START_REF] Dinh | Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo[END_REF]: BI based methods can be seen as an extension of the ML based methods with a major difference being the use of a prior probability. BI based methods use the initial MSA and the substitution matrix to generate a priori probability that the given topology should belong to the base tree architecture. Therefore, instead of testing the likelihood as in ML, BI used the prior probability to make the decision [START_REF] Yang | Molecular phylogenetics: principles and practice[END_REF]. I.8.6 Reliability of the tree topology: After a tree topology have been generated, statistical measure like bootstrapping, jackknifing are used to test its robustness [START_REF] Felsenstein | PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP[END_REF]. Bootstrapping works very similar to e-value calculation. Random sites in the alignment is replaced and is used to build new trees. It is possible that some positions will be repeated in the subsample, while some positions will be left out. Such multiple resamples are run for mostly 100 to 1000 times based on the size of the dataset. A bootstrap value closer to 100 gives higher confidence in the branching.

An introduction to these concepts is important to understand the subsequent chapters in the thesis.

All of these concepts would be used at different places in different chapters. For simplification, the thesis is organized into three major sections; A) Chapters 1 to 3 that focuses on secondary structures rather than whole proteins. B) Chapter 4 deals with domain level analysis of a structural assembly while Chapter 5 studies a complete protein structure of a membrane protein. Proteins involved in both the chapters are crucial due to the pathologies they are involved in. C) Chapter 6 is though related to chapter 5 but it does not study the structural behaviors. Rather it is focused on sequence analysis and phylogenetics which adds a fresh perspective towards the end of the thesis.

Each chapter deals with a systematic study of an individual idea. There are some supplementary (marked as 'S') and sub-chapters (marked as 'a', 'b', 'c' with the chapter number).

The supplementary chapter provides information related to the parent chapter while sub-chapters are individual studies dealing with the question in parts. Each chapter including sub-chapters and supplementary chapters contains information regarding, introduction to the topic, methods used, results and discussion and conclusiong and future perspectives. In some chapters, an additional section for acknowledgment is included to thank the team members and collaborators for their support. At last there is a conclusive outline of the thesis that is written from the perspective of personal learning experience during the PhD and the impact of different projects on that learning curve. There are two chapters that should have been included in this thesis document but cannot be included due to restraints of space and time. Each of these two chapters are associated with our collaborators in India and Canada. My responsibility towards the collaboration with Karboune lab, Before the first protein 3D structure was solved at atomic resolution [START_REF] Kendrew | A three-dimensional model of the myoglobin molecule obtained by X-ray analysis[END_REF], Pauling and Corey provided evidence that polypeptide chains can adopt a limited number of repetitive local protein structures stabilized by intramolecular hydrogen bonds [START_REF] Eisenberg | The discovery of the alpha-helix and beta-sheet, the principal structural features of proteins[END_REF][START_REF] Pauling | Views of helical peptides: a proposal for the position of 3(10)-helix along the thermodynamic folding pathway[END_REF][100]. The two major local folds are:

(i) the α-helix (or 3.613 helix) with hydrogen bond between amino acid residues i and i + 4, and (ii) the β-sheet composed of extended strands with hydrogen bonds between adjacent strands, running parallel or antiparallel. They roughly represent 1/3 rd and 1/5 th of the residues found in proteins, respectively. Therefore, protein structures are often represented as seen in crystals as (i) rigid macromolecules (ii) comprising of repetitive units of helices, sheets and coils. However, both the definitions are partial because in physiology proteins are highly dynamic macromolecules and the description of protein structures could be more precise.

Of the current popular secondary structures, helices and β-sheets are the two predominant conformational forms. The hydrogen bonding pattern of the two differs considerably and therefore they can be treated as two separate independent conformations with respect to protein folding. For instance, different types of helices like 310-and π-helices have been proposed as intermediate conformations in the folding of an α-helix [101][102][103]. Similarly, β -turns, bends, and strands are commonly observed during the formation of β-amyloid aggregates. Moreover, our team have previously worked with 'chameleon sequences' that are short stretch of structured regions that can interchange between helix and strand conformations [104]. Therefore, for simplicity of the analysis, the dynamics of these secondary structures will be studied individually. The first one being different types of helices.

α-helices

Since the characterization of helices in 1951 [100], extensive explorations have been conducted to better understand their formation and their role in the kinetics of protein folding. Although a general view of the folding kinetics is too complex to define theoretical folding models for helices.

Recent cutting-edge experiments have underlined their significant contribution through different examples [105]. Furthermore, with accumulating high-resolution experimental 3D structure, many studies have been carried out to decipher the sequence features that preferentially drives folding towards a given local fold.

In this context, α-helices have been intensely analyzed [106][107][108]. It has been emphasized that the length of an α-helix depends on its amino acid composition [109,110] and that its extremities (or caps) have specific signatures [111][112][113]. These caps can be stabilized by hydrophobic interactions between helical residues and residues outside the repetitive structures [114][115][116]. The importance of such interactions was highlighted for instance in the case of class α-glutathione transferases where, using computational approaches, it was shown that the highly conserved helix 9 modulates their catalytic and binding function and that a mutation of N-cap residue Asp-209 destabilizes the enzyme's function [117,118].

For structural description of α-helices, please see section I.2.2 1.1.2 310-helices 310-helices (shown in Fig 1.1) are less frequent than α-helices and represent about 4% of the residues in proteins. The 310-helix is characterized by intramolecular hydrogen bonds between residues i and i+3, and is usually short, containing three or four residues per turn [119,120].

Nonetheless, two-turn and longer 310-helices have also been reported [120]. In terms of location, they are preferentially observed at the termini of α-helices and are considered as connectors between two α-helices [START_REF] Baker | Hydrogen bonding in globular proteins[END_REF]121,122]. However, the 310-helix is also often found in the regions connecting strands within β-hairpin or β-β-corner motifs [123]. In terms of sequence, their amino acid content is different from the α-helix [124].

A specific analysis of a 310-helix adjoining the α-helix and β-strand has shown that the composition of 310-helices in vicinity of β-strands is much more conserved among family members of homologous structures than those 310-helices adjacent to two helices [123]. The preferred length of the 310-helix occurring between an α-helix and β-strand is equal to 3 residues, but extends to 4 residues when located between two α-helices (α-310-α) [125]. which are less frequent than both of αand 310-helices. They represent about 0.02% of the residues in proteins. In the π-helix (or 4.416-helices), hydrogen bonds are formed between amino acid residues i and i+5. This helix conformation is less stable due to steric constraints, which could also explain why π-helices are rare [START_REF] Low | THE π HELIX-A HYDROGEN BONDED CONFIGURATION OF THE POLYPEPTIDE CHAIN[END_REF]. In 2000, Weaver found only 14 well-defined π-helices in the available PDB files (i.e. about 13500 structures) [126]. However, the π-helix should occur more frequently in protein structures than has been previously described, and should be conserved within functionally related proteins [START_REF] Fodje | Occurrence, conformational features and amino acid propensities for the π-helix[END_REF]127]. π-helix show distinct residue preferences that differ from those of α-helices [127]. Interestingly, it was shown that on a limited number of π-helices they were directly linked to the formation or stabilization of a specific binding site [126]. Thus conformationally, π-helices can be of crucial importance in protein-protein or protein-ligand interactions. Two views, lateral and dorsal (bottom) are provided for each helix to appreciate the differences in their helical rise, pitch and the helical core.

Prediction of helices from amino acid sequences

The individual studies on different types of helices point out significant differences in the amino acid composition of various helical motifs, which can be exploited for their prediction. For instance, the secondary structure prediction method SSPRO8 performs reasonably well for 8 different states with a prediction accuracy of ~62 -63% [127,128]. However, although it aims at separate predictions of α-, 310-and π-helices; the 310-helix prediction rate is very low and the πhelix is rarely predicted. The latest approach with RaptorX Property performs slightly better for 310-helix, but remains unsuccessful for the π-helix [127][128][129].

The rare occurrences of these motifs largely explain the low rates of predictions. It may also arise from the difficulty to assign 310-and π-helices. Although the hydrogen bonding pattern and other structural parameters are well characterized for 310-and π-helices yet assignment methods fail to assign. Their failure might be due to the enhanced flexibility profile of these helical structures [130]. 1.1.5 Dynamic relationship between helices: What is known! Indeed, a dynamic relationship would exist between the different kinds of helices, for instance between α-and π-helices as shown in [131]. Importantly, 310-helices and to a lesser extent πhelices, have been proposed to be intermediates in the folding / unfolding of α-helices [101][102][103].

However, such studies are often based on model systems like polyalanine peptides and use molecular dynamics (MD) simulations to inspect the effect of chain-lengths and N-terminus residues in α-helix folding [132]. Unfortunately, flexibility profiles and putative interconversion between helical states have never been conducted for a large set of protein structures.

Therefore, it was decided to conduct the first large scale MD simulation study from a large number of structural folds. The underlying motivation being to catalogue the flexibility profile of helices and depict how the helical regions evolve (Fig 1.1). Thus the study provides new insights into the flexibility and deformability of the different helical states, which are an essential component of the structure and function of biological macromolecules. The MD simulations analyse and quantify the stability of helices by considering α-helices as well as 310-and π-helices.

Methods

Dataset preparation

A non redundant dataset at 40% sequence identity was extracted using ASTRAL compendium 2.03 [133-135]. It consists of 5580 protein chains resulting from 4432 PDB files. By filtering on chain lengths between a range of 50-250 residues, resolution better than 1.5 Å and excluding chains with any discontinuity in position numbering, missing residues, modified and/or incomplete residues; only 169 domains were selected. Only globular proteins were used. An in-house parser was used to filter out and fetch the information as implemented in earlier publications from the lab [136]. The selected 169 SCOPids are provided in the Table 1.1. The 169 domains represent an equilibrated repartition among the different SCOP classes: all-α represent 18.9% of the chains, all-β 29.6%, α/β 24.8% and 26.7% represent α+β class. These SCOP domains belong to 155 X-ray structures in PDB.

Table 1.1 SCOP ids of the final selected 169 domains. The first four columns contain 35 SCOP ids each while the last one contains 29 entries. The 'd' (first character) signifies that the given structure is a domain. While the following four characters denote the PDB ids of which the domain is a part of. The 6 th character in the string is the chain ID of the PDB file. The last or the 7 th character if present, signifies the alternate structure form of the domain or isoforms.

Protocol for MD simulations

Three independent MD simulations of 50 ns each were performed for all protein structures with GROMACS 4.5.7 software [137], using AMBER99sb force field [138]. Thus generating a collective simulation time of 150 (3*50) ns. Each protein structure was immersed in a periodic dodecahedron box using TIP3P water molecules and neutralized with Na + or Cl -counter-ions. The system was then energetically minimized with a steepest-descent algorithm for 2000 steps. The MD simulations were performed in isothermal-isobaric thermodynamics ensemble (NPT) with temperature fixed at 300 K and pressure at 1 bar. A short run of 1 ns was performed to equilibrate the system using the Berendsen algorithm for temperature and pressure control [139]. The coupling time constants were equal to 0.1 ps for each physical parameter. A production step of 50 ns was done using the for temperature and pressure control, with coupling constants of T= 0.1 ps and P= 4 ps. All bond lengths were constrained with the LINCS algorithm [141], which allowed an integration step of 2 fs. The PME algorithm [142] was applied for long-range electrostatic interactions using a cut-off of 1 nm for nonbonded interactions. This protocol was applied to each of the 169 protein domains. From each MD simulation, the conservation of the secondary structures was observed and the structural deviation of each snapshot from the initial structure was measured. Conformations were saved after every ps. For each MD simulation, the secondary structures were analyzed and the structural deviation of each snapshot from the initial structure was measured. Trajectory analyses were performed with the GROMACS v4.6.5, in-house Python and R scripts. Root mean square deviations (RMSD) and root mean square fluctuations (RMSF) were computed on Cα atoms. Normalized RMSFs and normalized B-factors were computed as in Bornot et al, 2011 [143].

Analysis of the local protein conformation

Secondary structure assignment was performed using DSSP version 2.2.1 with default parameters. DSSP assigns secondary structures as a 7 state model based on intra-hydrogen bonding pattern.

The 7 states are represented as 'H'-α-helix, 'G'-310-helix, 'I'-π-helix, 'S'-bend, 'T'hydrogen bonded turn, 'B'-β-bridge, and 'E'-extended β-strand. In contrast to the previous version of DSSP (cmbi version, 2000) or as termed by the authors as DSSPold, the irregular or coil or loops are marked with a blank in the output. Thus reducing 8-state assignment by DSSPold to 7-state assignment. Since loops or coils are highly flexible structures the blank spaces in the DSSP output were replaced by 'C'.

It should be noted that the Gromacs v4.5.7 that was used for the molecular dynamics, support the DSSPold developed by Kabsch and Sander in 1983. Since, 25 years, a prominent error in the judgement of hydrogen bonding pattern lead to the misassignment of π-helices as H or T [144]. Therefore, the new version of DSSP (v2.2.1) was used on each frame from the MD trajectory to avoid any errors that may have been induced due to the use of DSSPold.

Protein Blocks (PBs) were also assigned to the same number of frames and Neq was used to analyse the behavior of PBs throughout the simulations. Detailed discussion of the methodology and results from PB analysis will be discussed in Chapter 2.

Clustering approach

In the initial state, i.e the input structure, each residue is associated to one of the 8 defined secondary states assigned by DSSP [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF]. Post simulation, the states for each residue is again assigned using DSSP. Hence, each residue is associated to a vector of size, S=8 representing the 8 defined secondary states and more specifically, the occurrence of each observed state. To define common behaviors between residues, a k-means clustering approach was used [145].

At first a subset is created that represents all the residues that were associated to a particular state before MD, e.g. a subset of 310-helices. Then a fixed number of clusters 'k' is determined, with k=5 (selected after few tests). As per the DSSP states, the k-clusters are of size S=8. All the data of the subset is then compared to each k-cluster and the one with the minimal Euclidean distance is considered the winner. After one cycle (and after all subsets had been used), the values of the k clusters are modified in order to correspond with the associated observations. The modifications done to a cluster is such that each cluster is the barycenter of the associated observations. After a few cycles, the k clusters are stable and can be analyzed for behaviors of different helices.

Results and discussions

Analyses of protein structures

The DSSP assigned 8 states for each frame of all the domains under simulation. In the dataset, the distribution of the helices assigned using DSSP is as follows: 31.5% are assigned as α-helix while 3.99% as 310-helix and 0.28% as π-helices. This distribution is similar to the distribution observed by Tyagi et al.in 2009 [146]. As shown in Table 1.2, the lowest B-factors are associated to αhelices, an expected feature since α-helices are found most prominently in the ordered state [START_REF] Craveur | Protein flexibility in the light of structural alphabets[END_REF].

Interestingly, π-helices are observed in the dataset to be less flexible than 310-helices with average normalized B-factor values of 0.09 and 0.24, respectively. Both correspond to the flexible region as defined in [START_REF] Craveur | Protein flexibility in the light of structural alphabets[END_REF]147,148]. This tendency is correlated with the relative accessibility of the residues computed by DSSP, a higher accessibility being observed for 310-helices than for πhelices (Table 1.2 and Figure 1.2, row 2). Table 1.2 Behaviors of helices. Average normalized B-factors (from X-ray structures), average normalized RMSF (from the MDs) and the average relative accessibility surface area (for X-ray structures) of α-, 3 10 -and π-helices are presented.

Analyses of molecular dynamics

The distribution of normalized B-factors (Fig 1 .3A) and normalized RMSF (Fig 1 .3B) is highly similar to the distribution observed in a previous studies from our lab, performed with a smaller dataset [143,149]. Figure 1.3C shows the correlation between normalized B-factor values and normalized RMSF (Pearson's coefficient r = 0.43). The correct correlation is also very close to the one previously observed by Bornot et al, 2011 [143].

Interestingly, 60.2% of the positions do not change at all. Thus no local deformability is observed as is also reflected with an Neq value of 1.0. Furthermore, the behavior observed with Bfactors is confirmed with RMSF analysis. The most rigid helical structures are α-helices while π-helices appears more flexible but 310-helices are observed to be the most flexible ones (refer to Table 1.2). Row1), normalized RMSf (Row2), and relative Solvent Accessibility (row 3) for α-helix (col1), 310-helix (col2) and π-helices (col3). The values in Table 1.2 are calculated from these plots.

Helical persistence during simulation

Based on the frequency of the initial DSSP state during the dynamics, perseverance of a state can be estimated. Estimation of perseverance can answer questions such as, how many times an initially assigned 'H' persisted as an α-helix during the simulation of 150 ns and how many times does it changes its conformation? However, it does not provide any details about the changed state.

α-helix

DSSP α-helix state represents nearly 30% of the residues in the complete dataset of 169 domains. Of those residues (Fig 1 .4A), 31.5% always remain as α-helical during the entire simulation. However, 91.4% maintains an α-helical state for more than 50% of the simulation time, while only 3.9% remain as an α-helix for less than 25% of the time. These statistics illustrate the very stable behavior of the α-helix. 1.3.3.2 310-helix Despite its relative importance, the 310-helix is observed to be a less stable local structure during simulations in comparison to the α-helix. As can be seen in Figure 1.4B, the tendency of 310-helix residues to remain in the 310-configuration is very limited. Indeed, no residue was found to retain the 310-helix conformation for the collective simulation time of 150 ns (Table 1.3). The residues adopting a 310-helix conformation in the initial structure are 3.9% of the total residues in the dataset. Therefore, the representation of 310-helix is just 13% of the α-helical representation.

Among the residues initially observed in the 310-helix state, only 15.7% retained initial state for more than 90% of the simulation time. However, 54.1% of the residues were observed more than half of the time as 310-helix. 1.3.3.3 π-helix π-helix was observed to be an extremely rare state in the initial input structures. It was observed 14 times less than the 310-helices, i.e 0.02% of the total dataset. They are depicted to be slightly less accessible and with regards to their average B-factor and RMSF values, they are supposedly more stable. However, Figure 1.4C shows that this is not the case. Indeed, only 2.4% of the residues remained as a π-helix more than half of the simulation time and the rest is not stable. More than 97.6% were observed as a π-helix for less than 1/4 th of the time.

However, rare nature of π-helix is a serious concern given that the selected 169 structures spanned all the SCOP classes. This observation formed the basis of re-assignment of the structures and MD trajectories using DSSP v2.2.1 as the DSSPold underestimates π-helix. 1.3.4 Impact of secondary structure reassignment on the persistence status DSSP reassignment had no observable change in 310-helices (still 3.5%) representation and perseverance frequencies. However, π-helix assignment has a significant 15-fold increase in the initial structures, increasing from 0.02% to 0.32% of the total residues. The increase in π-helix assignment is derived from 2/3 rd of the previously assigned α-helices and 1/3 rd of turns. As α-helix state has a dominant representation of 30%, the decrease in their representation is non-significant.

Thus the DSSP reassignment provides a different view of π-helices. The π-helices are found to be relatively more stable as expected from their B-factor and RMSF analysis.

The updated persistence rates for π-helices are also updated in Table 1.3 as the last row (π-helixDSSPv2.2.1). along with their older values for comparison. It is observed that 39.6% of initially assigned π-helices remained as π-helices for more than 50% of the simulation time. Similar to the previous assignments, none of the π-helices remained as π-helices for 100% of the time, however, 15% remained for more than 90% of the time.

Table 1.3 Initial state perseverance of each helix.

The different columns identify the %age of simulation time, each helix remained in its initial conformation. For e.g. 29.1% of α-helices remain as α-helix during the whole simulation and 91.4% of initial α-helices remains as α-helix for more than 50% of the simulation time. The last column, < 25% defines the persistence of an initial confirmation for less than 1/4 th of the simulation time. Therefore, a large value in this column clearly signifies high flexibility or deformability. Please note that the data was reassigned using new version of DSSP and major changes were observed only in π-helices, as depicted by rows 3 and 4 in the table.

Conformational exchanges during simulations

So far the frequency to retain the initial DSSP state is analysed. It is observed that the initial state is not preserved during the entire simulation, except for α-helix (H). Nonetheless the three helices change their initial state for more than 50% of the simulation time. Therefore, another important question arises: which conformation do they transform into? Do they preferentially explore the conformational space of other helical conformations or the non-helical ones? Table 1.4 depicts the exchange rates between helical as well as non-helical states. For example, ~10% of times an α-helix adopts a non-helical state. Among the helical states it remains an α-helix for 88.3% of times thus clearly establishing α-helix as the favored conformation. While, an αto 310-helix transformation happens at 1.64%, the change from α-to π-helix is negligible.

However, 310-helix transforms to α-helix in 8.29% of cases while retains a 310-helix conformation for 53.4% of the cases. It shows significant transformations to non-helical states. π-helix in contrast to 310-prefers α-helical conformation (~57%) than retaining π-helix (3.87% of the times). Apart from α-helix conformation, π-helix to non-helical transformations are significant at 38.3%.

Collectively, the helical states transform to non-helical states at 28.8% of times. However, 61.72% of the cases that have 310-helix as initial conformation adapts a helical conformation while π-helix and α-helix initial conformations stays in the helical fold for 61.7% and 90% of times respectively. Therefore, indicating that positions that have an initial conformation of a helix will tend to remain as a helix.

Table 1.4 Exchange rates of helices expressed as percentages.

The table quantifies the Helical and non-helical exchange rates for helices. Most of the α-helix (88.3%) tends to remain as α-helix thus denoting the rigidity associated to it. 310-helix and π-helix changes to non-helical conformations (including coil), the most. Similar results can be interpreted from the table.

k-means Cluster analysis: Dynamic behavior of the helices during simulations

To understand the extent of transitions among helical states, clustering of the ensemble of conformations was done, based on k-means with k=5 clusters for comparative purpose. The clusters are named according to their major DSSP state, with subscript indicating a minor DSSP state (T for turn or C for coil). For example, the cluster α C will indicate a cluster majorly contains the α helix conformation but also has some coil states as well. The detailed composition of each cluster starting from the α-helix, 310-helix and π-helix initial state is given in Figures. 1.5, 1.6 and 1.7, respectively. The classification highlights that a residue initiated from an α-helical state (Fig 1 .5) tends to transit preferentially towards a β-turn state. Indeed, apart from the most populated cluster α 1 (76.4% of the residues) that is composed of residues remaining as an α-helix, the second cluster α 2 (11.5% of the residues) reveals a decreased content of α-helices in favor of β-turn states. In clusters α T1 and α T2 (4.2% and 6.6% of the residues, respectively), apart from a small subcluster depicting conformations that switch to the 310-helix conformation (light blue). This shift causes increase in population of β-turn conformations. The least populated cluster α C is associated with non repetitive structures (1.1% of the residues). Also, it underlines the correlation between flexibility and the presence of β-turns: the higher the β-turn or coil content, the larger the normalized RMSF (nRMSF). A similar correlation is observed with accessibility and Neq values. Clearly, the cluster α 1 represents most of the buried and stable α-helices.

For residues initially assigned as 310-helices (Fig 1.6), the most populated cluster is 310.

The 310 cluster represents residues that remain in the 310-helix conformation (40.5% of the residues). It is also the most rigid (both low B-factor and RMSF). From cluster 310 T1 to cluster 310 T2 (25.0 and 17.5% of the residues respectively), the 310-helix content decreases and the content of β-turns increases. The flexibility increases concomitantly. It also perfectly correlates with the relative accessibility (going from 32.8 to 36.3 and 41.8) and Neq values (1.34, 1.49 and 1.59, respectively). The preferred transition to the β-turn was expected as the 310-helix was shown to overlap by nearly 90% [150]. This is one of the reasons for the disappearance of β-turn type III [151]. The cluster 310 C has the highest content of non-repetitive structures and is, as expected, associated with high flexibility. Surprisingly, residues in this cluster are less accessible than those in clusters 310 T1 and 310 T2 . Cluster 310 α (10.5% residues) that represents the transition to α-helical conformation exhibits low accessibility values, the lowest RMSF values compared to clusters 310 C , 310 T1 and 310 T2 , and the lowest Neq values,which shows the slightest local conformation change of all the 310 clusters. However, it is associated to high B-factor values. Extent of flexibility can be estimated from the correlation. For e.g 310 C is the most flexible clusters with most of the 310-helices transforming to coil and bends. Cluster 310 α shows high Bfactor value but low RMSf which suggests that transition from 310 to α is not the only one dominating the cluster. As evident from the cluster as well as Table 1.5, that transition to Turns also contribute to the dynamics of this cluster. Figure 1.7 summarizes the dynamic evolution of the rare π-helices. The cluster named π (10.1% of the residues), which showed the highest frequency of π-helices, was also associated with the β-turn, bends and some coil conformations, but not αor 310-helices. This is, however, a tip of the iceberg of contradictions in this cluster. Cluster π was found to be associated with the lowest crystallographic B-factors and had the highest relative accessibility. During the MD, it had a very flexible behavior, with the highest RMSF values and also the highest Neq values observed. The contradictions could be explained with the following three clusters, π α1 , π α2 and π αT that have a higher α-helical content with no π-helix residues and few β-turns. They showed a slight increase in their Neq values. Cluster π α1 represents π-helix residues (38.7%) that had medium B-factor values associated with a higher stability as an α-helix because they are buried compared to other clusters. Clusters π T and π αT have intermediate flexibility behaviors, while the most flexible cluster is cluster π α2 . Extent of flexibility of π-helix can be easily estimated from the correlation, as most of the clusters lie on the right half having higher B-factor and RMSf values. For e.g cluster nπ T has less RMSf but higher B-factor value which suggests that π-helix to Turn transition does not lead to deformability. In contrast, the cluster with π-helices conserved has high RMSf value thus showing the inherent flexibility in π-helices. C) The cluster matrix for the assignments done using DSSPold. As can be observed that almost none of the clusters had π-helix representation.

Table 1.5 Analysis of different clusters. Shown are each cluster, its occurrence, the average normalized B-factors (nBfactors), the average normalized RMSF (nRMSF), the average relative accessibility solvent area (rASA) and the average number of equivalent (Neq) expressed as percentages.

Impact of secondary structure reassignment on the exchange rates and dynamics

For the reasons described in section 1.3.3 and occurrence of many contradictions in the π-cluster, it was required to reassign structures using DSSP v2.2.1. As expected, the major changes are seen in exchange rates of π-helices and cluster π while clusters αand 310 remains largely unaffected.

The exchange rates of π-helices are 28.5% to α-helices, 1.43% to 310-helices, and remains as π-helices 42.2% of the times. Therefore, the exchange rates vary largely from the DSSPold assignments where 56.9% of π-helices transformed to α-helix and only 3.87 remained as π-helices. Also, ~27.7% of π-helices transformed to non-helical conformations which shows a decrease of 10.6% from DSSPold non-helical transformations. This clearly indicates that the 38% of increase in π-helices is contributed by 50% decrease in α-helices conversions.

After reassignment, a pure π-helix cluster was found (named nπ) representing 20% of the residues. Figure 1.7A shows the new clustering for these π-helices. It is totally different from the previous cluster π, which was a mix of β-turn, bends and some coil conformations. Two other clusters (nπ α1 and nπ α2 ) are also found associated with the transition to α-helices; they represent 23.4% and 16.5%, while cluster nπ T is mainly associated with turns (25.5%). These three share common features with previous clusters π α1 , π α2 and π T, but the proportion of π-helix residues in these clusters is drastically higher. The only fuzzy cluster is cluster nπ V , which is a mix of α-helix, π-helix, β-turn, and β-bridge (14.7%). Therefore, the results after reassignment shows that the previous assignment of the π-helix strongly biased our views of this local protein conformation.

Conclusions and future perspectives

In a previous work from the lab on β-bulges, it was shown that one β-bulge from a 15 β-bulge containing structure disappears after 2/5 th of the MD simulation and never returns [START_REF] Craveur | β-bulges: Extensive structural analyses of β-sheets irregularities[END_REF]. Thus depicting that, sometimes non-classical conformations associated with a classical repetitive structure can show some unexpected behaviors. This could be the effect of their inherent flexibility. Therefore, current study was designed to understand the dynamic behaviors of repetitive structures at the basic level of structural complexity, i.e the secondary structures. Herein the focus is specifically on the helical structures while the rest states of secondary structures are under analysis.

The first pertinent result is the quantification of the persistence of helical residues in their original local conformation. More than 3/4 th of α-helix residues remain in the helical conformation while it decreases to 40.5% for the 310-helices. Surprisingly, even if π-helices are mostly buried, they are not observed to be stable. The second interesting result on the flexibility and deformability of helical structures is the huge difference between the three types of helix. The α-helix shows good correlation between stability of the α-helical content and (i) the flexibility as seen through Bfactors and RMSf, (ii) accessibility of the residues. The Neq analysis of α-helix residues depicts that besides persisting as α-helix, they have a higher tendency to assume β-turn conformations than either the 310-or π-helices. The 310-helix shows a similar general behavior in 90% of cases. Indeed, correlation is good in terms of flexibility (both crystallographic and in silico), accessibility (with the exception of cluster 310 C ) and Neq values.

Nonetheless, the 310-helix that transforms to the α-helix conformation shows different characteristics. It retains higher B-factor and RMSF values than the average of cluster 310 but is associated with lower accessibility and lower Neq. The cluster 310, seems to have the dynamic characteristics of a local protein conformation that can adopt an α-helical conformation [152].

Using classical DSSP (cmbi version), the π-helices cannot be described as stable and therefore new DSSP version 2.2.1 is used to reassign all the initial structures and trajectory frames. The residues that stayed mostly associated with the π-helix conformation are also associated with βturn, bend and coil conformations, but never αor 310-helices. A counterintuitive finding is that they are also associated with low B-factors but due to the high accessibility, they are very flexible/deformable thus showing the highest RMSF and Neq values. The other residues lose their initial π-helix conformation and mainly assume an α-helical conformation or to a lesser extent a β-turn conformation. Such dynamic behavior of π-helices with low B-factors and high accessibility may be characteristics of post nucleation, cooperative protein folding effect. Also, it was recently shown that π-helices help the protein chain to fold properly and also in helix packing. They facilitate favorable non bonded interactions by positioning the functionally important helical residues in the correct orientation [127]. Therefore, it indeed becomes fitting for π-helices to exhibit such dramatic flexibility in their dynamics.

For this analysis, 169 protein chains were selected with a limited redundancy from SCOP.

They are of high quality. However, one must not downplay the fact that crystallization also produces some crystal contact packing effects, and these were found in a limited number of cases [START_REF] Carugo | Protein-protein crystal-packing contacts[END_REF][START_REF] Luo | A structural dissection of large protein-protein crystal packing contacts[END_REF]. The crystal packing might have an effect on the initial assignment but during molecular simulations, it did not have a significant effect. It is important to properly define the properties of these helical conformations that can have implications in both experimental and computational studies, i.e. analyses of flexibility of protein local conformations, force field parameterization and disorder, etc.

The dynamics of β-strands and related DSSP states are analysed along with more precise local structure estimations using protein blocks. Chapter 2 will contain the details and discussion about their dynamics. Before transitioning to beta-strands, helical component of the protein secondary structures needs to be complete. Therefore, it is highly fitting to discuss about Polyproline-II helices (PPII) that are conventionally ignored by popular assignment softwares like DSSP, Stride, etc. A short discussion dedicated to PPII is provided as a supplementary to chapter1.

Chapter S1: Recent advances on polyproline II

About half of the globular proteins are composed of regular secondary structures like α-helices, and β-sheets, while the rest are constituted of irregular secondary structures, such as turns or coil conformations. Other regular secondary structures are often ignored, despite their importance in biological processes. Therefore, three-dimensional structure information is usually described as a simple succession of these repetitive structures (see Figure S1.1), connected by "random" coil [START_REF] Eisenberg | The discovery of the alpha-helix and beta-sheet, the principal structural features of proteins[END_REF]153]. Helical structures are locally stabilized by hydrogen bond patterns of backbone atoms (between residues i and i+4) [START_REF] Pauling | Views of helical peptides: a proposal for the position of 3(10)-helix along the thermodynamic folding pathway[END_REF], while extended structures are also maintained by hydrogen bonds but at longer distances [100]. As shown in previous chapters, the two forms are highly abundant as they represent 1/3 rd (helices) and 1/5 th (sheets) of the total residues. A third defined state, called β-turns, is characterized by the reversal of polypeptide chain and is stabilized by a hydrogen bond between the first and last residues [START_REF] Venkatachalam | Stereochemical criteria for polypeptides and proteins. V. Conformation of a system of three linked peptide units[END_REF]151,154]

Figure S1.1 Structural characteristic of three secondary structures. A) Right-handed α-helix, B) left-handed PPII, and C) A β-strand. The cartoon representation highlights the structural geometry, while ball and stick represent the atomic arrangements of the three secondary structures. The proline rings can be observed in (B), and the comparison of oxygen (red) and nitrogen (blue) clearly indicates the absence of intra H-bonding in PPII.

In A and C, the close proximity of oxygen and nitrogen atoms makes it favourable for intra Hbonding. High helical rise of the PPII and lack of intra H-bonding make its backbone highly solvent accessible. Images are generated with the PyMOL software.

S1.1 Introduction to polyproline II helices

PPII is characterized as a left-handed helical structure with dihedral angles characteristic to that of β-strands and with an overall shape resembling a triangular prism [158,159]. [167]. Interestingly, the presence of proline residues is not a strict requirement for a PPII and that indeed establishes PPII as a distinct class in secondary structures.

Rather, it has been advocated since 1993 to include PPII in mainstream secondary structures, such as α-helices and β-sheets [168]. A striking fact is that residues associated with PPII conformations represent nearly 5% of the total residues in a structure [169], but the lack of popular PPII assignment approaches hinders their systematic analysis.

S1.2 Amino acid compositions in PPII helices

A review article by Adzhubei and Sternberg in 2013 have refreshed the interest in PPII as mainstream secondary structures, such as α-helices and β-sheets. However, it also underlined the non-obligation of the presence of proline residues in PPII [170]. Numerous mutational studies, e.g., SH3 domain-PPII peptide binding analysis provided a desired assertion that PPII conformations are favourable in denatured space [171,172]. Impact of residue level mutations on PPII concludes that PPII conformation is retained even after successive changes of proline with alanine or glycine residues, implying that PPII are not constituted by a succession of proline residues alone. Therefore, PPII should rather be understood as a structural conformation found with different residue propensities in folded and unfolded states. Other experiments further establish PPII as a separate structural class [170,173].

Apart from these studies, restricted coiled library analysis performed by [START_REF] Jha | Helix, Sheet, and Polyproline II Frequencies and Strong Nearest Neighbor Effects in a Restricted Coil Library †[END_REF] explores the influence of neighbors on the residues having favourable PPII propensities [174].

Examination of the bias-free coiled library sets reveals dominant PPII conformation for ten of amino acid residues: Pro, Ala, Met, Glu, Leu, Asn, Cys, Gln, Lys, Gly, and Tyr Another proposal of similar propensities comes from Cubellis and co-workers who analysed position specific propensities in 5700 PPII helices and classified data with peptide lengths [175].

Thus, residues, such as Ala, Met, Lys, Thr, and Leu, favour PPII conformation in longer peptides, while Asp, Ile, and Glu adopts the conformation in shorter peptides (<3 res). Trp, Phe, and Gly do not favour PPII; however interestingly, Gly is present in a repetitive motif in collagen triple helix, while Trp and Phe have been crystallized in interaction with PPII-hydrophobic motif interactions.

Thus, supposedly, these residues could stabilize and mark the terminus of a PPII helix [175]. In the most recent survey, Kumar and Bansal show that 40% of PPII contain no Pro residues at all. Besides, aromatic amino acids are avoided within the helix, while Gly, Asn, and Asp residues are preferred in the proximal flanking regions [176].

Based on hard-sphere Monte Carlo simulations, the propagation of the PPII helix is logically explained by the interaction between the prolyl ring and the backbone (Cβ) of the previous residue. However, this logic breaks when a poly-Alanine adopts a PPII conformation, and therefore, a better explanation could be the neighboring environment and the presence of polar residues. PPII does not have characteristic main chain H-bonding pattern; thus arguably, Ser, Thr, Gln, and other polar residues can stabilize the PPII helix by non-local hydrogen bonding with the backbone [171,175] Therefore, the overall survey of amino acid propensities reveals that propensities of amino acids in PPII are highly context based. The composition of amino acids seems to deviate according to the presence of PPII in fibrous or globular protein context.

S1.3 Assignment methods for PPII

PPII dihedral angles are quite particular. The most classical way to analyse them is to use Ramachandran map (1963), as shown in Figure S1.3. As briefly discussed in thesis introduction section I.1., the map is based on calculations of dihedral angles between the two adjacent planes of protein backbone, hinged at Cα atoms. The dihedral rotation of the planes is restricted by the steric clashes that define the disallowed regions on the map. Therefore, the map is a very powerful tool to assess the stability of a structure based on the local analysis of degrees of freedom for dihedral planes. Further evolution of the map leads to the marking of areas for specific secondary structures, namely, α-helix, β-strands, and later β-turns (see Figure S1.3A). Lately, allowed region for PPII was assigned in the north-western quadrant of the map, allowed for β-strands (see Figure

Figure S1.3: Ramachandran Map. A) From a non-redundant dataset of the Protein Data Bank. B) highlights the allowed region for PPII helix assigned using modified DSSP approach [169]. Visualisation is done with the R software (R CoreTeam 2013).

More than 20 secondary structure assignment methods (SSAM) had been published in 30 years [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF][START_REF] Fodje | Occurrence, conformational features and amino acid propensities for the π-helix[END_REF][179][180][181][182][183][184][185][186][187][188][189][190][191][192][193][194][195][196][197][198]. They have been defined with various criteria [199]. The most popular SSAM uses backbone hydrogen bonding pattern-based methods. Nonetheless, very few SSAM assigns PPII to the protein coordinates. Only five SSAMs, to be more precise, include the assignment of PPII conformations.

XTLSSTR [187]: The first available approach was XTLSSTR , where a structure is assigned based on a simple approach similar to the visual inspection of secondary structures. It calculates three distances and two angles based on the backbone geometry and then searches for amide-amide interactions. It successfully assigns α-helix, 310-helix, extended β-strand, hydrogen bonded and non-hydrogen bonded turns, and polyproline (type-II) helices. SEGNO [182]: makes assignment based on distance and torsion angle calculation. For assigning PPII, it uses dihedral angles between the two-peptide planes separated by one and two residues, respectively, named diheco and diheco2. An important observation is that PPII is assigned when a residue is not defined as β-strand by SEGNO and lies within predefined values of Φ and Ψ angles.

Later, taking into account the range of the four diheco angles (220-270 and 100-140), the PPII helical conformation is assigned to the residue. These thresholds are relaxed for the termini of PPII with a minimum length of the helix to be three residues and the overall shape of PPII is deemed to be like a triangular prism.

PROSS [200]: uses the concept of mesostates from a torsional grid for the assignments. The grid is described as the unit squares covering all areas in a Ramachandran map. The grids are of two kinds based on their unit area: smaller unit square: fine grid and broader unit square: coarse grid.

Based on the type, each unit grid is referred to as a coarse/fine mesostate. Therefore, in principle, the Ramachandran plot is converted into a Φ/Ψ grid with marked regions (allowed, favourable, and disallowed) covering more than one mesostates. In a very similar approach related to SEGNO, PROSS also does not directly assign PPII conformation rather resolve it out after β-strand leftovers.

DSSP-PPII [169]

: is an extension of DSSP with included dihedral angle parameters for PPII assignment, thus isolating PPII from coils. DSSP-PPII uses dihedral space (Φ and Ψ, -75° and +145°) to define the core of PPII while increasing the space by a metric value, ε, radiating out at 1°. The value of ε is chosen as an equilibrium between the number of amino acids assigned as PPII by the three previous approaches plus, an extra constraint that two consecutive dihedral angles should be assigned as PPII. One of the major features of this method is to use DSSP that is already an established and trusted method for other secondary structure elements (SSE). Therefore, the code can be adapted to apparently any other assignment method, if and when required. A specific database had been proposed to the scientific community [169] A major caveat to use DSSP-PPII is that the DSSP at its foundation is the old DSSP (cbmi version) and not the corrected version from 2011 [144]. As discussed in section 1.2.3, the old version of DSSP is prejudiced towards π-helices and often assign them into α-helices or β-turns.

However, since the script is portable, current efforts are taken into account to adapt the script with new version of DSSP and include PPII helices too in chapter-1 analysis of α-, 310-, and π-helices.

ASSP [127]: is an extension of helical geometry calculation program, HELANAL-plus (Bansal et al. 2000) that is used to calculate the local helical structure parameters: twist, rise, virtual torsion, and radii. ASSP uses the difference between these parameters calculated over two or more adjacent Cα windows of four residues. Later, in the protocol, the overlaps are resolved based on the established minimum lengths of helices: α-( 4), 310-(3), π ( 5), and PPII [START_REF] Gilman | G Proteins: Transducers of Receptor-Generated Signals[END_REF]. Therefore, PPII conformations are assigned based on the helical geometry of the local region. Since it uses HELANAL, which further is based on Sugeta and Miyazawa, and Shakarji methods for helical geometry, ASSP tends to assign β-sheets with less efficiency [201,202]. Kumar et al, applied ASSP to analyse in detail the PPII [176] and found that near 3/4 of PPIIs occur in conjunction with αhelices and β-strands, and serve as linkers as well. They also underline a large number of CH•••OHbonds.

All these methods are well designed for PPII assignments. However, most of them tend to assign PPII by indirect approaches due to the different bonding patterns of PPII. Unfortunately, the number of PPII assignment approaches is still limited compared to SSAM for other secondary structure elements, and remains a limitation for the use by scientific community.

S1.4 Physiological importance of PPII

A distinct feature of polyproline helices is that unlike other SSE, they do not have intra-hydrogen bonding thus making the backbone as well as the side chains highly solvent accessible. Such conformations would be hankering for finding partners for hydrogen bonding and stabilization.

Therefore, the sequence and structural characteristics of PPII make it worth to be probed for partnered interactions. One of the important tools to study the PPII role in protein-protein and DNA-protein interactions is the SH3 domain models. SH3 (Src homology 3) domains are small yet important structural domains involved in cell signalling and regulation, e.g., Tyrosine kinases. SH3 domains are also well known to interact with PPII conformations [203]. Hence, host-pathogen models designed with SH3 domains are critical to understand the interaction space of PPII conformation with respect to proteins and/or nucleic acids.

Many such studies focusing on signal transduction and cell-cell recognition have been explored for potential PPII-protein and PPII-nucleic acid interactions [204,205]. For instance, Cterminus of Synapsin-I, a protein regulating synaptic vesicle transport in neurons, is a proline-rich region. Synapsin-I interacts with the cytoplasmic polyproline region of membrane protein, vesicleassociated membrane protein 1 (VAMP-I) [205]. Phosphorylation of a serine residue upstream of C-terminus PPII helix regulates the secretion of a synaptic vesicle, while VAMP-I helps in recognition. Similarly, in Ras-GTP signalling pathway, the SH3 domains of the adaptor protein bind to the polyproline region of SoS protein (xPxxPPPψxPx) leading to the exchange of GTP.

Another set of interactions [206] is in vacuolar sorting, where SH3 domain of phosphatidylinositol-3 kinase binds to the GTP-binding protein.

Structurally, it is acknowledged that the PPII helix-binding region of SH3 domain is a smooth hydrophobic surface flanked by conserved charged residues [206]. The PPII interactions also have a significant structural-functional role in transcription, as many transcription factors have proline-rich terminals [207]. This could also indicate the role of PPII interactions in multimeric complex formation during transcription. A well-characterized case of PPII-protein interaction is the RNA polymerase II (RNApolII). C-terminus of RNApolII has multiple copies of conserved motif YSPTSPS, which further is a two-fold SPXX motif. SPXX is a DNA binding motif found in DNA binding domains [208,209]. Furthermore, [START_REF] Hicks | The extended left-handed helix: a simple nucleic acid-binding motif[END_REF]

S1.6 Recent advances in PPII research

The growing interest in physico-chemical and structural properties of PPII, especially their short extended-helical structure has attracted the attention of pharmaceutical companies. Very recently, cell-penetrating vector approaches are designed based on PPII scaffold [START_REF] Fodje | Occurrence, conformational features and amino acid propensities for the π-helix[END_REF][215][216][217][218][219]. As discussed in section S1.4, PPII backbone has a high solvent accessibility and thus becomes highly hydrated in solvents. Therefore, use of PPII for cell penetration poses a challenge for hydrating the PPIIbased moiety and their convenient uptake in hydrophobic membranes [216]. Chmielewski's group [220,221] addressed this by designing and introducing cationic and hydrophobic moieties on the PPII backbone and observed no structural change. The compactness and inherent flexibility of the PPII conformation is the key to their adaptability and accompanied by cationic and hydrophobic moieties; they become highly suitable for a cell-penetrating vector [222]. A tremendous increase in PPII-based Cell-Penetrating Peptide (CPP) uptake compared to the traditional ones has been reported.

Another important difference is the claimed reduction in toxicity. This is based on the observations that PPII scaffold-based CPP: Sweet ArrowPeptides-SAP(E)-obtain a net negative charge unlike the traditional CPP which are positively charged [216,217].

S1.7 Summary: To consider PPII as a regular secondary structure

Polyproline II helix is arguably a distinct member in secondary structure elements, based on its geometry, sequence, and structure. PPII has a left-handed geometry unlike the right-handedness of popular protein helices (see Fig S1 .2). Its sequence composition varies based on the presence in a globular or fibrous protein environment. It is quite an interesting observation that proline, a major α-helix breaker or kink inducer, when in succession adapts a distinct helical form itself. Moreover, it dominates the α-helical form in denatured space. Such examples can be appreciated in light of the expanse of the secondary structural space. Although PPII conformation represents only 5% of the conformational space, it is highly recommended for it to be considered in the regular secondary structures. Besides, its representation is equivalent to, if not more than, the 310-helices. The involvement of PPII-protein and PPII-nucleic acid interactions in different pathologies, structural applications, and drug carriers makes it even more viable candidate to be included in the main regular secondary structures. Its potential role in Alzheimer's and Parkinson's could not be ignored, given recent publications on the subject. The presence of PPII in regular, ordered, and disordered regions while establishes that its distinctiveness is not sufficient to seize the complete structural space of PPII conformations. Therefore, more assignment approaches and coiled library experiments are needed to explore such conformations. Figure S1.5 shows the number of publications about PPII since 1968. The increase is clear, but remains limited. The number of papers had never been higher than 100 papers/per year. Therefore, we can hope for a better representation of PPII among regular secondary structures.

Figure S1.5. Year wise publications trends on Polyproline II helices. The bars depict the number of publications corresponding to the year on x-axis. An exponential function is represented in blue curve. Dark towers show the sudden surge in publications compared to the previous year.

Visualisation is done with the R software (R Core Team 2013)

Chapter 2: Understanding local structure behaviors using Protein Blocks

Introduction

In Chapter 1, a systematic analysis of dynamic behavior of helical structures was performed.

Therefore, Chapter 2 extends the similar question to β-strands or rather precisely, non-helical secondary structures which were not focused upon in Chapter 1. Besides using DSSP v2.2.1, Protein Blocks (PB) will also be used to have maximum coverage of the local structural space.

Since PB are 5 residue length based abstraction of the protein structure backbone, PBs are expected to provide better insights into the dynamics of local protein structure.

A brief recap

The α-helix (or 3.613 helix) and the β-sheet have been extensively analysed since their discovery by Pauling and Corey,151]. The secondary structure description of protein structures had led to the development of more than 20 secondary structure assignment methodologies [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF][START_REF] Fodje | Occurrence, conformational features and amino acid propensities for the π-helix[END_REF][179][180][181][182][183][184][185][186][187][188][189][190][191][192][193][194][195][196][197][198], with DSSP being the most popular one [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF]. Such descriptions have often been updated from time to time to include other types of secondary structures such as β-turns [START_REF] Venkatachalam | Stereochemical criteria for polypeptides and proteins. V. Conformation of a system of three linked peptide units[END_REF]150,155,186], PolyProline II [168,171,223,224] and loops categorization [225,226].

However, no single secondary structure assignment method (SSAM), have been designed for all types of known secondary structures. Therefore, specialists often have to use different SSAM based on its expertise and then normalize individual results to obtain a meaningful ss assignment.

Such an example can be seen in sub-chapter 3b of this thesis.

Limitations of SSAMs

Analysis using different SSAM can still have some limitations such as the non-definition or ambiguity of the coil state, some known problems with short repetitive structures and ofcourse, the discrepancies between different algorithms [198,[227][228][229]. Hence, alternative views have been proposed using systematic analysis of all local protein conformations. It has motivated the development of local protein structure libraries that (i) are able to approximate all (or nearly all) local protein structures and (ii) do not take into account the classical secondary structures. Such libraries brought about the categorization of 3D structures, without any a priori, into small prototypes that are specific to local folds found in proteins. The complete set of such local structure prototypes defines a structural alphabet [230].

Why Protein Blocks?

The precursor research in defining a structural alphabet was carried out by . This led to numerous applications, from the analysis of sequence-structure relationship [232] to the prediction of short loops [229], etc. In this context, Protein Blocks have been the most successful structural alphabet [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF]233] and following are few studies where PBs are extensively used:

(i) 3D protein backbone description [233], (ii) Local structure prediction [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF][START_REF] Etchebest | A structural alphabet for local protein structures: improved prediction methods[END_REF]234], (iii) Description and the prediction of long fragments [START_REF] Zimmermann | LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach[END_REF][235][236][237][238][239], (iv) Prediction of short loops signatures [START_REF] Dudev | Discovering structural motifs using a structural alphabet: application to magnesium-binding sites[END_REF]. A recent impressive development concerns the inclusion of PBs in threading approaches [250,251] and especially to an efficient one called ORION [252,253].

Interestingly, PBs show great use for analysis of protein flexibility [START_REF] Craveur | Protein flexibility in the light of structural alphabets[END_REF] using molecular dynamics in the specific cases of Integrins [254,255], transmembrane proteins like KISS1R [211] and NMDA Receptor Channel Gate [256]. Therefore, PB was selected as the choice for structural alphabet to analyse the dataset of 169 protein domains (see section 1.2.1).

Previous studies on local flexibility of protein structures

The number of large-scale analyses of protein dynamics remain slightly limited. A prominent one is the Dynameomics project [257]. It simulated a representative sample of all globular protein meta folds and focuses on the unfolding process. Although a robust study to probe protein folding, it does not correspond to the analyses of protein flexibility. A database is available and provides visual results [258]. A more related study can be the work of Grubmüller's group namely Dynasome [259]. They showed that in the 34 different descriptors defined to characterize the simulation of the 112 proteins, only a few Collective Dynasome descriptors describes most of the movement. However, that cannot be defined as a local structural analysis as the Dynasome descriptors define the structures globally.

Thus, it would be worth to see the dynamics of proteins from the local structure perspective that can provide insights on the inherent protein flexibility.

Methods

All the methods used were same as Chapter 1, given that the same data set was operated upon.

However, the clustering method as well as use of PB is different.

2.2.1

Data set preparation -Same as section 1.2.1 2.2.2 Molecular dynamics simulations -Same as section 1.2.2

Analysis of local protein conformation

For the analysis of non-helical states, the secondary structures were assigned using DSSP ver 2.2.1 for the same reasons as cited in section 1.2.3. The DSSP states analysed under the current chapter are: Turn (T), Bend (S), β-bridge (B), Extended β-strand (E), and coils (C) Protein Blocks were assigned using PBxplore [START_REF] Barnoud | PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks[END_REF] toolkit from GitHub. To follow the evolution of a local protein conformation in regards to its original PB assignment, a simple constituency PB ratio, named C PB was calculated. C PB is the percentage of times the PB x is found associated at this position where x is the initially assigned PB.

k-means Clustering

A residue is initially associated to one of the 8 defined secondary states assigned by DSSP [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF] and one of the 16 PBs [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF]. During MDs, DSSP and PBxplore are used again to assign the protein chain secondary structures. Hence, each residue is associated to a vector of size S = 8, representing the 8 defined secondary states and more specifically the occurrence of each observed state. For PBs the vector size is S = 16. To define common behaviors between residues, a k-means clustering approach was used [145].

At first, a subset is created. It represents all the residues that were associated to this state before MD, e.g. PB d. Then a fixed number of clusters k is determined. The k clusters are of size S=16 for PBs and S = 8 for secondary structures analysis. All the data of the subset is compared to each of the k cluster, and the one with the minimal Euclidean distance is considered as the winner.

After one cycle (after all the subset have been used), the values of the k clusters are modified to correspond with the associated observations. That is each cluster is the barycentre of the associated observations. After few cycles, the k clusters are stable and can be analysed. This will provide answers to questions like, how the residues associated to PB d assignment have evolved.

Results and discussions

Analyses of the data-set and simulations

In the secondary structure dataset, α-helix has the most assignments at a frequency of 31.4% succeeded by the frequency of extended β-strand (E) at 24.5%. Least assigned secondary structures by DSSP v2.2.1 were π-helix (0.32) and β-bridge (1%). Table 2.1 summaries the frequencies for different DSSP states.

Table 2.1 Frequencies of occurrences of DSSP states during dynamics. The table summarizes the frequency of occurrences of all the DSSP states during MD simulations of 169 domains.

Protein blocks also showed similar statistics with PB m (approximately the core of an αhelix) being assigned the most (28.9%). PB d that can be assumed closer to the core of a β-strand was assigned 19.8% of time. If a complete correspondence is to drawn between the frequecies DSSP states and PBs, then α-helix and β-strands are represented at 31.4% and 24.5% of times respectively by DSSP while ~35% and 30% of time respectively in PBs. For PBs, the values are calculated by adding the frequencies of PB l, m, and n, and PBs c, d, and e, since PBs l and n approximate terminus of an α-helix while PBs c and e approximates β-strand N and C caps. Table 2.2 summarises the observed frequencies of all the 16 PBs.

Dynamics of the non-helical secondary structures

Figure 2.2 shows a summary of the dynamic evolution of all non-helical states. Near 95% of the residues assigned to β-strand remains as β-strand during the dynamics while 4.2% goes to coil state. Interestingly, the rare β-bridge that tends to remains associated to β-bridge, also goes to coil state 12.8% and β-sheet with 10.3% of times. Unlike helical states, where interconvertibility was seen to some extent, non-helical states tend to have more perseverance. However, slight exchanges can be seen between bends (12.07%) and turns (7.23%).

Figure 2.2 Dynamic exchanges of non-helical DSSP states. All the initial DSSP non-helical

states are shown on x-axis (a vector S=5). On y-axis are all the DSSP states to which changes during dynamics are measured. The color scheme varies from blue to red, with red being the maximum and blue being minimum. The β-strand can be seen to remain as β-strand for 94.34% for times. This suggests the rigidity associated with β-strands. Turns (T) and Bends (S) remain as T and S for 75.69% and 74.77% of times but also interchange with: T to S -12.07% of times while S to T -7.23% of times.

Cluster analysis of non-helical states

Clusters were named with the most characteristic state other than initial state. For instance, β bridge would translate to that β-strand dominant cluster have β-bridges as the second most dominant state in the cluster.

Clusters of β-strand

The first cluster is cluster β (>99% of 'E' assignments). is represents 92.2% of the occurrences with extremely low normalized B-factor (-0.48) and normalized RMSf (-0.53), it corresponds to the most buried part of the dataset (mean relative accessibility of 14.4). Figure 2.3 shows the clusters for β-strands (E) in detail. The last four clusters have higher relative accessibility ranging between 21.6 and 28.9. They are named β C1 , β bridge , β Turn , and β C2 . As expected β C1 and β C2 are the most occurring with a frequency of 3.9% and 2.4%, respectively. The β C1 cluster is relatively more rigid with nBfac value of -0.20 and nRMSf of -0.22 compared to -0.03 of β C2 cluster. β C1 also have the most β-strand content with near 2/3rd of the cluster consisting of β-strand vs only 14% in case of β C2 .

Figure 2.3 Evolution of β clusters. The image comprises of a table that provides details about the individual frequency of the cluster and structural properties along with the behavior of DSSP states in different clusters

A β Turn cluster having a small frequency of 0.84% also appeared. It is quite flexible with regards to other clusters and display the highest accessibility among the 5 β-strand clusters.

However, β bridge forms a rigid cluster second to pure β cluster with nBfac of -0.31 and a low RMSf of -0.22. Surprisingly, it is the cluster with least frequency (0.6%) and represents 54% β-bridges, 6% coil and 39% β-sheet.

Figure 2.4 Evolution of β-turn clusters. The image comprises of a table that provides details about the individual frequency of the cluster and structural properties along with the behavior of DSSP states in different clusters. A major share of the cluster Turn α is contributed by α-helices (52%). This depicts the interconversion among helices and turns.

Clusters of β-turns

Venkatachalam first described and classified the β-turns as hydrogen bond turns [START_REF] Venkatachalam | Stereochemical criteria for polypeptides and proteins. V. Conformation of a system of three linked peptide units[END_REF].

Later, the definition of turns evolved from an energetic to a distance criterion between Cα [151].

DSSP differentiates between hydrogen bond turns (namely turns, T) and non-hydrogen bond turns (namely bends, S) [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF]. A turn has a perseverance with a rate of 75.7% (see Fig 2 .2) while it can transform to bends at 12.1% or to an α-helix (4.7%), coil (4.0%), 310-helix (3.0%) but rarely it is seen transforming to bridges or strands. The percentage of bends and helical state is expected, as 310-helix was often confused with type III β-turn (obsolete).

The clustering reflects these results with 5 cluster belonging to Turns (Fig 2 .4). Pure turn cluster, represents 63.8% of the initial turns while the rest four in decreasing order of their turn representation are: Turn bend1 , Turn bend2 , Turn α , and Turn C . The clusters are characterized by higher normalized B-factor (0.42 to 0.88) and normalized RMSf (0.32 to 0.74). All clusters show high relative solvent accessibility above 50% with an exception of Turn α having 36.9% accessibility. In conclusion, (hydrogen bond) turns are not so rigid with the extreme being Turn C having an nBfact of 0.88 and nRMSf of 0.74.

Clusters of bends

Slightly less frequent than turns, they also transform less to helical states relative to turns.

Consequently, the perseverance analysis reveal that 71% of bends remain as bends and 14.8% goes to coils, 7.2% to turns and 1.6% to β-strands (Fig 2 .2). These results are similarly reflected in the five clusters for bends.

The pure bend cluster occurs 63.8% of the time, followed by bend C1 , bend C2 , bend turn , bend β in decreasing order of their occurences of bends. Figure 2.5 show the details about the 5 bend clusters. The clusters, bend, bend C1 , and bend C2 are more rigid than Turns clusters with lower nBfac, nRMSf and rASA values. The bend turn is an equivalent of the two Turn bend (Turn bend1 , Turn bend2 ). The unique cluster among 5 bend clusters is the bend β that transforms at a rate of 63% to β-sheet, 25% to bends and 11% to coil. It has the lowest nBfac value, lowest rSA yet accessible at 30.1% but surprisingly, it has the highest nRMSF of 0.98 that is the highest mobility observed in any of the non-helical clusters.

Figure 2.5 Evolution of clusters for bend. The image comprises of a table that provides details about the individual frequency of the cluster and structural properties along with the behavior of non-helical DSSP states in different clusters.

Although bends and turns are similar in structure yet, for Bends unlike turns, an α cluster does not appear. Although in Bend Turn cluster slight tendencies to change to helices can be seen. This can also arise if there is a misidentification of turns (59%) by DSSP.

Hence, even if turns and bends are highly comparable, they have unexpected specificities.

The lack of hydrogen bonds at short range allows for a limited number of bends to participate dynamically in β-sheet and forms a specific recurrent cluster. For the turns, a specific cluster exchanges with α-helix state, but not specifically w ith 310-helix.

Overall PB analysis with respect to the initial assignment

As seen in Figure 2.1D, more than 60% of the residues have an Neq of 1.0, i.e. no change of PB assignment during the whole simulation. This rigid-constituency is highly dependent of the type of PBs. PBs geometrically related to core of repetitive structures like PB m (for α-helix) and PB d (for β-sheet) remained preserved at 100% with respective frequency of 86.2% and 75.4% (the values pertain to C PB as shown in Table 2.3).

Table 2.3 C PB -Frequency of PB staying in the initial PB assignment.

Analysis done using 8 different thresholds with 100%, the residues that are only seen during their dynamics associated to their initial PB assignment. Followed by 99-90%, 89-75%, 74-50%, 49-25%, 24-10%, 9-1% and finally less than 1%.

It decreases very rapidly in a strong gradient with PBs n (66.6%), l (63.2%), i (60.7%), a (60.0%), f (59.9%), k (56.5%), h (53.7%), o (51.2%), c (50.1%), b (46.5%), e (43.1%), p (39.9%), j (19.1%) and g (16.2%). These C PB tendencies hold valid at every level of perseverance of the initial PB as well as inversely. Therefore, if an assigned PB does not have high conservation for 100% of the time, its occurrences at shorter intervals, like 49-25% or 24-10% will add up to the C PB . Thus the PB at that position remains preserved. For instance, the lowest C PB in 100% threshold is for g (16.2%) and j (19.1%). At less than 50% of the times, the highest C PB are found associated to PBs g (40.3%) and j (16.2%) and not with PBs d (4.8%) and m (3.9%). Hence a strong correlation exists between the original assignment and the conservation of the local protein conformations. This leads to the occurence of the same PB and therefore low Neq.

However, a simple question may arise that if such C PB observations are not due to the accessibility of the residues? If a local protein conformation is accessible, it's probability to change increases. Such tendency does exist but is not a binary case for every PB (see Fig 2.6).

For PBs m and d, the percentage of rigid position (C PB >75%) is largely higher than in the deformable class (C PB <25%) and is directly linked to their solvent accessibility. However, the PB n does not show this simple (and expected) tendency, the difference between rigid and deformable position is not significant. Depending on the type of PBs, it goes from a slight tendency to no tendency at all. For instance, PB j that is one of the two less constraint PBs, is more exposed than the others. It has the same distribution of relative accessibility in the deformable classes and the rigid ones. Hence, no specific rules can be observed here. c, e, m, a, d, andn. This is reflected in the Neq plot on the alternate y-axis.

Cluster analysis of Protein blocks-PB evolution

Clusters were generated using k-means approach to study the behavior of individual PBs. Thus, 80 (16 * 5) clusters were generated and analyzed. Figures 2.8 through 2.11 shows a summary of their recurrent behavior highlighting clusters of PBs a, b, g and f. The clusters are named based on the dominant PB in the cluster.

Clusters of PB a

The five clusters of PB a shows interesting results. Figure 2.8 shows the details about clusters of PB a. Cluster a1, is the cluster with >98% of PB a and represents 4/5 th of positions initially associated to PB a. Surprisingly, it does not have the least normalized B-factor values.

Although it is associated to the lowest normalized RMSf and one of the lowest relative solvent accessibility values. It also represents a first example of that the more stable cluster (i.e. PB x that stay PB x) is not always associated to lowest nBfac and rSA.

Cluster a2 represents another behaviors, namely, the cluster that is still highly controlled by the original PB, but also goes to a large number of other local conformations. Therefore, in Cluster a2, PB a still represents 67% of the occurrences but with 6 PBs at an interconversion rate of more than 2%. The PBs to which initial assignment of PB a transforms to are: PBs b, c, d, f, l and m.

Cluster a3 introduces more fuzziness in PB a cluster. Only, 22% of PBs that were initially assigned, PB a remained as PB a. Most of the changes are shown to be attributed to PB c (65% of PB c).

Cluster a4 represents the fuzzy cluster, it represents only 5.3% of the original PB and therefore, must be the more deformable. The average Neq of 2.36 (overall), and cluster Neq of 8.43. it is also associated to highest accessibility, highest nBfac and highest nRMSf values. In our previous work, geometrical compatibilities among PBs were assessed by considering the second best PB for every local conformation [START_REF] Etchebest | A structural alphabet for local protein structures: improved prediction methods[END_REF]. According to this concept of geometrical resemblance, similar PBs can interchange more often. Clusters a3 and a5 followed such geometrical transitions, since they have respectively high frequencies of PB c (65%) and PB d (76%). However, no clusters with strong evolution to PB f can be seen. vs. -0.06 for cluster b3). Interestingly none of the three following clusters have used the expected major geometrical transitions (PBs d, c and f). However, geometric transition changes are observed for PB l for cluster b2 (22%), PB k for cluster b3 (64%) and PB a for cluster b4 (65%). Only cluster b3 can be considered as comparable with cluster b1 in terms of nRMSf and nBfac and closest rSA.

Clusters of PB b

Cluster b5 showed the maximum fuzziness with transitions among ~10 different PBs. Interestingly, the transition of PB f to PB d is associated with high nBfac, high nRMSf and high rSA that is quite uncommon for PB d. Cluster f2 is less stable than cluster b1 with a lower PB content of PB f (69%).

Figure 2.10 Evolution of clusters of PB f. The figure comprises of a table that provides details about the individual frequency of the cluster as well as static and dynamic structural properties.

Values for both Neq are given; the average Neq (Neq) and Neq of the cluster (cl. Neq). The spread of each cluster of f is shown. As apparent, one 'stable' cluster herein, f1. While one entirely fuzzy cluster among the 5 clusters is expected given the increasing deformability. f4 shows the characteristics of the fuzzy cluster. Transformations among PB f, g, h are considered geometrical transitions based on their geometric resemblance. However, cluster f3 and f5 shows a nongeometric transition from f to e and d.

Clusters of PB g

Finally Figure 2.11 shows the details about clusters for PB g. Cluster g1 (>89% of PB g) represents 55% of the original PB g positions and have the lowest rSA and lowest nRMSf but the second lowest nBfac (0.04 vs. 0.03 for cluster g2). As seen in the previous sections, PB g does not stay as PB g as often than other PBs. The following clusters are composed of 33%, 24%, 20% and 16% of PB g. The first surprise cluster is cluster g2 directed by PB e (57%) that is quite comparable to cluster g1 in terms of protein flexibility characteristics (similar nBfac and nRMSf). Cluster g3 was more or less expected as PB c is an expected geometrical transition; it represents 64% of the cluster. Surprisingly, the cluster g4 is controlled by PB p (52%) that is not a major geometrical transition.

Figure 2.11 Evolution of clusters of PB g. The figure comprises of a table that provides details about the individual frequency of the cluster as well as static and dynamic structural properties.

Values for both Neq are given; the average Neq (Neq) and Neq of the cluster (cl. Neq). The spread of each cluster of g is shown. As apparent, one 'stable' cluster was expected but a slight deformable g1 is observed. While one entirely fuzzy cluster among the 5 clusters is expected given the increasing deformability. g5 shows the characteristics of the fuzzy cluster but g4 also appears to be slightly fuzzy.

Summary of the clustering

For each PB, one cluster represents the initial PB with high frequency with more than 95% of initial PB, except for cluster g which represented PB g only 89.2% of times. This 'stable' cluster is not always associated to the lowest normalized B-factors and lowest mean relative solvent accessibility. One cluster among the five cluster is a fuzzy cluster with the highest average Neq and especially highest cluster Neq. The three remaining clusters are divided into:

(i) a cluster that is a degenerated version of the 'stable' cluster, often having more than mere 60% of initial PB and a mix of others eg, clusters a2, b2, c2, d2, f2, h2, i2, j2, k2, n2, o2, and p2.

(ii) Clusters that are directed by unexpected PBs, i.e. not from the major geometrical transitions.e.g. clusters b2, b3, b4, c3, e2, f3, f5, g2, etc.

(iii) The remaining clusters follows a major geometrical transitions among PBs Comparison of the obtained clusters showed that most of the fuzzy clusters are highly similar and that most of the other clusters with unexpected PB does not cluster with their associated PB clusters. Figure 2.12 highlights that the initial local conformations can go to very different conformational behaviors. Interestingly, near no cluster from a given PB is associated to one of its related generated cluster. Such that, cluster f1 is closest to cluster e3, cluster f2 is closest to d4, cluster f3 seems closest to e1, cluster f4 is a fuzzy cluster, and cluster f5 is closest to cluster a5. The dynamics, therefore have a strong local protein conformation impact that have been clustered and described.

Conclusions and perspectives

In the current analysis, MD simulations were performed on a large set of 169 representative protein domains. In chapter 1, it was shown that only 76.4% of the residues associated to α-helices retain their conformation, while this tendency drops to 40.5% for 310-helices and is never seen for πhelices. Taking the logical step further the current study extends the analysis to non-helical conformations and PBs. The resulting analysis confirms the rigidity of sheets, but also underline its capacity to transform into turn conformations. While the dynamics between turns (with hydrogen bond) and bends (without hydrogen bond) have some strong similarities, the two conformations behave distinctively. The turns can transform to helical structures while bends prefer to go to extended structures.

An entire analysis of a large set of protein dynamics simulations using a structural alphabet is performed. It is done on two levels: (i) a global view in terms of PBs, (ii) performing clustering for each types of PBs. Systematic analysis of PBs provide surprising results with multiple informations. As expected a large part of the buried positions remain highly stable, but it is not an observed (fixed) rule. In fact, for at least half of the PBs, the fact to be buried or exposed does not change its dynamics, at all. The majority of PBs tend to remain as their original PB, or at least with a high frequency. Some PBs have a higher tendency to be not as rigid as others and it is particularly true for PB g and PB i. The intriguing fact is that the change from a PB to another one is not an obvious geometrical change. It is more frequent to go to an unexpected PB than an expected one (due to its geometrical compatibility).

The use of two types of classification shows the difficulty to cluster properly these dynamical properties but it indeed improves (i) our knowledge of protein dynamics and (ii) the relationship between sequencestructure and dynamics.

Chapter 3: Understanding local protein flexibility in light of physiological structural events: Case studies

Proteins are the functional currency of the biological systems. All the molecular events from DNA replication, transcription, translation, to sorting, transport, expression, to signalling involve proteins in important roles. Such diversity of functions often involves the same set of proteins but with some molecular variations. For instance, phosphorylation, glycosylation, SUMOylation, acetylation, methylation, etc, at certain sites in a structure induces conformational changes. Thus contributing to its functional versatility. Therefore, it is indeed important to understand the impact of such structural changes on local structure dynamics. However, changes may be specific to certain amino acids. For example, N-glycosylation found on asparagine in the specific consensus sequence Asn-X-Ser/Thr; where 'X' can be any amino acid residue but proline [263]. Also, PTMs are extremely diverse, ranging from the addition of a small group of atoms, such as phosphorylation

Figure 3a.1 PTM cross-talks and information sharing is indicative of a PTM code. A) A schematic representation of cross talks between the PTM inducing proteins, like Kinases, known as writers, the PTMs like phosphorylation on regulatory centers, and the cellular function inducing readers. The dashed lines show the cross-talk between different PTMs. The sequential numbers

depict the sequence of events. These indicate that a protein can reach multiple functional states using PTM-driven logic gates, thus indicating a PTM code. B) shows an example of such PTM code to exist using PTM network of p53 regulation. Different PTM inducing proteins modify p53 genes with their respective PTMs. The fate of p53 regulation can follow different pathways depending upon the proteins that read the PTM induced by Writters. The proteins functions and their 3D structures are intrinsically related. Hence, it is expected that PTMs, which regulate function, impact the structure of proteins as well. Several previous studies have investigated the effects that PTMs could have on the protein structure and dynamics, using X-ray data [290], and NMR data [291]. Xin and Radivojac [290] computed local and global RMSDs between modified (with at least one PTM), and unmodified PDB chains of the same protein. They concluded from the statistical analysis of their RMSDs that N-glycosylation and phosphorylation induce conformational changes, with a limited impact, at both local and at global levels, with a larger influence for phosphorylation. On their side, Gao and Xu [291] suggest that disorder-to-order transition could be induced by the modifications of phospho-serine/-threonine, various types of methyllysines, sulfotyrosine, 4-carboxyglutamate, and potentially 4hydroxyproline.

Also intrinsically disordered protein regions have been associated with numerous PTMs, as hydroxylation, methylation, and notably phosphorylation [291][292][293][294] These PTM databases contain crucial sequence annotations, specific to some PTM types and/or organisms [298], and provide related structural data thus mapping the PTM sites on corresponding structures in Protein Data Bank (PDB) [299].

Numerous machine learning methods consisting of predicting PTM sites were published recently. They mainly focus on certain types of PTM and/or organisms, and differ in their learning protocols (support vector machine, random forest, neuronal network, etc.), and in the set of descriptors extracted from the mining of the experimental data [297,300]. Few of them, used descriptors derived from structural data, such as prediction of secondary structures, disorder and accessible surface area [301,302], or from structural properties extracted from PDB [303,304]. Besides a global view on PTMs, the database also provides details for each PTM and further connects to different PTM information and annotations found in other databases. Such data are very informative for studying relationship between PTMs and protein structures, for designing comparative modeling protocol, and for prediction protocol based on different approaches, for example, on secondary structure descriptors.

Figure3a.2 PTM-SD, a database of structurally resolved and annotated PTM in proteins.

A summary of the PTM-SD database Query and Search page. Also depicting the tools that can be implemented to reduce redundancy (clustering), compute statistics and Neq (local entropy) values. Using these tools, a customized dataset can be created from the required query with PDB and/or UniProt ids, selective type of PTMs based on specific modified residues in specific organisms. Similar queries were used to generate the dataset used for studying effect of PTMs on the protein backbone conformations (selections highlighted in green).

Since PTM-SD gives access to X-ray structures of modified residues in proteins that specifically correspond to all PTM annotations along with their statistical characterization like Neq [136]. It was used to investigate the impact of PTMs on the protein backbone conformations observed in crystallographic data. The currrent structural analysis is focused on understanding the following:

I.

The diversity of the backbone conformations of N-glycosylated and phosphorylated regions.

II.

Local and global effects on the backbones were compared between 4 specific examples of PTMs associated to a high number of experimental data.

III.

The conformational changes of the presence and absence of PTMs on the protein were also compared, in regards to the backbone flexibility.

3a.2. Methods

3a.2.1 Dataset preparation

The dataset was generated using PTM-SD. It comprises of structures pertaining to phosphorylation, N-glycosylation and methylation while also contains corresponding structures without a modification. Table 3a.1 summarizes the dataset composition. The comprehensive dataset included a total of 9,870 PTMs that are present on 5,948 structures. From these PTMs, 7,110 modifications are N-glycosylation while 1,874 are phosphorylation and 886 are methylations. The dataset was further refined to remove redundancy (>25% identity) using PTM-SD clustering toolkit. The percentage identity signifies that the sequences in each cluster have greater than 75% identity and the intercluster sequences will have more than 25% difference in their sequence identity. In summary, it removes the same type of PTM at the same position if the sequences are 75% identical. A derived dataset was also generated to assess the impact of PTM on the global structure.

Therefore, a dataset comprising 4 proteins; Renin endopeptidase (N-glycosylation), Liver carboxylesterase (N-glycosylation), Cyclin dependent Kinase 2 (Phosphothreonine) and Actin (Methylation) was generated (refer to Table 3a.3).

Table 3a.3 Dataset to analyse local and global impacts of PTMs on 4 proteins. Four proteins as listed in

Column1 are selected to study the impact of PTM on the protein structure. Column 2 lists the modification taken into account while Column 3 & 4 are the no. of structures used for comparison of structural impact in presence and absence of the PTM, respectively.

3a.2.2 Protein Blocks (PB) assignment

PB assignment was done using our in-house PBxplore tool [START_REF] Barnoud | PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks[END_REF]. The PB assignment translates a 3D structure to 1D sequence of PBs. The input is the structure coordinate file from PDB, representing an X-ray structure with or without PTM. The algorithm uses 5 residues long window for each position. For each "n th " position, 8 dihedrals ψn-2, φ n-1, ψ n-1, φ n, ψ n, φ n+1, ψ n+1, φ n+2 are compared to the reference set of 16 PBs. The comparison is performed using the RMSDA criteria (Root Mean Square Deviation on Angular values) [START_REF] Schuchhardt | Local structural motifs of protein backbones are classified by selforganizing neural networks[END_REF]:

𝑅𝑀𝑆𝐷𝐴 (𝑉 1 , 𝑉 2 ) = √ 1 2(𝑀 -1) ∑ 𝑀-1 𝑖=1 [𝜓 𝑖 (𝑉 1 ) -𝜓 𝑖 (𝑉 2 )] 2 + [𝜙 𝑖+1 (𝑉 1 ) -𝜙 𝑖+1 (𝑉 2 )] 2
where, V1 is the 8 dihedrals vector extracted from the 5 residues long window; V2 is the 8 dihedrals vector corresponding to the compared PBs. PB, which gets lowest RMSDA is chosen as the representing conformation observed in the window.

3a.2.3 Local structure entropy -Neq

3D structures of a specific protein could be observed with different conformations in X-ray crystals, or during molecular dynamics simulations. This could be attributed to the intrinsic flexibility of the structure or the consequences of interactions with small molecules (ligand, cofactor, water molecules), or macromolecules (proteins, DNA, RNA). Under such scenarios, each of these 3D conformations would be assigned a different PB sequence (see Fig 3a .3). By analyzing the variation of PBs at each position, it is possible to investigate the local conformational changes in a protein structure.

The equivalent number of PBs (Neq) is a statistical measurement similar to Shannon entropy and represents the average number of PBs observed at a given position [START_REF] De Brevern | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks[END_REF]. Neq are assigned using PTM-SD utility toolkit where Neq is calculated as follows:

𝑁𝑒𝑞 = 𝑒𝑥𝑝 (-∑ 16 𝑖=1 𝑓 𝑥 . 𝑙𝑛(𝑓 𝑥 ))
where fx is the frequency of PB x (x goes from a to p). A Neq value of 1 indicates that only one type of PB is observed, while a value of 16 is equivalent to a random distribution. Most of the statistical analyses were done using Python programming language and R software [312].

3a. The farther the positions are from the PTM sites, the higher is the disorder in the structure;

suggesting that less residues were available at these positions in the PDB chains to be used for the PBs assignments and the Neq computation. However, the data used is diverse enough to reach high level of Neq (6.48) Despite the intrinsic link between PTM and protein function, the molecular effects of the modifications on the protein structures and dynamics remains poorly understood. Our study, like previous systematic studies of structural data of modified and unmodified protein [290,291], shows that these effects could be of multiple types (stabilization and destabilization), at different scales (at the local PTM region, in other part of the protein as allosteric effect, or at a global level), and depend of the PTM types. However, in order to propose general rules for the molecular impact of each type of PTMs, additional structural data related to the large amount of PTM annotations already available is needed. In the scope of a systematic study, these data have to be used carefully.

Indeed, many factors, independent of the presence of PTMs, could have affected the structure of the proteins, such as the crystallographic packing, the presence of engineer mutations or crosslinks to help crystallization process, the presence of ions, ligands and protein partners in contact with the protein structure of interest.

Molecular modeling of PTMs combines with molecular dynamic simulation is an interesting alternative. Some recent computational studies have investigated the effect of PTMs

[300] on the stability of specific proteins. However, the success of such simulations also rely upon the growing number of experimental data, for the development of accurate PTM force field parameters. Once standardized, such molecular dynamics protocols can be of great use to understand impact of multiple PTMs on the structure of a protein.

A critical caution for any systematic, PDB based structural analysis is the uncertain nature of missing regions. As expected, in our datasets numerous PDB structures lack coordinates for some regions, which is depicted by a dip of the red curves in Neq plots. These particular regions mainly correspond to disorder regions in protein, which diversify the functional spectrum of However, there lies a subtler side of disordered proteins that pertains to regions that are either ordered or disordered based on their environmental context or interacting partners [315,317].

Such regions in proteins are called conditionally disordered regions. Many enzymes and viral peptides behave in a similar manner where the structural orderliness changes with respect to their binding partner. Between these structurally disordered proteins and those with well defined three dimensional structure lies a conceptual boundary thus defining the structure-disorder continuum

[315].

3b.1.1 Dual Personality Fragments

Assumed to be lying at the boundary of structure-disorder continuum, these protein fragments can transit from order to disorder and therefore exhibit properties of both the states. Thus many of such protein regions are visible in crystal structures, for eg. the catalytic loop of an enzyme. These fragments have been described by various names. They are called "dual personality fragments" by Zhang et al., 2007 [313], "Ambiguous regions" by Le Gall et al., 2007 [324], and "twilight zone"

by Szilágyi et al., 2008 [315,325]. Due to the presence of such structures, the earlier version of Disorder refers to a highly flexible ensemble of structures co-existing such that a definitive structure cannot be identified. Therefore, these are highly difficult to detect. NMR can show the ensembles with large deviation in certain regions but the technique is limited by the small size of proteins. Therefore, there is not enough data generated for a systematic analysis. On the other hand, X-ray crystallography can indirectly indicate disorder. Due to their highly flexible state, the disordered region would be shown as a distortion or noise on the diffraction pattern. Therefore, such region would be missing in the X-ray coordinate files. Since, DPF are structures that can transit between order and disorder, therefore some of the ordered state of a DPF would be crystallized [313].

The rationale behind using the X-ray data is founded on the generation of crystals for crystallography. It is common to observe multiple crystal structures of the same protein. Often, with motive of improving the resolution, crystallographer alters the crystallization conditions, or introduce mutations, or add co-factors to the crystal solution, etc. From a crystallographer's perspective, these will yield different crystals with better resolution than the other. However, these conditions also become ideal to identify DPFs. With changes in crystallization conditions, the protein behavior will also change and many a times, this can be a trigger for order to disorder or vice versa transformation of a DPF [313].

Therefore, DPFs can be identified by using different crystal structures of the same protein and then comparing them. D) Amino acid propensities were identified for ordered, disordered and DP fragments.

Smaller and hydrophilic amino acids like, Ser, Ala, Lys, Glu, and Gly were found abundant in disordered regions and deficient in ordered regions. Many polar and charged residues like, Asp, Thr, Gln, Pro, and Arg are found to have similar preferences for ordered and disordered regions. But these amino acids have higher propensities to be found in DPF regions as well. Ordered regions also some often occuring residues associated to them like, Iso, Phe, Typ, Tyr, His, Met, and Cys.

Of these, Thr, Arg, Gly, Asn, Pro, and Asp were found exclusively in DPF.

E) From a similar analysis of clustered amino acids based on their physicochemical properties, it was determined that disordered regions have affinity towards polar residues while DPF have affinity towards hydrophobic and charged residues. Moreover, it was found to be more likely that in 20% of cases a PTM site is to be found within 5 residues of DPF. Moreover, as can be seen in preceding sections that the study from 2007 does not detail in to the structural aspect of DPF and characterized mostly based on sequence features.

3b.2 Methods

3b.2.1 Dataset preparation

A redundant dataset of crystal structures were extracted from PDB (2016) hosted at www.rcsb.org [134]. Each chain in a pdb entry was treated separately as a single structure. Short chains having 8 residues or less were removed. Only crystal structures having resolution better than 2.5 Å and R-factor higher than 0.25 were selected. Thus, a total of 192163 structures (individual chains) were used for all analysis. If a given alignment has PB 'z' represented more than twice in between an alignment with gaps in one structure (zz-----z) but a defined region in the counter structure in the alignment (bcddddef). It was marked as a DPF. If the counter structure also lacks a defined region, then if will be classified as Disorder. The rest of the alignment where both the regions consists of well formed PBs and no 'z' is present, they were marked as Order. Figure 3b.2 represents a schematic example of the logic.

All the analyses were performed using python, R and bash scripts. Images were generated using R. Therefore, the DPF are analysed in a state of induced rigidity yet most of the secondary structures associated mostly with moibility are observed. This provides crucial insights into their structure and function relation.

3b.3.3.1 Protein Block distributions

Protein Blocks provide much closer approximations of protein backbone than regular secondary structures do [START_REF] Craveur | Protein flexibility in the light of structural alphabets[END_REF]. Therefore, it is fitting to compare protein block distributions for order and DPF regions. Figure 3b.7 shows the PB distribution of DPF (blue) and order (red) residues.

Both the DPF and order regions are highly populated with core of α-helical conformation as seen by abundance of m on both plots (Fig 3b. Counter-intuitively, the PBs g, h, i, and j (approximately loops) are less abundant in DPF than in order regions. Perhaps, protein blocks were able to resolve turns and bulges from the assigned state of coil (C) by DSSP and others.

3b.3.4 B-factors distributions

So far, in the preceding sections, indirect attempts have been made to understand flexibility by using secondary structures and PBs. However, a direct method for assessing flexibility is B-factors.

Since DPF are captured in their structured state, B-factors are available from X-ray data. Figure 3b.8 shows a linear comparison of normalized B-factor values for both DPF and order state. Both shows a similar gumbel curve differing in their maximum and minimum values. The B-factor values for DPF suggests relative rigidity when compared with those of the ordered state. This is indeed counter-intuitive and needs more verification. However, crystal structures are known to suffer from crystal contacts that badly alters the B-factors of a protein structures. Therefore, attempts are underway to generate short molecular dynamics for random set of 200 structures from both states having equal representation. This will help in bypassing the problems, if any, due to crystal packing effects and will provide a more robust analysis on flexibility of backbone. .9 shows the distribution of rSA for DPF (blue) and order (red) states. The two medians from the box plots suggest that DPF have more accessibility than ordered regions. Although the median of DPF lies at ~25%, the first quartile limit (Q1) is just above 10% which is at the border of accessibility and buried areas (< 7%). The interquartile range (IQR) of both the plots are comparable with DPF having an IQR of 28 while order have an IQR of 20. The Q1 of ordered region lies at 2% suggesting some deep buried regions, Figd 3b.9. The non-outlier data in the box and whiskers of DPF (blue) have much more surface accessibility compared to the non-outlier data of order. However, the outliers to the boxplot of ordered region shows intense clustering from ~55% to 75% thus suggesting some high accessible conformations. The outliers to the DPF boxplot are lesser in number yet they have higher rSA values (>80%). From these analyses it can be concluded that DPF have more rSA than order regions. This can be supported by the need to interact with multiple partners and solvent, since order can be triggered by environmental changes such as temperature, pH, etc. The lower Q1 and smaller range of order regions can be explained by the strong relation between rigidity and buryness of the regions in the structure.

3b.4 Conclusions and perspectives

Subsequent to the analysis of the effect of PTM on protein backbone, another unique event of protein life was analysed ㅡ Dual personality fragments. DPF are regions in a protein structure that can transform between disorder and order structural states. This makes them quite important as such structures support conceptualizing the structural continuum that suggests that structural states are more fluid than rigid. However, they are not very well characterized given that there has been only one systematic analysis on them, that too in 2007. The study by Zhang et al focuses on sequence characterization of DPF and that too based on identical structures alone. As DPF are essentially the disordered fragments that transit to structured state, surprisingly, their structural data remains unexploited. Therefore, it was decided to design a systematic analysis of DPF sequence and structural properties and comparison with those of order and disorder states. This could provide insights into the structure and function of DPFs and could also be suggestive of the structural properties of the otherwise denatured state.

As suggested by Zhang et al, that DP fragments differ from the disorder and order in their specific sequence composition. The DPF characteristic amino acid signature, as proposed by

Zhang et al. is, 'T,R,G,N,P, and D' [313]. However, there can a caveat in the analysis as the propensities they take into consideration are solely from the corresponding data-set and not from other studies. Therefore, while analyzing amino acid distribution for DPF, order and disorder previously known propensities for order and disorder were also considered. Instead of a suggestive sequence motif, the analysis proposed characterization by frequency. Such that, if a given region has high frequency of Cys, Gly, Asp, and Lys then it can be an indicative of a DPF region. The rationale behind such an approach is two fold. A) from the analysis, Cys, Gly, Asp, Lys turn out to be specifically high for DPF regions. Also, Asp is labelled as inconsistent in being either order or disorder promoting and Gly, Asp are also part of the proposed signature by Zhang et al. B) two of the residues, Cys and Gly are rigid and moderately flexible while rest two are highly flexible.

Also, Cys and Gly are hydrophobic while Asp and Lys are hydrophilic. Since, they differ in their properties, the probability of a region having high frequency of these four residues can be a reliable indicator of a DPF. Besides, the structural features of the region should also be considered. For instance, having a region with high occurrence of C, G, D, and K that has higher alpha helical and beta turn content can be a DPF. Such characterization can be used as a motivation to develop machine learning tools to predict DPF from sequences alone by using PSI-PRED or jPred.

The rSA analysis shows that DPF are much more accessible than ordered regions and fittingly so. DPF have been shown to contain the site of a PTM or located near a PTM site. They have also been proposed to be active regions in Molecular recognition features (MoRFs) and enzymes [327], both of which requires interactions with multiple partners. Therefore, functionally high flexibility and more solvent accessibility is beneficial for DPF. However, there have been certain ambiguities in the B-factor analysis for flexibility. These can be attributed to the crystal contacts due to packing defects. Therefore, a logical step is to randomly select ~200 structures from the dataset and perform short MD simulations to understand the role of flexibility in DPF.

Such an analysis is expected to provide much better perspective on the structural biology of DPF and may as well on the folding of protein structures.

Integrin αIIbβ3

The integrin αIIbβ3 is a fibrinogen receptor expressed at the platelet surface. It also binds to vWF in case of severe injuries. As evident from the nomenclature, it consists of an αIIb subunit noncovalently bound with β3 subunit. It is responsible for platelets aggregation, a key process in

Calf-2 domain

The last domain in the αIIb ectodomain extending from residues 744 to 959 (numbered as in PDBid 3FCS). Calf-2 is made of 10 consecutive β-strand connected by 11 loops as shown in Therefore, the prime objective will be to understand the role of inherent flexibility in the domains of Calf-1 and Calf-2. Additionally, to study the structural changes induced by GT and FNAIT mutations specific to these domains.

Methods

Structural data

The αIIb Calf- Some missing atoms in side chains of residues 667 and 668 of Calf-1 were completed using Modeller software v.9.14 [START_REF] Sali | Comparative protein modelling by satisfaction of spatial restraints[END_REF]. However, Calf-2 had important missing regions which posed a challenge. Two regions of length 11 residues (position: 763-775) and 34 residues (position: 840-873) were missing and therefore were difficult to model using classical modeller protocol.

Modeling the missing regions in Calf-2

Homologs for αIIbβ3 integrin (PBDID 3FCS) were searched in PDB database using blastp v2. 6.0 [401] that returned 8 structures. Sorting based on low e-value and high query coverage reveals two proteins having the highest percent identity of 38%. PBDids 4G1E and 3IJE were selected from primary results. Although both the αVβ3 integrin structures have the missing regions yet 4G1E is selected since it has a missing region of 17 residues compared to 34 residues in 3FCS and 28 residues in 3IJE. Therefore, selecting 4G1E will at least make the gap covered by half the length. Moreover, the Calf-2 domain of 4G1E is structurally closer to that of 3FCS with an RMSD of 0.72 Å and TM-score of 0.95.

Apart from sequence based homology search, structurally similar proteins were also sought after using FATCAT (Flexible structure AlignmenT by Chaining Aligned Frames Fragment Allow Twists) [345]. The top hit being a leukocyte specific receptor, αXβ2 integrin (PBDID 4NEH) having 24% identity. However, the Calf-2 domain in 4NEH is complete without any gaps, having a total length of 190 residues. Although the αXβ2 integrin is a distant homolog yet using two more related templates for modelling αIIbβ3 Calf-2 domain can be useful.

Selected mutations and their structural variants

Calf-1 variants- These are suggestive of the underlying importance of the region. The selection of these variants in Calf-2 is suggested by the collaborators from platelet lab in INTS.

The seven GT aa substitutions were introduced in the structures by in-silico mutagenesis using PyMOL software [352] and the SCWRL method [353]. The effects of all mutations were studied exclusively.

Molecular Dynamics

MD simulations were done using GROMACS 5.1.1 software [354] with Gromos96 54a7 forcefield [355]. WT and variant forms were soaked in a rhombic dodecahedral simulation box with TIP3P water molecules and neutralized with Cl-ions. The MD protocol is similar to the ones used in our previous works [255,341]. After 1 nsec of equilibration (with position restraints on the protein), each system was simulated through 10 independent dynamics for a total of 1 microsecond (10 × 100 nsec). Molecular conformations were saved every 100 psec for downstream analysis.

The first 5 nsec of each MD simulation were discarded considering the noise generated by residues at the extremities.

Trajectory analyses were done with the GROMACS software, in-house Python and R scripts. Root mean square deviations (RMSD) and root mean square fluctuations (RMSF) were calculated on Cα atoms only. Residues interactions were analysed using the online tool PIC (Protein Interactions Calculator) [356].

Two important computational resources were used for running MD simulations. Our inhouse super cluster, Serenity, having 48 compute nodes with 16 cores per node, thus generating a computational power of 768 cores. Also, CINES national supercomputer OCCIGEN was used under allocation no. A0010707621.

Trajectory analysis using Protein Blocks

Protein Blocks (PBs) are very efficient in tasks such as protein superimpositions [START_REF] Joseph | Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies[END_REF]and MD analyses [241]. They are labelled from a to p: PBs m and d can be roughly described as prototypes for core of α-helix and central β-strand, respectively. PBs a to c primarily represent βstrand N-ter and PBs e and f representing β-strand C-ter; PBs a to j are specific to coils; PBs k and l to α-helix N-ter while PBs n to p to α-helix C-ter. PB assignment was carried out using PBxplore tool developed by our team and freely available at GitHub (https://github.com/pierrepo/PBxplore) [START_REF] Barnoud | PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks[END_REF]. PB were assigned for each residue of the domains and over every snapshot extracted from MD simulations. The equivalent number of PBs (Neq) is a statistical measurement similar to entropy that represents the average number of PBs for a residue at a given position. For details on Neq, please refer Introduction 1.6.3.

To underline the main differences between the wild-type (WT) and a variant for each position, ΔNeq value is computed. ΔNeq is the absolute difference between corresponding Neq values. However, a same ΔNeq value can be obtained with different types of blocks in similar proportions. Therefore, to detect a significant change in PBs profile, a ΔPB value was calculated.

It corresponds to the absolute sum of the differences for each PB between the probabilities of a PB

x to be present in the WT and the variant forms (x goes from PB a to PB p). ΔPB is calculated as follows:

𝛥𝑃𝐵 = ∑ 16 𝑥-1 | 𝑓 𝑥 𝑊𝑇 -𝑓 𝑥 𝑣𝑎𝑟 |
where, 𝑓 𝑥 𝑊𝑇 and 𝑓 𝑥 𝑣𝑎𝑟 are the percentages of occurrence of a PB x in respectively the WT and the variant forms of Calf-1 structures. A value of 0 indicates perfect PBs identity between WT and variant, while a score of 2 indicates a total difference.

Results and Discussions

The rigid, anchor region of the αIIb subunit leg that comprises of two domains of Calf-1 and Calf-2 is under investigation for inherent flexibility. A core β sandwich fold consisting of 8 to 9

antiparallel β strands connected with loops. Usually, a β sandwich fold is found in anchoring roles in the structures, for eg, in heavy chains (VH) of antibodies [START_REF] Harrison | DNA Recognition By Proteins With The Helix-Turn-Helix Motif[END_REF], Flaf protein in Archeal cell envelope [START_REF] Marsh | Structure, dynamics, assembly, and evolution of protein complexes[END_REF] and therefore has more rigidity associated to it. Moreover, the inside-out as well as outside-in signaling primarily interacts with the β3 subunit. Therefore, it will be interesting to understand the role of inherent flexibility in the dynamics of these domains. Also, the dynamics of wild type Calf domains will be compared with that of different variants (structural) implicated in GT and FNAIT.

Completing the missing regions in Calf-2 domain

With huge gaps of 11 and 34 residues, it will not be possible to understand the dynamics of Calf- On average β-strands are more rigid than loops [START_REF] Cowtan | Phase Problem in X-ray Crystallography, and Its Solution[END_REF][START_REF] Hauptman | The Phase Problem of X-ray Crystallography: Overview[END_REF] although some of their residues represent relatively high B-factor values in Calf 1. As it is known that B-factors are strongly influenced by the crystal packing of the structure [START_REF] Carugo | Protein-protein crystal-packing contacts[END_REF] therefore, it was checked and B-factors are confirmed to be not influenced by crystal packing contacts. loop1 (size: 9, positions 603-611), loop 2 (size: 10, positions 620-629), loop 3 (size: 7, positions 640-646), loop 4(size: 4, positions 653-656), loop 5 (size: 8, positions 665-672), loop 6 (size: 6, positions 678-683), loop 7 (size:6, positions 690-695), loop 8 (size: 8, positions 708-715), loop 9 (size: 11, positions 725-735), and loop 10 that begins at position 742.

Comparisons of dynamics between GT variants and WT Calf-1

The αIIbβ3 integrin was cut into compact structural domains through Protein Peeling [358] that correlate the delineations found in literature [344]. As shown in Figure 4.10, the variant residues under investigation are mostly located at β-strands presenting low flexibility with the exception of residue 653 localized near the β-strand 3 C-ter. Similar to WT system, the 7 variants (structural mutants) were studied with 10 independent MD simulations performed to a complete timing of 1 μsec and with parameters similar to Jallu et al., 2014 [341].

Protein block analysis of WT and variant dynamics

To resolve deformed region from rigid and flexible regions, PBs analyses of the MD trajectories is performed. PBs analyses revealed striking local structure alterations, but distant from the variant sites. Three variants R724Q, L653R and C674R are found to be representative of all behaviors observed for the 7 variants.

R724Q

This aa variation is located at β-strand number 8. In regards to the WT structure (Fig 4 .12), the highest Neq differences are at S621 (beginning of loop 2), A644 (loop 3) and L710 (loop 8).

These loops that are naturally flexible are even more so in the variant. Therefore, an increase in flexibility is observer. Conversely, residues L624 to D628 have a lower Neq value thus indicating that loop 2 represents a dual behavior, with increased deformability at its beginning and enhanced stability in its C-ter part. Surprisingly, the mutant residue Q724 (β-strand 8) conserved the same Regarding the structure, the polar amino acid arginine contains a longer aliphatic sidechain than glutamine, an uncharged hydrophilic polar amino acid. Q724 conserves the backbonebackbone interaction with E648 as observed with R724 (β-strand 3, see Fig 4 .12E). Besides, Q724 lost the ionic bond and the side chain -side chain interactions with E648 but made new hydrogen bonds through side chains interactions with E722. This showcase a classic example of structural compensation that maintained the local conformation of the residue through different interactions.

The highest ΔNeq (2.71) that is also associated with the highest ΔPB (0.57), is observed for S621 Hydrogen interactions with T682 and K677 are retained but the backbone -backbone interaction with E681 is lost and replaced by side chain and ionic side chain interactions with R724 in loop 9. In the variant structure, this region has high fluctuations in PBs, mainly associated to loops that even affected the C-ter of the β-strand 5 located the above loop 9.

C674R

This variant is associated with a C674R substitution in β-strand 5. An Neq profile (Fig

4.
14A) similar to that occurring in the R724Q substitution is observed (see section 4.3.5.2). Loop 2 presented the same increased deformation at its beginning (S621), followed by a stiffening in its centre (residues L624-D628). The same PB series "ehiac" (L624 -D628) is found in greater proportion than in the Q724 and R674 variants, reinforcing the local stiffening of the loop in this region. The main destabilization was far upstream of residue 674 (Fig 4 .14C).

Loss of a disulfide bridge:

With the C674R substitution, the residue 674 not just lost its covalent disulfide bond with C687 located at the end of β-strand 6, but also its aromatic interaction with Y659 in β-strand 4 (Fig 4 .14D). However, the mutated R674 made an ionic bond with E688 located at end of β-strand 8 that strengthened a backbonebackbone interaction. The 80% frequency of PB d (the highest) in WT decreased to 49% in the variant. Surprisingly, N675 and Q676 located downstream the substitution remained structurally stable with similar PB occurrences.

The highest Neq variation affected R671 as shown by the strongest ΔNeq (5.02) and ΔPB (0.91). The side chain of R671 is mainly exposed at the domain surface and forms a single ionic interaction with the neighboring E670, like in WT. But in the variant conformation, the R671 sidechain can occasionally turn towards loop 8 to make ionic side chain interactions with E688 (Fig

4.14E

). The frequency of PB d (the highest) drastically decreased in the variant leading to an increased disorganization of the neighborhood.

Experimentally, the C674R mutation severely impaired the αIIbβ3 complex expression with only 10% of the integrin expressed at the surface of the patient's platelets and transiently transfected CHO cells. However, the C674R mutation did not impair pro-αIIb synthesis but affect the stability of the complex that is not correctly matured and/or expressed at the cell membrane. In the remaining 5 variants studied, compensation mechanisms were also observed. Most interactions formed by WT residues are replaced by new ones, allowing conservation of the local structure. Surprisingly, regions displaying significant changes (high ΔPB) are distant from substitution sites without any contact/interaction with the substituted aa. These regions contribute towards increasing the deformability and are usually located at interfaces adjacent to neighborhood β-propeller, Calf-2 or Thigh domains. These results depict changes resulting from substitutions in distant regions suggesting long-range mechanism to be at play.

Different variants with common mutation sites. L721R and L721V showed quite different results (Fig 4.15). Compared to L721R, the L721V substitution had very little impact on RMSF, apart for the end of loop 8, which is a highly flexible region. This is particularly true for E712 (loop 8), whose ΔNeq were respectively, 3.33 and 0.

Conclusions and future perspectives

The MD simulations of Calf-1 domain allowed to demonstrate more or less pronounced structural changes in the wild type structure as well as the impact of GT variants. The analysis gets huge enhancement by using protein blocks statistical measures like ΔNeq and ΔPB. These helped in closely evaluating the regions that comprised of a local regions of rigidity inside otherwise deformable regions, for instance as analysed in case of Glu 667, Fig 4 .10. Flexibility profile of the Calf domains showed that although their anchoring role demands them to have a rigid core yet the connecting loops contribute to the structural dynamics of the core. This principle gets even more profound by studying the effect of GT variants on the Calf-1 structure. Overall in the structural mutants, the beta-strand core tends to maintain or regain rigidity which can be attributed to its structural role in the big integrin complex. However, the impact of GT variants that may disturb the core are systematically compensated by the loops. The energy gain or loss due to lost interactions in mutants is shown to be compensated by new interactions and the residual energy is apparently transferred to the loops. This causes the long range effects of the impact of mutation, as observed at residues L653, L721, and R724.

While, mutation at C674 and P741 variants displayed conformational changes at the mutated site, predominantly. In the case of the C674R substitution, the resulting loss of the disulfide linkage relaxes the structure and introduces significant structural alterations (Fig 4.14 and 4.13). Such an effect is largely suggestive that the structural-functional context of the structure influences the rigidity. Thus, inherent flexibility is important and crucial to the conservation of the core.

For P741R it should be noted that residue 741 is located at two residues upstream from the C-term of Calf-1 and is normally in contact with the Calf-2 domain. Thus, the absence of the neighboring domain in Calf-1 MD simulations can impact the observations. To resolve, MD simulations of the complete domains have been performed. However, the technical failure of our computational cluster inhibits the inclusion of results from Calf-2, Calf-1 + Calf2 + knee + Thigh domains. Nonetheless, similar observations were also made in the dynamics of these domains as well. The calcium containing domain, 'Genu' (knee) seems to play a key role in assisting the flexible domain to be stabilized during structural changes from leg to thigh regions.

Although the primary objective was to profile the inherent flexibility in all-beta, rigid Calf domains yet the evaluation of the dynamics of GT variants enhanced our understanding of local structure dynamics. With deservingly expected developments in the project, it will be interesting to compare and test the inferences from leg region with apparently flexible domains of β3 subunit.

Chapter 5: Protein dynamics in structural assemblies-An affair of ACKR1 and

Plasmodium vivax

Introduction

As have been seen from subsequent chapters that local protein structures are context dependent.

For instance, in chapter 3, differences in dynamics of secondary structures can be observed between PTM and DPF dependent contexts. Moreover, in chapter 4 the dynamics of backbone is preserved, although key residues lying in beta-strands are mutated. Both the Calf domains in Integrin αIIbβ3 have a structural role of anchoring the chain to the cytoplasmic membrane.

Therefore, the ambitious mutation in the core of the domain had compensatory effects to preserve its function. Thus the objective is to understand protein flexibility in a more complex structural organization that is, a multimeric assembly. Also, in the preceding chapters the domains and local structures under investigation can be encapsulated as having a globular nature. Therefore, to add contrast to the structural contexts studied so far, a transmembrane protein is selected as a case study to understand protein dynamics in structural assemblies.

The selected protein is Duffy Antigen / Chemokine Receptor (DARC). DARC has a physiologically promiscuous behavior in humans while being corruptly implicated in Malaria.

DARC is a transmembrane GPCR and thus expectedly have scarce information about its structure.

Therefore, the primary challenge will be to generate a robust structural model and consequently investigate the dynamics of its important structural regions. Being a GPCR implicated in a pathology like Malaria, its structural dynamics can be exploited to design effective inhibitors for Malarial transmission.

Malaria

Four Plasmodium species Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, and Plasmodium malariae, are the cause of malaria in Homo sapiens sapiens while a simian parasite Plasmodium knowlesi may also be able to infect [360]. Of these Plasmodium falciparum malaria mostly lead to fatalities while the rest leads to milder yet recurring and severe infections. Although, Plasmodium vivax infections are not as fatal as Plasmodium falciparum, yet it is the most widespread malaria causing species in Asia, Europe and Americas [361,362][363] as shown in the The sexual part of the life-cycle is carried out in vectors, mostly mosquito. The infectious stage is executed in the host cells, mostly Humans. After a blood meal the parasite is transferred to the host via salivary glands. The parasite multiplies in the hepatic cells but is asymptomatic and therefore difficult to diagnose malaria. In 2-3 weeks, the parasites rupture the hepatic cells to enter blood stream where they infect Erythrocytes. The erythrocyte infection leads to malarial symptoms. The parasite cells multiply in the erythrocytes and spread over and thence when the vector takes another blood meal the parasite enters the blood stream of vector to carry on the sexual cycle. A detailed description of such residues is provided in the subsequent sections in lieu of a fitter context. The loss of the ionic interaction is compensated by new interactions with the well conserved Tyr residue of the following intracellular loop-ICD3 and Gα subunit of the G-proteins [381].

Therefore, the Arg works as a molecular switch and DRY motif is crucial for G-protein mediated signaling upon activation. The Atypical Chemokine Receptor (ACKR) contain variations in this DRYLAIV motif and therefore cannot transduce signal after binding to a chemokine. Some ACKRs for eg, D6 (ACKR2), CXCR7 (ACKR3), (GPR35) contains the motif with some variation and are able to transduce signal by independent pathways [382,383]. The DRY motif is completely absent in ACKR1 and therefore it cannot couple with G-proteins and thus no signal transduction is observed, see Fig 5 .4B. Besides, ACKR1 is also the only member in chemokine receptor family that binds non-specifically to chemokines. It binds to inflammatory chemokines of both types: CXCL as well as CCL. ACKR1 have been reported to interact with: CXCL1, CXCL8, CCL2 and CCL5 in erythrocytes. This makes ACKR1 behavior to be an 'atypical' one amongst the subfamily of atypical chemokine receptors. Among chemokine receptors, surprisingly less information is available about ACKR1 while it is the oldest known chemokine receptor receptors is believed to be influenced by the class of the chemokines involved. Since, DARC binds to both classes of chemokine ligands with similar affinities, it poses a challenge to identify a correct template for comparative modelling. Consequently, three questions emerge: 1) Does DARC exist as a monomer or an oligomer? 2) If as oligomer, then is it a heteromer or homomer? 3) Which of the chemokine receptor(s) can be effectively used as a template(s) structure? Fortunately, in 2010 first structure of chemokine receptor was solved and released as a series of 4 structures with different antagonist ligands [394][395][396][397]. All the structures were crystallized as homodimers thus diminishing the alleged role of crystal packing. This was followed by a series of NMR and mutagenesis studies which assesses most CXCRs and DARC as a dimer [377]. Chakera and collegues in 2008 [398], had shown that DARC exists as a heteromer in cells proposing a CCR5/DARC complex. While these evidence were supporting DARC to be modelled as a dimer rather than the previous monomeric model. The question on homo-or heteromeric state was still unanswered because the heterodimeric studies were based on case specific analysis. DARC dimerizes with CCR5 to restrict the conformational changes in CCR5 that favors chemotaxis, thus acting as a trans-inhibitory partner [398]. DARC's physiological state on erythrocytes as well as during Plasmodium vivax contact was still not clear.

In 2014 Batchelor et al. [389], crystallized the DARC-PvDBL interaction and showed the assembly to be a heterotetrameric and heterotrimeric. In both the structures, homodimer of PvDBL The selection of an effective template structure still remained at large. To decide whether to use a CXCR or CCR structure or both as a template, a crude phylogenetic approach was adopted.

An understanding of the phylogenetic placement of DARC with other human chemokine receptors should provide sophisticated support in selection of a structural template. Therefore, a phylogenetic tree was plotted using the sequences available for 21 chemokine receptors [399,400].

Based on the tree topology and branch lengths, it is observed that DARC is highly distant from the rest of the clades while CXCR4 is suggested as a potent structural template for modelling DARC.

Methods

Structural template selection

Template search

The sequence of the ACKR1 or DARC was extracted from UniProtKB/Swiss-Prot database (accession number: Q16570). The sequence corresponding to the isoform 2 was selected as it has been annotated as the physiological form. The sequence so obtained was used as a query in blastp v2.6.0+ [401] as well as pHMMER v3.1b2 [402] to search against PDB database. For blast, amino acid substitution matrix BLOSUM62 [403] was used along with word_size: 6, window_size: 40.

The composition based scoring adjustments (comp_based_stats) were used with an e value threshold of 1e -5 . pHMMER was run with default values using BLOSUM62 substitution matrix.

e-value restrictions for domains, -domE and -incdomE, were used to focus the search on the 7TM domains and thus remove false positives.

The blastp search extracted only two hits, pertaining to ECD1 of ACKR1 in PDB structures: ids 4NUV and 4NUU. These PBD represent the heterotetrameric and heterotrimeric assembly of PvDBL and DARC, respectively [389]. pHMMER also has these two as top hits and since -domE option was used; other TM proteins were also hit. However, a conclusive result was not obtained. Although, the matched subjects have >90% identity but they had critically less query coverage (< 8%). This noise in the results was created by the relatively long ECD1 of DARC.

Therefore, the query sequence was re-submitted after clipping the first 50 residues. Using the same parameters as before, with blastp and pHMMER; the query fetched significant number of hits containing chemokine receptors as well as other GPCR structures too. It is noteworthy, that only 50 residues instead of 60 residues of ECD1 were clipped in order to maintain the effect of ECD1 in the structure. The results were scanned and resubmitted to PSI-blast (after blastp) and JACKHMMER (after pHMMER). The iterations were stopped after third run (including the first run) with the final set containing 15 pdbs. An attempt to enrich the potential template dataset was made using FATCAT [345] at default configuration. The structure search method did not find any new hits that could be added to the 15 hits found by the sequence search methods.

Template selection

Of the selected 15 PBDids, 5 were removed given the missing residues in their structures (based on REMARK 465 of PDB file), higher R-free value and lower resolution (> 3Å). The final set of potential templates contained 10 protein structures representing CXCR4, CCR5, CXCR2

and a viral GPCR protein. The PBDids for these structures are: CXCR4: {3ODU, 3OE6, 3OE8, 3OE9, 4RWS}, CXCR2: {4JL7, 4N6X}, CCR5 {4MBS}, vGPCR: {4XT1, 4XT3}. To verify their structural integrity, a conformational space analysis was performed using a three layered structural analysis, which was called a Three Tier Method (TTM). The TTM comprises of three simple metrics namely, TM-score [404], RMSD (root mean square deviation) and alignment coverage.

All the structures were first isolated into individual chains and heteroatoms were removed.

The boundaries of TM-helices of each chain were identified and were pair-fitted using ProFit Version 3.1 [405]. This step validated the structural integrity of the GPCRs. After this preliminary analysis, TM-align [404] was used to perform all v/s all pairwise alignments of individual chains, as TM-align is length independent. The statistics extracted from the TM-align were used in the TTM. Table 5.1, shows the output of the TTM with a comparison of all vs all chains. The use of PDBs 3OE6, 3OE8, 3OE9 in the analysis acted as a positive control for the TTM. The first section analyse the TM-score, which depends on the reference structure selected during alignment. Higher the TM-score, more related are the structures. Second section establishes structural relatedness using RMSD: lower the value, closer are the structures. The second section acts as a validation for the first section. In the third section, alignment lengths or coverage was used to assess the structural comparison.

These steps along with the preliminary analysis done using ProFit proved crucial in identification and exclusion of the outliers like, 4JL7, 4N6X, and parts of 4RWS and 4XT3. The TTM helped in narrowing the templates from 10 to 2. The selected templates were 3ODU (CXCR4) and 4MBS (CCR5). 3ODU exists as a dimer in the asymmetric unit while 4MBS contains a monomer in its asymmetric unit (based on REMARK 290 and 350 of their PDB files).

Phylogenetic Analysis for template selection

To select between a CXCR and a CCR template to model DARC was a challenge, given that DARC binds to both classes of chemokine ligands. Therefore, phylogenetic information of chemokine receptors was used in the selection process. Sequences of 21 chemokine receptors out of 24 known Human chemokine receptors were selected. 3 receptors, namely CX3CR1, XCR1, and ACKR6 were excluded as they do not bind to the canonical chemokine ligands [399,400].

Multiple alignment of these 21 sequences was performed using MAFFT v7. 2 [406], with default parameters. The multiple sequence alignment (MSA), in Stockholm format, was annotated with the helical boundary information of 7TM helices. Sequence editor, JalView v16 [407] was used to refine the MSA based on the conservation of cysteine residues and DRY motif.

Tree generation and visualization

The resulting MSA was submitted to IQ-Tree v1. 4.2 [408]-an efficient tree reconstruction algorithm based on maximum likelihood. A phylogram was generated using JTT exchange rate matrix having free rate heterogeneity and empirical frequencies from the data: (JTT+F+R10). The tree was bootstrapped at 1000 exchanges. The resulting phylogram was visualized using iterative Tree of Life (iTOL) [409]. For details on the tree building parameters, see section 6.2.4.

Structural Modelling

Sequence Alignment

The sequence of the template structure and ACKR1 (without the ECD1) was aligned using Promals3D [410]. In addition to the global alignment of sequences, Promals3D also takes into account the secondary structure elements (SSE) of template and the predicted sse using PSIpred [START_REF] Mcguffin | The PSIPRED protein structure prediction server[END_REF]. Therefore, it helped in easy visualization of the alignment in the TM regions. However, the global pairwise alignment was also annotated with the assigned SSE of template using DSSP. It was made sure that there were no critical in/dels in the helical regions. The final alignment was saved in PIR format in the alignment.ali file. The alignment had the complete sequence of DARC minus the 49 residues at N-terminal, namely ECD1.

Structural Modelling and assessment

Modeller v9.16 [START_REF] Webb | Comparative Protein Structure Modeling Using MODELLER[END_REF] was used to generate structural models using the template and the alignment file. Modeller script, model-default.py was customized by adding snippets to define spatial restraints and different model assessment scores. Two constraints were added; the transmembrane boundaries and two disulfide bonds. Three assessment scores were given to assess the best model among the 100 models generated. Apart from DOPE/molpdf [START_REF] Webb | Comparative Protein Structure Modeling Using MODELLER[END_REF]411], and GA341

(model reliability), normalized DOPE score (nDOPE) was also calculated in order to compare models from different templates. For instance, to compare model quality generated from a monomeric template and a dimeric template because DOPE works only with single chains.

Since, the query is a TM protein and assessment scores like DOPE/molpdf, GA341 are optimized for globular proteins, it was required to assess the models differently. Therefore, MAIDEN was used [412]. Model quality Assessment for Intramembrane Domains using an ENergy criterion-MAIDEN is a statistical potential optimized using structural information of membrane proteins from PDBTM (xml file) [413]. The energy potential of TM proteins is supplied as an 'intp' file with option '-e'. These potentials are also used to calculate an approximation of the free energy which is given as the raw potential in the output. MAIDEN uses sequence based decoys to calculate Z-scores, similar in principle to e-value calculations. An important feature of MAIDEN is that it uses globular potentials for calculations of the extra-membrane portions.

Further, a conformational space for the models were sampled using TM-score of 101*101 pairwise alignment of all models against all models (100) and the template (1). This step helped in understanding the expanse of models from the template as well as among themselves.

A top twenty approach or "T20 test" was devised to select the best model. Top twenty models were selected from each of the scoring functions. An intersection of the 4 sets, each containing 20 models was calculated. The set can be represented as (MAIDEN ∩ (GA341 ∩ (nDOPE ∩ DOPE))), where the set names represent the 20 best models according to the scoring function. Since, only one of the scores is optimized for TM proteins, MAIDEN set was given preference over nDOPE, DOPE and GA341 sets. For instance, if a model appears lower in T20 of nDOPE and DOPE/molpdf but ranks as the best model according to MAIDEN score, that model will be selected. Thus a MAIDEN score is weighted more than the rest of the assessment scores.

This information is then clubbed with the TM-score analysis and a best model is selected. In summary, the best model should be ranked amongst the top 20 models in each scoring scheme and should be a representative of the collective structural space of template and models.

Membrane Building

The structural model should be embedded in a lipid bilayer. This step is important for the analysis of the pathophysiological role of DARC. Therefore, the first step was to resolve the protonation states of asp, lys, glu and his residues at the physiological pH 7.2, which was done using proPka

[414] and PDB2PQR server [415]. The dimeric model was then submitted to PPM server [414,416] to estimate the extent of the lipid bilayer. PPM server was used after specifying that the N-term of the model lies on the extracellular side. The concentration, type of lipids, and distribution ratio was calculated strictly to satisfy these findings from literature. Therefore, the selection of heterogeneous lipids in step 02 of Membrane builder was the bottleneck of the protocol. Out of 182 different lipids, POPC, POPE, POPS, POPI and PSM were chosen. Since all of these are derivatives of the same acyl chain, Palmitoyl Oleoyland therefore offer consistency in acyl unsaturation and chain lengths. Therefore, the effect of acyl chain unsaturation on membrane fluidity can be effectively normalized. Further, these lipids are added in definitive ratios. Cholesterol (CHL1) is added in equal amount at 25:25 (outer: inner) satisfying the ratio 1:1.

Results and Discussion

Our interest in studying the structure of ACKR1 (previously named DARC) is two fold. Primarily we wanted to understand the inherent dynamics of a protein structure in a multimeric assembly; a homodimer in this case. Secondly, DARC is rather an interesting protein that does not transduces signal, unlike other chemokine receptors and plays a crucial role in pathophysiology of P.vivax malaria. Absence of a robust structure for DARC makes it challenging to understand its structural biology as well as its interaction with the PvDBP. Therefore, it becomes elementary to model the structure of DARC so as to achieve our objectives.

Phylogenetics based selection of the template

As explained in the methods section of this chapter, to enhance the template selection procedure, The intersection set contained models numbered: m33, m59, and m85. m85 has the least energy in MAIDEN calculations and ranked 16th in the nDOPE set. However, it appears last in the GA341 set. m33 tops the nDOPE, DOPE and GA341 sets but ranks 9th in MAIDEN scores. m59 like m85 ranks 2nd in MAIDEN set but appears in the last quarter of nDOPE, DOPE and GA341 sets. Since, both m59 and m85 are ranked higher in the MAIDEN set they were weighted higher. Of these two, GA341 score becomes a 'decider', with m85 having a score of 0.15 in contrast to higher score of 0.21 for m59. GA341 is a score to estimate the accuracy of the model using percent sequence identity between individual chains of model and template. An ideal model would be scored 1.00 thus having 100% sequence identity with all the template chains. However, our approach is to find a divergent structure from the CXCR4 template and thus m85 is selected on this argument. The RMSD between m85 and m59 is 0.56Å while structural deviation from template is 2.14Å and 2.17Å for m85 and m59 respectively. The deviation from template structure is also quantified by average TM-score (mean of chain-wise scores) as 0.812 and 0.815 for m85 and m59 respectively.

Structural orientation in the bilayer

Modeller protocol does not take into account the effect of solvent on the structural model.

The conformations may change with solvent properties, especially in the case of TM protein structure; wherein some region interacts in a hydrophobic environment while the rest resides in a hydrophilic environment. DARC is a TM protein that is expressed on the RBC membranes and therefore it is required to embed the obtained structural model in a lipid bilayer. However, before building the membrane, it is essential to mark the boundaries of the TM regions and estimate the orientation of the TM helices. Therefore, PPM web server was used [416]. The PPM protocol that uses the alignment of protein structure's z-axis with the normal to the bilayer and minimization of the transfer free energies of the embedded amino acid residues, provided a membrane orientation for the homodimeric model of DARC. The assembly have a ΔG for transfer energies as 107.9

kcal/mol. The thickness of the hydrophobic bilayer is estimated as 30.6 ± 0.8Å which matches with the average bilayer depth of TM proteins. Moreover, PPM server also reports a tilt in the TM helices at 4. ± 2°. The TM boundaries are also provided for the seven helices. These results provide some elementary understanding of the structure of the DARC homodimer.

Comparison with the old computational model of DARC

As it is mentioned in section 5.1.4 that our lab has generated a homology model of DARC in 2005 therefore it would be logical to compare the old model and the new. Although, there are robust reasons to believe that the two structural models are not fittingly comparable. In 2005, the protein data bank had only a very few crystal structure of 7TM fold and even rare would have been to find a crystal structure for Class A GPCR. This can be put into context by acknowledging that the first crystal structure of chemokine receptor was published in 2010. Therefore, the comparison of the two structural models is merely a conventional exercise.

The structural model from 2005 (for reference will be called, DARCold hereafter) is modelled based on a bovine Rhodopsin GPCR template. The bovine rhodopsin (PBDid 1F88:A) had a sequence identity of less than 20% in the TM regions. According to [START_REF] De Brevern | A structural model of a seven-transmembrane helix receptor: the Duffy antigen/receptor for chemokine (DARC)[END_REF] an ensemble of structural models were generated but only two were selected as they followed the spatial restraints [241]. The major difference is that both the models are monomeric. And as during the current study, a lot of time was invested in finding our the correct oligomeric state of DARC in erythrocytes. An explanation for the monomeric model could be that the Rhodopsin is a nonerythroid protein and therefore DARCold might represent the structural model of DARC from vascular lineage (Fy a ). DARC is known to express as monomer in epithelial cells. Therefore, in order to compare the current model with DARCold, one subunit of the dimer have to used. The RMSD between two subunits of DARC dimer is 0.12Å. Figure 5.9 shows the structural superposition between the old models and single subunit of the current dimeric model. Since, the DARCold was modelled as a whole structure therefore, both the models consist of ECD1 which causes a lot of noise in comparing the TM-scores and RMSD. Therefore, alignment was done using current model as the reference and ECD1 from DARCold was ignored. The RMSDs between the DARCold and current monomer is 6.25Å and 5.84Å with the ECD1. The TM-scores among the two DARCold models and current model are 0.73 and 0.76 depicting that the TM domains belong to the family. However, upon a quick observation of Figure 5.9, it can be seen that the TM helices of DARCold does not show a well defined outward tilting at their N-terminal face. Therefore, the major deviation in TM-region among models can be seen at the N-terminal face of TM-helices.

Another striking difference among models is that the old models does not have the disulfide bridges among conserved cysteines. This can also explain the less defined to absent outward tilting of DARCold. However, the TM-score among the old models is 0.82 and they have an RMSD of 2.63Å. The gray color of the ECD1 and ICD4 signifies that these were not included in the alignment using TM-align. B) Superposed structural models of ACKR1 monomer (green) and more extended of the DARCold models. The loops of ECD1 are not not shown here since they were not used in the alignment. These loops can be seen in C) forming an anti-parallel beta sheet (in blue). In C) the helices orientation can be appreciated. In A and B, the deviation of yellow and blue model from green model is mostly inward and therefore, they do not have a well defined outward tilting. The cysteine residues are shown in Red.

Generation of a mimic RBC bilayer

While the effect of a lipid membrane on the structure can be estimated using a lipid membrane embedded structure of DARC using CHARMM-GUI (see methods), the bottleneck is the choice of lipids in the membrane. Since, the second objective of the project is to apprehend the DARC -PvDBL interactions, the type of membrane is an important factor. The membrane builder from CHARMM-GUI is a sophisticated software but like every other computational tool, the biological significance of its result depends on the given inputs. Therefore, it was important to generate an in-silico mimic of a physiological RBC membrane. However, it becomes really complicated because the reticulocytes (young RBC) are slightly different from erythrocytes (mature RBC)

[424]. Unfortunately, there are no standardized concentration of lipids available for reticulocytes but some RBC centered research articles mention lipid distribution among cytosolic and extracellular halves of the lipid membrane [424,426,428]. Such articles were mined (details in Methods) and an estimate of the lipid concentrations and distributions amongst the two leaflets of reticulocyte membrane was proposed.

After estimating the orientation of the structural model and estimating the reticulocyte membrane composition, it was subjected to the membrane building protocol at CHARMM-GUI web-server. The structure was inspected manually for the TM boundaries, helix orientations and disulfide bridges, at each step of the six-step long protocol. After the first step, the oriented structure showed a beta strand in ECD3 between TM helix 4 and 5 while the input structure has a β-hairpin loop. It was found that the appearance of β-strand is due to the difference in the visualization platforms as the coordinates of both the files were same. In step 2, after verifying the cross-sectional areas, heterogeneous lipids were added with the system having a water crosssection of 10 Å (height) on both sides of the bilayer. The system size was calculated based on the number of lipids added to the system. A total of 121 lipids were added to the upper leaflet while 110 lipids were added to the lower leaflet. This is to maintain the asymmetry of the reticulocyte membrane. The final structural model was assembled with the generated membrane using replacement method (Fig 5.10). The resulting dimeric structure is minimized for removal of bad contacts, especially between cholesterol ring and the lipid tails, using PME and SHAKE algorithm in Gromacs [429].

Structural interface

The interface of the homodimer membrane system is calculated using PIC web server [356] and ViP approach. ViP is developed by our collegue, Dr. Jeremy Esque based on his work on VLDP to describe structures using Voronoi and Laguerre tessalations. ViP has been standardized

for membrane bound interfaces and therefore provides confidence to the calculations from PIC.

The interface interactions are observed to be predominantly hydrophobic in nature with few important polar interactions. Residues from TM5 and TM 6 of both the monomeric units forms majority of the interface. However, a smaller interface lying closer to the cytosolic leaflet of the bilayer is also observed. However, ICD2 also have charged interaction (Arg) forming a smaller interface at intracellular face.

Conclusions and future perspectives

ACRK1 is the oldest known among chemokine receptors. However, it was identified as a blood group antigen system in 1950 and called 'Duffy' in reference to the patient in whose sera, the antigen was found. Therefore it was called Duffy Antigen [431]. It would be decades later in that Duffy antigen would be found to have structural properties of a chemokine receptor and acts as a receptor for malarial parasite, P.vivax [432]. Since 1993, there have been numerous studies on Duffy antigen's role as a P.vivax receptor [433-435] but a few discusses about its role as a chemokine receptor [366,430,436]. Later in 1998, the contribution of Duffy antigen towards chemokine system would be identified as a receptor of chemokine ligands belonging to different classes [437]. Thus Duffy antigen was renamed as Duffy Antigen Chemokine Receptor (DARC).

DARC is identified as a mammalian chemokine receptor that can bind to inflammatory chemokines across classes. Besides able to bind effectively to different chemokines, it does not transduce the signal as it lacks the motifs that couple with G-proteins. Therefore, in International Union for Pharmacology (IUPHAR), updated the nomenclature and replaced DARC with Atypical Chemokine Receptor 1 (ACKR1) [384]. Among the atypical chemokine receptors, ACKR1 is the only one that exhibit promiscuous binding with chemokines and lacks the DRYLAIV motif completely. Also, ACKR1 serves as a receptor for Plasmodium vivax merozoites leading to the symptomatic infectious stage of malaria.

These makes ACKR1 an important protein from physiological, pathological and evolutionary perspectives. Still, there is none to scarce information about the structure of ACRK1 except for the residues 19-30 in its N-terminal extracellular domain. Therefore, we decided to build a structural model for ACKR1 integrating the physiological, pathological and evolutionary information available about ACKR1. The physiological and pathological properties of chemokine receptors assisted in identifying the key residues. Structural information from other chemokine receptors would provide the basic scaffold for modeling ACKR1. However, chemokine receptors exist in various oligomeric states and therefore it was challenging to decide the oligomeric state as well as homo-or hetero-composition of ACKR1. These questions were addressed with the information gathered on oligomerization of chemokine receptors and phylogenetic analysis of human chemokine receptors. Thus, ACKR1 was decided to be modelled as homodimer based on its closest homolog (with available structure), CXCR4. The modelling procedure was provided with the knowledge about the transmembrane boundaries and disulfide bridges. Since, the structure under consideration is a membrane protein caution was taken in selecting the best structural model.

One such approach is to use T20 Test, where sets containing 20 top ranked models from different assessment scores are intersected to obtain best model(s). The generated model is validated for conservation of important residues and structural features. Moreover, comparison with 12 years old model show important difference underlying the needed to propose novel one. As the structure under study is a membrane protein, the structural model is embedded in an in-silico membrane system. Given that ACKR1 expressed on reticulocytes acts as the receptor for P.vivax, the embedded membrane system mimics the real RBC membrane composition. The interfacial residues are identified from the dimer and they are in accordance to the physiological data available for chemokine receptors. This enhances confidence in the structural model of ACKR1.

In terms of understanding the behavior of local structural flexibility, we notched up to a more complex structural organization with a dimer formation in a phospholipid membrane system.

The primary objective is to understand the dynamics of local secondary structures and protein blocks at the interface region as well as at the sites of conserved motifs, like ICD2. Thereafter, a perturbation response study of key residues in the dynamic local structures can help us understand the role of allostery in the 7TM structure of ACKR1. Therefore, conclusive remarks on the dynamics of the local structures in the homodimeric, membrane embedded, assembly of ACKR1 will require all atom molecular dynamics. However, given the enormous size of the system the computational cost is expensive. Therefore, while the 1 microsecond range simulations are running on the cluster, a primary study of the motions using ANM based normal mode analysis (NMA) is designed. The NMA of the ACKR1 dimeric model will also be used for perturbation studies.

The impact of this study will be two-fold. Besides understanding the role of structural flexibility in a membrane protein assembly can reveal insights about the behavioral changes in local structures depending upon the context. While, a molecular model of P.vivax DARC Binding

Ligand region II (PvDBL-RII) is under process, the molecular modelling and dynamics protocol designed for ACKR1 can be directly applied to PvDBL-RII. The docking pose of ACKR1 dimer and PvDBL-RII is already known from the PBDID 4NUV. Therefore, molecular dynamics of the complex with an RBC mimic membrane is expected to be impactful towards identifying key residues in the complex and targeting them to inhibit P.vivax binding to DARC in reticulocytes.

Our new colleague Dr. Agata Kranjc Pietrucci has already started working towards modelling the PvDBL and ECD1 of ACRK1.

Chapter 6: An evolutionary perspective on Chemokine Receptors.

Introduction:

As discussed in chapter 5, the template selection protocol for modelling ACKR1, was supported [442,443].

Therefore, a comprehensive phylogenetic analysis of chemokine receptors is designed to understand some, if not all, peculiarities of chemokine receptors.

Chemokines:

Chemokines are an abbreviated form of chemotactic cytokines and are defined as the cytokines that induce chemotaxis by binding to GPCRs. They are involved in major immunological and homeostatic pathways and thus form the largest family of cytokines [444][445]. The chemokine concentration acts as a chemoattractant to guide the migration of cells, mostly leukocytes (also called homing). Chemokines are small proteins weighing 8 to 10 kilodaltons characterized mainly by 4 invariant cysteine residues [445]. Functionally, chemokines can be characterized as inflammatory and homeostatic. The homeostatic chemokines are constitutively secreted and perform functions like leukocyte trafficking out of bone marrow, and across blood and lymphatic vessels. Therefore, the homeostatic chemokines are important for immunosurveillance and immune tolerance of the organism [446] [447]. Moreover, the memory of an immune response is also dependent on the leukocyte localisation during an immune response [448]. The migration of leukocyte also happens during diseases that deregulate the immune system. For example, inflammation during atherosclerosis, chronic allergies in autoimmune diseases, multiple sclerosis and many others. The chemokines that traffic such leukocytes are called, inflammatory leukocytes.

A complete classification of chemokines can be seen in Figure 6.1C Upon chemokine receptor activation, the S-S bridges between ECD1 and ECD4 / TM7, and TM3 and ECD3, brings the helices closer forming a pocket at the extracellular face (see Figure 6.3) for the binding of chemokine ligand. At the intracellular face, the ICD3 and ICD4 orients in close proximity to the heterotrimeric G-proteins (known as Gα, Gβ, and Gγ) with Gα binded to a molecule of GDP (guanosine diphosphate). Apart from ICD2 and ICD3, the intracellular regions of TM helices [START_REF] Gilman | G Proteins: Transducers of Receptor-Generated Signals[END_REF]5,6,[START_REF] Jalkanen | Amino Acids and Small Peptides as Building Blocks for Proteins: Comparative Theoretical and Spectroscopic Studies[END_REF] signaling is directed by the C-terminal helix of ORF74. Therefore, the evolutionary pressures on the C-terminal of ORF74 will be totally different from canonical and atypical chemokine receptors [482,483]. A mutational study reports that, the C-terminal region of the vChemR are usually shorter and this helps them evade the normal internalization procedure of a chemokine receptor.

All of the vChemR except YLDV and ORF74, behaves in a promiscuous manner and can bind to multiple chemokines depending on their host's immune response. Therefore, it becomes compelling to understand such biological hacking of genes that exploits the immune mechanisms, otherwise designed to identify and kill such viruses.

Our prime interest in understanding chemokine receptors was due to ACKR1's (DARC)

promiscuous behavior and its relation to Plasmodium vivax. Since ACKR1 branched out as an outlier amongst Human chemokine receptors phylogenetic tree (see section 5.3.1). The study was initially directed to find evolution of ACKR1. However, given the complex yet interesting relationships of the chemokine system a number of attempts have been made to understand the phylogeny of the chemokine system, including the vChemR [381,461,484]. All these studies have a caveat that they are centered around the mammalian phylogeny since virally encoded chemokine receptors effect the mammalian hosts. Therefore, in order to answer our questions about evolution of ACKR1 and understanding the phylogenetic perspective of the chemokine receptors, a comprehensive protocol was designed. The objective of this protocol would be to understand the exhaustive phylogenetic relationships among the chemokine receptors.

6.2 Methods:

Extraction of homologs and building the dataset:

The 21 sequences of chemokine receptors (as obtained in chapter 5) were subjected to pHMMER v3.1b2 [402] with SWISS-PROT database as the target. Output was controlled using e-value (-E) and domain wise e-value (--domE) cutoffs at 10e -5 . After filtering the hits by high e-value, short sequences and the domains with low coverage values were also removed. Hits which had significantly large bias were also removed. As a result, a refined set of 118 homologs were obtained.

A multiple sequence alignment of the 118 sequences was generated using alignment program MAFFT v7.27 (Multiple Alignment using Fast Fourier Transform) [406] with its iterative alignment method G-INS-i at 1000 iterations. The method is recommended for global alignment of sequences of similar lengths and works best on a set of <= 200 sequences. The generated MSA was checked for unnecessary gaps using a python script provided by our collaborator Dr. Sophie

Abby. The refined alignment was used to build an HMM profile that would be used to search the nr database (March 2016 release). Using 118 sequences instead of the 21 sequences assure that the HMM profile have enough diversity to match distant homologs.

Data-set optimization:

Visualizing 6404 sequences as a single phylogram would have been difficult and noisy. Therefore, the sequences were clustered at 65% sequence identity using SiLiX clustering program [485],

resulting into a non-redundant dataset. The threshold for clustering was chosen based on the consideration that a non-redundant database (NCBI_nr) clustered at 100% identity was used. Also, the average percent identity between class A GPCRs is 26% [397,398]. The clusters were selected based on SiLiX Family_networks_builder (silix-fnet) utility. It gives weighted edges that describes the network between predefined families. In this case, the set of pre-defined families (FILE.FAMS file) include the 21 Human chemokine receptors and the utility was used without -strict option.

The clusters were therefore selected based on their size (minimum 100 sequences) and weights of the edges. Thus by clustering, 3277 sequences were obtained. Of these, 148 were singletons, i.e a cluster that have only one sequence. Therefore, a final sequence set comprising of distant homologs of chemokine receptors was obtained, containing 3129 sequences. This alignment was used as a seed for MSA of 3129 sequences. A seed alignment given to MAFFT is expected to anchor the alignment for 7TM conservation. However, it should be noted that it does not conflict with the substitution rates of each position, as described by the substitution matrix.

MAFFT was used with a progressive refinement method, FFT-NS-i that uses a rough guide tree.

It should be noticed that use of a progressive alignment method gives huge boost to the speed while accuracy can be affected. However, use of the more accurate iterative methods with ~ 3000 sequences would have been computationally expensive. Therefore, the iterative alignment method (G-INS-i) used during MSA for HMM building and the presence of the seed sequences will provide close approximations for the guide tree. The default BLOSUM62 substitution matrix was used because of the SiLiX clustering performed at 65% during data-set optimization stage. The resulting MSA was edited using python scripts to remove empty gaps and noisy alignment positions. The final MSA was visualized using JalView application [407] 6.2.4 Generating the phylogenetic tree:

A robust maximum likelihood (ML) tree was generated from the MSA of 3129 sequences, using IQ-Tree v1. 4.2 [487]. Since, the data-set comprises of distant homologs with no a priori information regarding their evolutionary rates, using an ML based method is fitting. IQ-Tree provides many advanced options to optimize the tree and therefore many options were supplied to the IQ-Tree command. Following options were provided with the command:

Amino acid substitution matrix The frequency rate change of amino acids for TM proteins can vary significantly, given the low sequence identity among GPCRs. Thus, an empirical calculation of frequencies from the data (F) was selected. The model for the rate of heterogeneity was selected by using IQ-Tree's

ModelFinder utility with (-m MFP) option. Therefore, before selecting the heterogeneity rate, a test run was performed on the data and a free rate of heterogeneity across sites (R) was suggested.

R10 signifies a free rate model with 10 sites (at given time) being allowed to evolve at different rates. The selection of the rate category is a computationally expensive step for IQ-Tree and 10 is the maximum value available for large data-sets.

Additional important matrices that are derived from the protein structures were also given with option -madd. These matrices are based on structural properties like, extended, helix, other sites (EHO), 2 state and 3 state models for solvent accessibility: exposed and buried sites (EX2), and exposed, intermediate and buried sites (EX3). Ex_EHO combines the EHO and EX2 models while UL are the unsupervised trained models of EX2 and EX3. All the matrices might not be used during tree assembly, however in case of TM proteins these matrices can be of assistance.

Finally, the ML tree was bootstrapped with 1000 steps. Ultrafast option (-bb) was selected given the computationally exhaustive process otherwise. IQ-Tree was also commanded to write the bootstrapped tree along with individual branch lengths. This tree will be used for final visualization. Another statistical measure, Bayes probability test (-abayes) was also used to test individual branches along with bootstraps. These provide confidence to the tree topology.

Tree visualization:

The bootstrapped unrooted tree was visualized using interactive Tree of Life (iTOL) [409]. The topology was changed to circular and scaled according to the branch lengths for better visualization. The tree topology was analyzed for the clade organizations. In-built iTOL utilities were exploited to isolate different taxa based on branch lengths while color-marking CCRs, CXCRs, ACKRs and others. All the nodes having branch lengths < 0.05 were collapsed for improved visualization. The collapsed nodes are shown as circles proportional to the size of each node. Labels are shown at the tree circumference.

Results and Discussions

Composition of the dataset:

The raw dataset obtained from HMMer searches contained 3810 sequences that were annotated as a predicted chemokine receptor or GPCR, or uncharacterized, or hypothetical proteins, or synthetic proteins. Contemporary annotations are mostly automated and thus can be potentially misleading [490,491]. Therefore, sequences were not removed from the dataset based on their annotations.

Initially there were 3810 sequences that were annotated as a predicted chemokine receptor.

862 sequences were annotated as hypothetical proteins while 33 sequences had titles with "synthetic" keywords in them. After refinement of the first stage, based on Hmmer domain wise e-values and bias to score ratio, 892 sequences that annotated as 'predicted', 'unnamed', and 'partial' were removed. 552 of 862 'hypothetical' proteins were removed while only 4 synthetic proteins were left. When the rejected proteins were analysed, their average length was 134 residues which directly translates to the reason for their high domain e-values and high bias. Post clustering, the number of such sequences was reduced to 2029; of which 19 are annotated as unnamed while 57 were labelled as 'hypothetical'. Therefore, 1953 proteins with annotations containing 'predicted' proteins were selected in a dataset of 3129 sequences. This amounts to ~62% of the dataset, thus giving away the state of the contemporary automated annotation methods. (US28) and unique long 33 (UL33) were present in the final dataset.

Multiple Sequence Alignment

Before proceeding with the generation of the phylogenetic tree, it is logically fitting to validate the MSA on which the tree will be built. The alignment has huge gaps especially at the terminal regions. Such gaps are well expected given the known diversity of N-and C-terminal regions among class A GPCRs, decoy and viral chemokine receptors. The terminals do not have a previously known conserved position except for the C-terminal microswitch containing motif, NPxxY. However, no conserved positions are observed downstream NPxxY and therefore these regions are removed in consideration of the loss of information to the noise they generated.

Similarly, the N-terminal positions are highly variable and no highly conserved column is observed before the first cysteine residue in the sequence of the seed chemokine receptors. This corresponds to the 45 th to 55 th residues in the human chemokine receptor, given the variable length of their Nterminal deformed region. The high conservation of the first cysteine is supported by its role in the important disulfide bridge formation with TM7. The SS bridge may reduce the flexibility of the N-terminal loop and thus forming a binding pocket for the chemokine ligand.

The rest of the MSA also have some enormous gaps and the TM boundaries are also not completely conserved. This is also expected given the average identity of class A GPCR family is

~26%. Yet the important residues such as most of the cysteine, proline, tyrosine, tryptophan threonine, aspartate, and asparagine residues are found to be highly conserved if not completely conserved, as shown in Figure 6.7. This finding validated the MSA as all these residues will be under selective evolutionary pressure given their important role in the function. Apart from these, the motifs containing microswitches, including TxP, are also well conserved. However, these motifs have a single or double insertions in them. As this is further investigated, it is found that these insertions are contributed by a single sequence in each case as can be seen in Figure 6.

Tree topology

The circular tree is referentially rooted at CCR1 as shown in the Figure 6.

Occurence of Viral encoded chemokine receptors

As discussed before, in the section 6. The two swinepox viruses are present very close to each other at a distance of 0.005 units.

The viruses in the CCR8 clade are quite distant from rest of the clade while the HCMV and SPV in ACKR4 and CCR10 resp, are located at a minimal distance from their respective clades.

Given that CCR10 previously included ACKR2 and the SPV forms a distinct clade in CCR10 could be an indication that vChemR are closer to decoy receptors than canonical receptors. This is also supported by the fact that most of the vChemR are known to function as scavengers, a property ACKR shares as well. More detailed analysis of ACKR and vChemR will be required to conclude such a hypothesis, especially when the studies on human and mouse chemokine receptors have

shown otherwise [381,461,484].

Taxonomic distribution of the clades

The taxonomic contribution of the sequences in a phylogenetically scaled tree can reveal the evolution of the query protein/gene. Therefore, an analysis is performed by taxonomically identifying each sequence in the tree from its source organism and thereafter observing the stages in their evolution. Overall, the tree is populated by the taxonomic class Mammalia as majority while there are substantial number of chemokine receptors from classes Aves, Reptilia, and Fishes.

Moreover, Mammalia is not the majority population in all the clades. Chemokine receptor sequences of CCR4 and CCR8 have predominantly Avian contribution and CXCR4 have majority of sequences originating from class Reptilia. Figure 6.9 provides information about the sequence contributions by different taxonomic classes to each clade. Also, Figure 6.8 depicts the taxonomic distribution by denoting a representative cartoon belonging to the most dominant taxa in each clade. In the clade CCR4, the population of Aves chemokine receptor sequences (at 46%) is nearly succeeded by the mammalian chemokine receptors at 44%. CXCR2 and ACKR1 where some other species contributes significantly, if not equally to the clade's receptor pool. For instance, in CCR5 17% of sequences belong to homo sapiens, preceded by 34% of sequences originating from Primates. Also, it is the highest contribution of homo sapiens to any clade in the tree. These statistics from CCR5 are indicative of the number of studies performed on CCR5 possibly due its involvement in HIV-1 infection. On the contrary, there only two sequences belonging to Humans in ACRK1, one of which happens to be the query seed sequence. Similarly, a single human chemokine receptor is observed in CCR1, CCR2, CCR6, CCR7, CCR9, CXCR1, CXCR4, CXCR6 through CXCR8, and all atypical receptors, except ACKR1. These statistics are indicative of the conservation in the human chemokine receptors and different evolutionary pressures on Human CCR5 and ACKR1.

Tracing evolution of ACKR1

In clade of ACKR1, the phylogenetic clade is mapped with the information of the source species to understand the evolution of ACKR1. Figure 6.11 shows the mapped species, as cartoons, on the clade of ACKR1 along with their taxonomic grouping (numbered circles). The branch distance from the root can be estimated using the radiating scale values given on the right side. The numbers shown on the nodes signifies that the nodes after that can be collapsed under same order. For eg. [START_REF] Nishizuka | The role of protein kinase C in cell surface signal transduction and tumour promotion[END_REF] corresponds to superorder Cetartiodactyla, [START_REF] Agarwal | Role of Protein Dynamics in Reaction Rate Enhancement by Enzymes[END_REF] will collapse all bats species under chiroptera. Although the ACKR1 clade has a distance of more than 7.0 from the root, yet the whole clade, except marsupials, have evolved within 1.0 units.

Although the clade is phylogenetically remote to rest of the chemokine receptors the branches within the clade are very closely related to each other. If the clade is collapsed at 0.05 branch lengths, more than half of the clade is clustered. Moreover, as discussed in the sections above that Mammalia is the dominant population and contains enormous diversity within it. Thus, it was difficult to understand the evolutionary history of ACKR1 just by mapping the taxonomic information. Therefore, the clades were collapsed under their taxonomic orders to simplify the visualization of, otherwise very closely related sub-clades, Figure 6.12A. One taxonomic order is expanded each time to analyse the evolutionary progress, Fig 6.12[B to D]. The branch lengths are converted to evolutionary ages by subtracting individual branch lengths from the root distance.

This will reveal the youngest leaf with an age of 0 and closer the leaf gets to the root, the age value increases. Figure 6.12A depicts the phylogram focused on the ACKR1 clade extracted from the whole tree. All leaves are shown collapsed at their respective taxonomic orders, except hominidae.

For instance, all the chemokine receptor sequences belonging to camels, cattle, whales, horses, sheep, pig, and other even-toed ungulates are collapsed together under Cetartiodactyla taxonomic order. Some of the leaves cannot be clustered given their taxonomic placement in the clade. For instance, Dermoptera also known as the flying lemurs lies as a single node linking Primates to order Afrotheria. The sub-clade of hominidae is not collapsed because it contains the seed ACKR1 sequence and can serve as a reference point for tracing ACKR1's evolution. Although, the reference time of occurence of ACRK1 in Primates is comparatively later from the origin of ACKR1 in mammalia yet the speciation in primates is very rapid. Most of the topology of ACKR1 clade resembles that of the mammalian tree of life but with some critical exceptions, like placement of Eulipotyphla. If validated, such exceptions can give insights into the evolution of ACKR1 among Mammalia.

Structural relatedness between clades

The sequences of chemokine receptors have high amount of diversity with exception of a few functionally conserved regions. However, from a structural perspective the receptors share a common 7TM GPCR fold. Therefore, a quick protocol is designed to extract structural information from the tree. All the sequences in each clade was used as a query against the PDB database using Blastp. The resulting hits were sorted based on high query coverage and low e-value and the top hit was selected. It was performed in order to get one structural representative from each clade.

However, it is observed that only 12 unique PDB ids are represented as hits for the 3129 sequences. These PDB ids are: 1Z9M, 3AU4, 3DYU, 3ODU, 3OE0, 3OE6, 4MBS, 4NUV, 4XNV, 4XT1, 4YAY ,4ZUD, 5LWE, 5T1A, 5UIW, and 5XSZ. Of these 3OE6, 3ODU, and 3OE0 are crystal structures of CXCR4 and 4NUV is the structure of residues 19-30 of ACKR1. Therefore, 4NUV was rejected from the list. Since all the blast hits matched chain A of the PDB proteins, chain A of these 11 structures was extracted. From the blast results the PBDids were reverse mapped on to their query sequence and thence to their respective clades in the tree. At last, the clades that have a structural representative are: CCR2 (5T1A), CCR5 (4MBS), CCR8 (5UIW, shared with CCR5), CCR9 (5LWE), CCR10 (3AU4), CXCR4 (3ODU, 3OE6, 3OE0), GPR35 or CXCR7 (4XT1, 4XNV, 5XSZ), ACKR1 (1Z9M), and ACKR3 (3DYU, 4YAY, 4ZUD). The chain of the structures is aligned using TM-Align to assess their structural relatedness. Table 6.1:

summarizes the closeness profile of these structures. PBDID: 1Z9M is the structure of a Nectin like cell adhesion molecule which does not have a 7TM structure. Therefore, 1Z9M cannot be treated as an ACKR1 representative. Similarly, 3AU4 also does not have a 7TM structure being a Netrin receptor involved in apoptosis. Netrin receptor has a large number of helices interspersed by two beta sheets but does not belong to 7TM GPCR family and therefore, it cannot be a representative of CCR10. However, according to blast results, these PDB ids are related to the ACKR1 and CCR10 clade and therefore kept in the analysis. These might be useful as a positive and negative control for structural alignment of 7TM using TM-Align.

As revealed by the green color in the Table 6.1 that all but ACKR1 and CCR10 are structurally related. The relatedness of the structures is given by the TM-Score; a value of 0.5 and above implies that the structures are related while a value below 0.3 reflects random structures.

ACKR3 have very good structural similarity with CCR5, CXCR4, GPR35, CCR8 (shared with CCR5), and CCR2 with an average TM-Score of 0.72. CXCR4 have structure relatedness with CCR2, CCR8, and CCR5 with an average TM-Score of 0.63. GPR35 have an average score of 0.67 while CCR5 have an average score of 0.57. However, CCR2 have an average score of 0.49 when CCR2 is used as the reference but when rest of the structural representatives are aligned with CCR2 the average TM-Score is 0.73. Therefore, CCR2 is structurally related to only CXCR4 (score 0.86) but not with rest of the structures. However, they show high local similarity with the CCR2 structure when they are aligned to CCR2. Such an analysis is also important to assess the structural diversity of chemokine receptors. Strong evidence provided by the overlaps in the clades of CCR2 and CCR5 as well as CXCR1 and CXCR2 is suggestive of similar evolutionary pressures among the two pair of receptors. The similarity between the gene locations of their respective ligands as well their functional similarity further supports the merging of the two clades. However, detailed analysis of their ligand's genetic and biochemical profiles as well as receptor cross-talks during heterooligomerization have to be performed for confirming the hypothesis. Series of works by Zlotnik, Nomiyama, Yoshie, et al explores the genomic organization and evolution of chemokines going back to agnathan fishes [492][493][494][495]. The future intend of our project is to utilize such information to deduce parallels between evolution of chemokines and that of chemokine receptors. Such a study can inform us about the evolutionary pressures of chemokines on chemokine receptors and vice versa.

The current tree is rooted on CCR1 which is not an outlier to the phylogram and therefore may arise doubts. However, the rooting of the tree in this particular study does not matter because the principle question to address is the phylogenetic relationships among chemokine receptor and evolution of individual clades. Therefore, an unrooted tree can also be used instead of a rooted one. Given that ACKR1 is the most distant clade and thus can be treated as an outlier, based on the distance and not on function, a tree rooted on ACKR1 was also generated. The tree topology remained the same with few node rotations. Thus the current tree topology having each branch tested with bootstrap and bayesian probability has a high confidence value.

The great diversity among the sequences of chemokine receptors is complemented by the huge diversity of the species in the tree. The species information, in reference to the tree of life, is used to understand the evolution of chemokine receptors. One such deduction is carried out in the clade of ACKR1. The evolution of ACKR1 is traced by the virtue of their branch lengths converted to ages. The age of each node helps in identifying the first and recent occurence of ACKR1 in species and also in tracing the gaps in between. However, unfortunately there are no ACKR1 sequences identified outside mammalia yet and thus limits the access of our analysis. The absence of non-mammalian ACKR1 gene might be the reason for the huge distance of ACKR1 clade from rest of the clades that have sequences from Amphibia, Fishes as well as Reptilia.

However, there can be another explanation to the absence of ACKR1 in non-mammalian classes. A strong hypothesis might be that ACKR1 is not a chemokine receptor, based on its atypical behavior and non-specific binding to different chemokine classes. Such behaviors are noticed in some viral chemokine receptors too but ACKR1 differs from viral chemokine receptors also. The virally encoded receptors are in a constitutively active state while ACKR1, especially in reticulocytes does not signal or scavenge at all. Therefore, it might have been a viral chemokine receptor that was genetically pirated from the host during infection. During a subsequent infection by the virus (possibly a ssDNA or retrovirus) the altered receptor gene was back-pirated into the host genome. However, this is a mere speculation and would require rigorous analysis to test it;

for which the current evolutionary tree will be highly useful.

A caveat of studying evolution of viral chemokine receptors along side canonical and decoy chemokine receptors is the choice of the substitution matrix. Although the vChemR are genetically pirated from the vertebrate hosts yet they are expressed as a viral protein and therefore, virus specific substitution models should be preferred. Comparing tree topologies of ACKR and viral chemokine receptors generated using a virus specific substitution matrix to the current tree topology can be insightful.

310-helix are dynamic than α-helices. The residues associated with π-helices were found to be also closely associated with β-turn and bend rather than other helices. A counterintuitive finding was that π-helices that showed low B-factors but high accessibility. Thus defined them as very flexible/deformable also supported by their very high RMSF and Neq values. Such dynamic behavior of π-helices may be characteristics of post nucleation, cooperative protein folding effect of protein folding.

Since π-helices, due to their high deformability were being assessed for their involvement in disorder and folding process, we found out the that Polyproline II helical conformations dominates other helical forms in less structured space. Moreover, based on its geometry, sequence and structure it should be a part of regular secondary structure elements. PPII has a left-handed geometry unlike the right-handedness of popular protein helices. Therefore, in an attempt to understand PPII conformation, we reviewed the recent advances made in PPII. We found out that there has been a sudden surge in publications related to PPII. An interesting personal learning was that it is not necessary for PPII helical conformation to be comprised of proline residues at all.

Rather, the amino acid composition of PPII can change depending on its context and so does its length. This provided interesting insights into inherent flexibility PPII helices contain.

After the analysis of dynamic behavior of helices, the study was extended to non-helical conformations as well. The resulting analysis confirmed the rigidity of sheets, but also underline their capacity to transform into turn conformations. While the dynamics between turns (with hydrogen bond) and bends (without hydrogen bond) showed some strong similarities, the two conformations behave quite distinctively. These revealing results about the dynamics of DSSP secondary structure states motivated us to analyze the structures using a structural alphabet -The Protein Blocks (PB). Systematic analysis of PBs provided surprising results with multiple

information. An important one was in regards to the relationship between solvent accessibility, stability and dynamics. While a large part of buried residues remained stable, important discrepancies were observed. For at least half of the PBs (16 in number), the fact to be buried or exposed did not affect their dynamics, at all. Majority of PBs persisted as their original PB. Some PBs showed higher tendency to be not as rigid as others, particularly PB g and PB i. The changes amongst PBs in their clusters were assessed based on their geometrical compatibility. More frequently they tend to exchange with an unexpected PB than an expected one. Thus depicting the inherent flexibility in protein backbone using simple molecular dynamics. These results with PBs and DSSP states provided a very basic understanding that protein structures are much more dynamic than we usually assume due to our exposure to static X-ray crystal data. The importance of having such inherent flexibility can be attributed their involvement in regulation of cellular function.

Such regulatory processes require protein structures to modulate their behavior in different contexts and at molecular level, it is achieved by post translational modifications (PTM).

Therefore, we analyzed PDB data extracted from PTM-SD database in order to find the impact of PTMs on protein backbone. N-glycosylation, phosphorylation and methylation were selected as PTMs of interest based on the sufficient data that exist in PDB. Besides their global analyses, specific example proteins were chosen for the three PTMs -N-glycosylation in Liver Bangalore. I have already completed the structures of kinases with and without PTM, using molecular modelling. Moreover, we also recently submitted a molecular dynamics analysis of active and inactive protein kinase A. Therefore, these data can be used for a more global understanding of the impact of PTM on protein backbone.

A major concern while dealing with PTM structures are the missing regions in PDB crystals that mostly pertains to disorder in protein. The disorder helps protein structures to expand their protein-protein interactome. The selectivity of interacting partners and order-disorder transition of the protein structures is also regulated by PTMs, and most of the times by phosphorylation thus explaining the missing regions in our analyses. While managing these missing regions in phosphorylation data, we stumbled upon a rather interesting structural event.

A unique structural event of protein life known as Dual personality fragments (DPF) was identified and analyzed subsequent to the analysis of the effect of PTM on protein backbone. DPF are regions in a protein structure that can transform between disorder and order structural states and is expected to be lying at the core of structural continuum. Almost scarce information is available for DPF as Also, Cys and Gly are hydrophobic while Asp and Lys are hydrophilic. This information coupled with involvement of DPF in multi-partner interactions and MoRFs (Molecular recognition features), the presence of high propensity of these residues makes sense. Additionally, having a region with high occurrence of C, G, D, and K that also have a higher alpha helical and beta turn content is also an indicative of a DPF. Although the definite characterization of DPF and using that information to predict DPF from sequence seems distant for now yet the information acquired about the protein local structures is enriching.

The finding of a region in CDK2 that showed distant effect upon phosphorylation indicated allostery or at least long range interactions in protein structures. This motivated us to move further ahead from analysis of secondary structures to more complex multi-domain proteins like integrin αIIbβ3. The integrin structure undergoes structural transition from a bend to open conformation and allostery have been shown to play a major role. We decided to study two rigid domains in the structure which functions are the anchor during the structural transformation. Therefore, the intrinsic dynamics of these rigid beta sandwich domains, Calf-1 and Calf-2, would be quite exciting to study. Especially since the αIIbβ3 is implicated in a rare bleeding disorder like Glanzmann Thrombasthenia (GT) and pregnancy related disorder Fetal / Neonatal Alloimmune Thrombocytopenia (FNAIT). Protein blocks statistical measures like ΔNeq and ΔPB were used to analyze the impact of GT mutations on the Calf-1 domain. The significance of using these measures is their ability to resolve local rigid regions encompassed inside an otherwise deformable region.

It was observed that the impact of GT variants that may disturb the core β-strands are systematically compensated by the loops. The energy gained or lost due to loss of interactions in mutants was shown to be compensated by new interactions with the residual energy being transferred to the loops. Interestingly, of the seven GT variants studied only two, C674 and P741, displayed conformational changes at the mutated site. The case of the C674R substitution was particularly enriching for me as I remembered the famous Anfinsen's experiment. The resultant loss of the disulfide bridge relaxes the structure and introduces significant structural alterations but the β sandwich architecture persist. Such an effect suggested that the structural-functional context influences the rigidity. Thus, inherent flexibility is important and crucial to the conservation of the core.

In terms of understanding the behavior of local structural flexibility, we further notched up to a more complex structural organization with a dimer formation in a phospholipid membrane system.

The protein of interest was Duffy Antigen Chemokine Receptor, DARC. DARC is identified as a mammalian chemokine receptor that can bind to inflammatory chemokines across classes. Besides able to bind effectively to different chemokines, it does not transduce the signal since it lacks the motifs that couple with G-proteins during GPCR signaling. Therefore, International Union for Pharmacology (IUPHAR) updated the nomenclature and replaced DARC with Atypical Chemokine Receptor 1 (ACKR1). Among the atypical chemokine receptors, ACKR1 is the only one that exhibit promiscuous binding with chemokines and lacks the DRYLAIV motif completely.

Most characteristically, ACKR1 serves as a receptor for Plasmodium vivax merozoites on human RBC that leads to the symptomatic infectious stage of malaria. Although DARC was the first one to be identified among chemokine receptors yet to date no experimentally determined structure exists. Only a structural model generated using homology modelling exists that was done by Alex in 2005. Therefore, we decided to build a structural model for ACKR1 integrating the latest physiological, pathological and evolutionary information available. The physio-pathological properties of chemokine receptors assisted in identifying the key residues. Structural information from other chemokine receptors was also instrumental in establishing the basic scaffold for modelling ACKR1. Using these along with phylogenetic information of human chemokine recepors as well as structural information acquired in preceding chapters, ACKR1 was modelled as a homodimer based on the crystal structure of active CXCR4 (PDBid 3ODU). The dimeric interface was determined and key residues were identified. Most importantly, the dimer model is embedded in an erythrocyte membrane mimic system. Special caution was carried in building the membrane and was perhaps the most challenging task of all due to the specific cell structure of RBCs.

The primary objective is to understand the dynamics of local secondary structures and protein blocks at the interface region as well as at the sites of conserved micro-switch motifs.

Therefore, while the 1 microsecond range simulations are ongoing on the cluster, a primary study of the motions using ANM based normal mode analysis (NMA) is designed. The NMA results have been just weeks before completion of this thesis and therefore not included in the main Given the endemic that is malaria and especially the global widespread presence of P.vivax infected malaria, the prime objective has been extended to study the interactions of ECD1 with P.vivax DARC Binding Ligand.

During the template selection for modelling ACKR1, information of phylogenetic relationships among 21 human chemokine receptors was used. The resulting tree showed ACKR1 to be highly distant from rest of the family. This kindled the curiosity regarding the evolution of ACKR1. The design of protocol for generating the tree was suggested by Sophie. Thus we used state of the art tools like SiLiX for clustering the sequences and IQtree for generating the maximum likelihood tree. Also, the sequence alignment was checked at each stage for conservation profiles of important functional residues. For the final multiple sequence alignment (MSA) of 3129 sequences, the manually verified MSA of 118 sequences (used for HMM profile building) was used as a seed. The most fascinating part was the analysis and deducing functional information from the tree. Strong evidence provided by the overlaps in the clades of CCR2 and CCR5 as well as CXCR1 and CXCR2 suggested similar evolutionary pressures among the two pair of receptors.

The similarities between the gene locations of their respective ligands as well their functional similarity further support the merging of the two clades. The evolution of ACKR1 was traced by converting their branch lengths to ages. The age helped in identifying the first (primitive) and the most recent occurrence of ACKR1 among species of tree of life and also in tracing the gaps in between. Strikingly, there are no ACKR1 sequences identified outside class Mammalia and thus limits the access of our analysis. The absence of non-mammalian ACKR1 gene might be the reason for the huge distance of ACKR1 clade from rest of the tree that have sequences from Amphibia, Fishes as well as Reptilia. A strong hypothesis might be that ACKR1 is not a chemokine receptor and have been a viral chemokine receptor that was genetically pirated from the host during infection. However, during a subsequent infection by the virus (possibly a ssDNA or retrovirus) the altered receptor gene was back-pirated into the host genome.

Sadly, I do not have sufficient time and information at present to test this hypothesis but

this question remains open. Hopefully, I may return to address this question during my research career. I do acknowledge that the test may be negative for such a strong hypothesis but such is the pleasure of finding things out.

I 1 I. 1 2 I. 2 3 I. 3 4 I. 4 5 I. 5 8 I. 6 32 Chapter 1 :

 112233445586321 . Introduction................................................................................................................................Amino acid structures..............................................................................................................Dihedral angles…....................................................................................................................Protein structure organization..................................................................................................Beta sheets conformation.........................................................................................................Protein family classification.....................................................................................................Type of proteins based on cellular environment....................................................................10 I.7 Bragg`s law............................................................................................................................11 I.8 Workflow of X-ray crystallography.......................................................................................12 I.9 Statistics of EM maps.............................................................................................................14 I.10 Simplified view of electron microscopy...............................................................................15 I.11 A sample CD-spectra for a protein.......................................................................................17 I.12 Growth of sequence space versus structural space...............................................................18 I.13 Workflow of PSIPRED.........................................................................................................20 I.14 Protein Blocks……...............................................................................................................21 I.15 Workflow of Modeller..........................................................................................................24 I.16 DNA Substitutions…............................................................................................................30 I.17 Point mutations and SNP......................................................................................................Understanding dynamic behaviors of secondary structures...............................37 1.1 Different types of helices.......................................................................................................39 1.2 Normalized B-factors, RMSf, and rASA for helices.............................................................45 1.3 Normalized B-factors, RMSf, and rASA for whole dataset..................................................46 1.4 Persistence of initial helical state...........................................................................................47 1.5 Different clusters for α-helix..................................................................................................51 1.6 Different clusters for

58 S1. 1 68 Chapter 2 :

 581682 Different clusters for π-helix..................................................................................................53 Chapter S1 : Recent advances on polyproline II......................................................................Structural characteristic of three secondary structures........................................................58 S1.2 Orientation and structural organization of different helices................................................59 S1.3 Ramachandran Map.............................................................................................................62 S1.4 Interaction of Nova Protein K homology domain with RNA hairpin.................................66 S1.5 Year wise publication trends on Polyproline II helices.......................................................Understanding local structure behaviors using Protein Blocks.........................70 2.1 Distribution of different structural properties in whole dataset.............................................75 2.2 Dynamic exchanges of non-helical DSSP states....................................................................76 2.3 Evolution of β-clusters...........................................................................................................77 2.4 Evolution of βturn clusters...................................................................................................78 2.5 Evolution of clusters for bend.................................................................................................80 2.6 C PB in regards to relative solvent accessibility.......................................................................82 2.7 Exchange rates among different PBs......................................................................................83 2.8 Evolution of clusters for PB a.

90 Chapter 3 : 1 I. 1 4 Chapter 1 : 54 Chapter 2 : 81 Chapter 3 :Chapter 5 :Chapter 6 :

 903114154281356 Hierarchical clustering of the 5-clusters of each PB.............................................................Local protein flexibility in light of physiological structural events....................90 3a : Impact of post-translational modifications on protein backbone conformation.........92 3a.1 PTM cross-talks and information sharing is indicative of a PTM code.............................93 Index for Tables I. Introduction................................................................................................................................Characterization of different types of helices found in proteins.............................................Understanding dynamic behaviors of secondary structures...............................37 1.1 SCOP ids of the final selected 169 domains.........................................................................41 1.2 Behaviors of helices..............................................................................................................44 1.3 Initial state perseverance of each helix.................................................................................48 1.4 Exchange rates of helices expressed as percentages.............................................................49 1.5 Analysis of different clusters................................................................................................Understanding local structure behaviors using Protein Blocks.........................70 2.1 Frequencies of occurrence of DSSP states during dynamics...............................................73 2.2 Frequencies of occurrence of all PBs during dynamics.......................................................74 2.3 C PB -Frequency of PB staying in the initial PB assignment................................................Local protein flexibility in light of physiological structural events....................90 3a : Impact of post-translational modifications on protein backbone conformation.........92 3a.1 The dataset for PTM analysis............................................................................................97 3a.2 Dataset for phosphorylation sites......................................................................................98 3a.3 Dataset to analyze local and global impacts of PTMs on 4 proteins................................99 3a.4 Statistical tests for the 4 proteins....................................................................................109 Protein dynamics in structural assemblies..........................................................164 5.1 Three tier method for filtering template structures..........................................................178 An evolutionary perspective on Chemokine Receptors.....................................196 6.1 Structure relatedness among different clades...................................................................227 INTRODUCTION I.1 Proteins

Figure I. 1 .

 1 Figure I.1. Amino acid structure in (a) shows the -NH2, R and -COOH groups and (b) shows the classification of amino acid based on the nature of R group. + Source internet, www.stanford.edu/tutorials/biochem/

Figure I. 2 .

 2 Figure I.2. Polypeptide chain in fully extended conformation showing the Φ and Ψ dihedral angles. The different bond lengths are also shown. + + Source internet: Nelson & Cox, Lehninger Principles of Biochemistry, 4th ed. (2004)

Figure I. 3 :

 3 Figure I.3: Levels of protein structure organisation; Primary, Secondary, Tertiary and Quaternary structures. + Source internet, www.ucsf:com/lectures/molecularbiology/proteins

Figure I. 4 .

 4 Figure I.4. The top and side views of (a) antiparallel β-sheet (b) parallel β-sheet.

Figure I. 3

 3 Figure I.3 shows a β-hairpin motif in the secondary structure section.

Figure I. 5 :

 5 Figure I.5: A hypothetical protein family classification showing Family, Superfamily and Subfamily level hierarchy. + Source internet. www.ebi.ac.uk

a)

  Domain: A part of a protein. For simple proteins, it can be the entire protein b) Species: The domains in "protein domains" are grouped according to species name c) Protein domain: Grouping together similar sequences having essentially the same functions d) Family: It contain proteins with similar sequences signifying homology but typically distinct functions e) Superfamily: Proteins in a family are grouped together which have at least a distant common ancestor f) Fold: It groups structurally similar superfamilies g) Class: Grouping the protein structures mainly on secondary structure content and organization. Four classes are defined in SCOP -domains containing all α-helices (a.*), domains containing all β-sheets (b.*), domains containing α/β (c.*) and domains containing α+β (mainly segregated, represented as superfamily d.*). SCOP classification also goes beyond 'c.*' uptil 'g.*' containing different categories of transmembrane proteins. *denotes the sub-family structure after the superfamily a, b, c, d, e, f, and g.

(

  passing the membrane more than once) protein. They function mostly as membrane receptors, transport channels, Ion channels, and cell-adhesion and aggregation molecules. Membrane proteins form a separate class in SCOP (e.* to g.*). An example of globular protein, membrane protein and fibrous protein each is shown in Figure I.6.

Figure I. 6 :I. 5

 65 Figure I.6: Examples of (a) globular proteinmyoglobin (PBDid-1vxc) (b) membrane protein -DARC dimer embedded in membrane (modelled in chapter 5) (c) fibrous proteincollagen triple helix (PBDid-1bkv) + Source internet, www.rcsb.org/

  [START_REF] Jalkanen | Amino Acids and Small Peptides as Building Blocks for Proteins: Comparative Theoretical and Spectroscopic Studies[END_REF].

Figure I. 7 .

 7 Figure I.7. Pictorial representation of Bragg's law.

  molecular replacement (MR), multi-wavelength anomalous diffraction (MAD), multiple isomorphous replacement (MIR) etc. Once the electron density is obtained, the mean positions of the atoms in the crystal as well as the extent of disorder in the structure can be determined. A flow chart for X-ray crystallography is shown in Figure I.8.

Figure I. 8 .

 8 Figure I.8. Flowchart showing workflow of X-ray crystallography. A crystal is bombarded with X-rays to obtain a diffraction pattern. The pattern is then used to generate an electron density map into which atoms are fitted, sometimes based on best guess. + Source internet, www.stanford.edu/tutorials/biophysics/

Figure I. 9 .

 9 Figure I.9. Statistics of EM maps and their resolution since 2013. (a) shows the frequency of occurrence of various resolutions of EM maps and (b) shows the best and average resolutions over the years. The worst resolution has been clipped to 30Å for this plot. + Source internet, www.emdb.org/

  the pseudo-atomic model for the structure in question. Depending on the resolution, this can be achieved either through de-novo model building or through rigid body fitting or flexible fitting of predicted models / structures from other techniques. A simplified view of the electron microscope is given in Figure I.10.

Figure I. 10 :

 10 Figure I.10: A simplified view of the electron microscope.

Figure I. 11 .

 11 Figure I.11. A sample CD-spectra for a protein. The profiles for different secondary structures are shown in different colours.

  homology modelling, threading, and ab-initio prediction. The first two are knowledge-based methods; they predict protein structures based on knowledge of existing protein structural information in databases. Homology modelling builds an atomic model based on an experimentally determined structure that is closely related at the sequence level. Threading identifies proteins that are structurally similar, with or without detectable sequence similarities. The ab initio approach requires molecular simulations to predict structures based on physicochemical principles governing protein folding without the use of structural templates. There are meta-servers that combine fold recognition and homology modelling to model a structure based on multiple templates matching different folds.

Figure I. 12 .

 12 Figure I.12. Growth of sequence space vis-à-vis the structural space. (a) shows the number of sequences deposited in Uniprot since 1990 (Taken from Uniprot) (b) shows the number of structures deposited in PDB over the years + Source internet www.rcsb.org

Figure

  Figure I.13.

Figure I. 13 .

 13 Figure I.13. Workflow of PSIPRED.

  prototypes for the central region of α-helix and β-strand, respectively. PBs a-c primarily represent the N-cap of β-strand while e and f correspond to C-caps; PBs g -j are specific to coils, PBs k and l correspond to N cap of α-helix while PBs n -p to C-cap.

Figure I. 14 :

 14 Figure I.14: Protein blocks. Top row depicts the 5 residue long prototype. Bottom row shows the 16 protein blocks along with their respective secondary structure approximations.

Figure I. 15 .

 15 Figure I.15. Workflow of Modeller. The target sequence is aligned to the template, spatial restraints are obtained and then satisfied to obtain a 3D model. + Taken from Modeller v9.14 tutorial pages

I. 8 . 1

 81 Type of changes: In molecular biology mutations are caused by substitutions of nucleotide bases or amino acids. However, given the central dogma of molecular biology the substitutions in nucleic acids are fundamental. In DNA the substitutions are of two types: A) Transitions: a substitution of a purine by purine base or pyrimidine by a pyrimidine base. A ↔ T or C ↔ G substitutions will classify as transitions [Figure I.16]. B) Transversions: a substitution of a purine base by a pyrimidine base is called transversion. (A or G) ↔ (C or T, or U in RNA) is termed as transversion. Such substitutions are less frequent than transitions [Figure I.16].

Figure I. 16 .

 16 Figure I.16. DNA substitutions. The exchanges between purine to purine nucleotide bases is called transitions and are more frequent in nature. The exchange of a purine base with a pyrimidine base is called transversion. Transversions are rare. + Source internet, www.humangenomeproject.org

Figure I. 17 .

 17 Figure I.17. Point mutations and SNP. A) A point mutation is any change in the sequence of DNA that may or may not alter the amino acid sequence. B) Single nucleotide polymorphism (SNP) are the co-existing changes in the DNA all of which can affect the amino acid sequence. However, none of the effects of SNP is deleterious. The different polymorphisms (A, G, T) are termed as alleles. If the lower representing allele in a SNP has a fixation value of above 1%, then it can be called as a mutation. However, defining an allele by 1% fixation is still debatable. + Source internet, www.humangenomeproject.org

O. 1 O. 2 O. 3 O. 4 O. 5

 12345 McGill university, Canada is to perform docking analysis of seven different sugars with Levansucrase from five different species. The collaboration with N. Srinivasan lab at Indian Institute of Science involves the investigation of structural dynamics of different protein kinases in their active and inactive state. Recently, a research article derived on molecular dynamics analysis of inactive and active protein kinase A (PKA) have been submitted to Biophysical Journal. Following are the main objectives on which chapters are constructed: OBJECTIVES Understanding local dynamics of repetitive secondary structures. Studying the dynamic behavior of structural alphabets-Protein Blocks. Systematic study of local structural changes in special events in a protein structure biology, like Post-translational modifications and Disorder to order transitions. To understand role of inherent flexibility at a more complex structural organization and function; Study of protein domains in a protein structure that undergoes structural transition. Systematic analysis of structural organization and dynamics in a protein protein complex; Study of a structural oligomer and interactions involved in pathology.

Figure 1 . 1

 11 Figure 1.1 The different type of helices.Two views, lateral and dorsal (bottom) are provided for each helix to appreciate the differences in their helical rise, pitch and the helical core.

Figure 1 . 2

 12 Figure 1.2 Normalized B-factors, RMSf, and rASA for α-, 310-, and π-helix. The 3 x 3 matrix shows individual plots of normalized B-factors (Row1), normalized RMSf (Row2), and relative Solvent Accessibility (row 3) for α-helix (col1), 310-helix (col2) and π-helices (col3). The values in Table1.2 are calculated from these plots.

Figure 1 . 3

 13 Figure 1.3 Normalized B-factors and RMSf behaviors on the whole dataset. A) -Normalized Bfactor distribution; B) -Normalized RMSf distribution; C) Correlation between normalized Bfactor distribution and normalized RMSf.

Figure 1 . 4

 14 Figure 1.4 Persistence of initial helical state. The frequency of residues remaining in the original assigned state of the three types of helices during simulations. A) α-helix; B) 310-helix; C) π-helix.

Figure 1 . 5 Figure 1 . 6

 1516 Figure 1.5 Different clusters for α-helix. A) Five clusters with a gradient of color are shown (ranging from red 100% to blue 0%). The displayed secondary structures are: α-, 310-and πhelices, β-strand, turn (T), bend (S), β-bridge (b) and coil (C). B) shows the correlation between normalized B-factors and normalized RMSf among different clusters. Extent of flexibility can be estimated from the correlation. For e.g α C and α T1are the most flexible clusters with most of the α-helices transforming to coil and turn conformations, respectively. Similarly, α 1 is the most rigid cluster with 76.4% of α-helices showing perseverance. (refer to table1.5)

Figure 1 . 7

 17 Figure 1.7 Different clusters for α-helix. A) Five clusters with a gradient of color are shown (ranging from red 100% to blue 0%). The displayed secondary structures are: α-, 310-and πhelices, β-strand, turn (T), bend (S), β-bridge (b) and coil (C). B) shows the correlation between normalized B-factors and normalized RMSf among different clusters. Extent of flexibility of π-helix can be easily estimated from the correlation, as most of the clusters lie on the right half having higher B-factor and RMSf values. For e.g cluster nπ T has lessRMSf but higher B-factor value which suggests that π-helix to Turn transition does not lead to deformability. In contrast, the cluster with π-helices conserved has high RMSf value thus showing the inherent flexibility in π-helices. C) The cluster matrix for the assignments done using DSSPold. As can be observed that almost none of the clusters had π-helix representation.

  . 25% of the total residues are associated with such structures [155]. However, another common repetitive conformation that was characterized before the β-turns in the 1950s, but often forgotten. Such conformations are called Poly-l-proline-II helices II (PPII) [156,157] (see Figure S1.1B).

  Figure S1.2 shows a comparison of PPII with other local structure helices. The PPII helix has distinct trans-isomers of peptide bonds with dihedral angles of [-75°, +150°]. The rise per residue of PPII helix is 3.1 Åwith three residues per turn. Thus, this distinct helical structure rises at 9.3 Å per turn compared to 6.0 Å pitch of a 310-helix. The primary reason for such open and relatively elongated geometry of PPII is the absence of H-donor atoms due to the cyclic side chain of proline residues. Therefore, the PPII conformation is highly acceptable of H-donor atoms from its environment or third party moieties enhancing its solvation energy. PPII (containing hydroxyproline) is observed commonly in the collagen triple helix and hence was deemed confined to fibrous proteins.

Figure S1. 2

 2 Figure S1.2 Orientation and structural organization of the different helices. A) α-helix: right handed with a spherical coiling. B) 310-helix, C) π-helix, and D) Poly-II-proline helix: left handed with a triangular prism coiling. Proline residues are marked in yellow. E) PPII helix with minimum residues possible. Only three residues can adopt a PPII conformation. In this example, none of the

  S1.3B). A recent review catalogues the evolution of Ramachandran map very efficiently [177]. It is, however, very distinctive observation that Prof. Ramachandran incepted the idea based on the collagen hydrogen bonding argument [161,178], which arose due to the presence of hydroxyproline [160].

  investigated the structural aspects of PPII in DNA binding and recognition [204]. Exemplifying with three DNA interacting proteins viz.; the third K homology domain of NOVA-2 see Figure S1.4 [210], the Epstein-Barr nuclear antigen-1, and the Drosophila paired protein homeodomain, they quantify the binding of PPII to the nucleotides' minor groove and underline the specificity and nonspecificity of recognition. The optimal size and specific recognition offered by PPII backbone residues strongly suggest to identify PPII as a nucleic acid binding motif [204].

Figure S1. 4

 4 Figure S1.4 Interaction of Nova Protein K homology domain with RNA hairpin (PDBid: 1EC6_A [210]). The conserved motif of the variable loop is coloured in yellow. The two PPII helices are coloured in magenta. The occurrence of C-term PPII helix is reported to be the difference between RNA bound and unbound form. Image is generated with the PyMOL software.

  [228,229], (v) Analysis of protein contacts [240], (vi) Structural modelling of transmembrane proteins [241,242], (vii) Definition of a reduced amino acid alphabet dedicated to mutation design [243,244], (viii) Protein structure superimposition and comparison [245-247]; (ix) Reconstruction of globular protein structures [248], Peptide designing [249], and (x) Definition of binding site

Table 2 . 2

 22 Frequencies of occurrences of all PBs during dynamics. The table summarizes the frequency of occurrences of all the 16 PBs during MD simulations of 169 domains. PB m and d are the most commonly observed PBs which is an indicator of order in the structure and less flexibility during dynamics. Distributions of normalized B-factor, normalized RMSf and relative solvent accessibility follow classical distributions as observed in chapter 1 (see Fig 2.1A to 2.1C). The correlation between normalized B-factor and normalized RMSf is 0.43, while their correlation with Neq is low, i.e. 0.24 and 0.14, respectively. Both B-factors and RMSf are global properties as they take into account the overall structure for calculations. Neq on the other hand is a local and precise value for the given position and can be influenced by ±2 neighbors since PB is a 5 residue long prototype.Therefor, low correlation between B-factors, Neq and RMSf, Neq may indicate that the local structures in the dataset proteins behave differently than the overall protein dynamics. Indeed, more than 60% of the residues have an Neq of 1.0, i.e. does not change during all simulations, while only 0.8% have a Neq higher than 4 (seeFig 2.1D). This indicates that overall the 169 chains have high mobility but their local structural regions do not move intrinsically.

Figure 2 . 1

 21 Figure 2.1 Distributions of different structural properties. A) Normalized B-factor, B) Normalized Root mean square fluctuations (RMSf), C) Relative solvent accessibility, D) Equivalent number of PBs, Neq. The image can be divided in to two panels. Left panel includes distributions for structural properties of static structures while right panel displays the structural properties of protein dynamics.

Figure 2 . 6 C

 26 Figure 2.6 C PB in regards to relative solvent accessibility. Following the analysis of C PB in table 2.3, the percentage a residue stay associated to its initial PB assignment was linked to the relative solvent accessibility (rSA). For each PB, four classes were defined to consider the association to initial PB, ranging from high (>75%, green), medium (75-50%, purple and 50%-25%, blue) to low (>25%, pink). In each panel, on the left is shown the occurrences, while on the right are the normalized values to 100% for every rSA value. The four PBs shown here are PB d (top left), PB m (top right), PB j (bottom left), and PB n (bottom right). The difference between rSA tendencies of the lower panel do not agree with those of top panel ones. The rSA profiles of PB d and m differs in different classes while for PB n these is no significant difference and for PB j there is no difference at all.

Figure 2 .

 2 Figure 2.7 shows the distribution of PBs accordingly to the initial assigned PB. It reflects the previous results underlying the important frequencies of PBs m and d (96% and 94%), and the low frequencies of PBs g and j (59% and 72%) to stay as assigned. It also shows the interconvergency amongst PBs, if any. Hence seven PBs transforms to PB m with a frequency higher than 2% (a threshold used in all representation) and eight to PB d. 22% of these transitions are observed, the highest one being PB g to PB p (9%), PB g to PB c (8%) and to PB e (6%), and from PB p to PB m (6%). While most of the transitions are logical in a way that geometrically they stay in similar neighborhood. Another way to analyse the evolution of PBs is the computation of Neq (see Fig 2.7).

Figure 2 . 7

 27 Figure 2.7 Exchange rates among different PBs. The plot shows the different PBs a residue adopts during dynamics. x-axis is labelled as 'from' i.e. the initial PB assignments and y-axis are the 16 PBs it has a possibility to change to. The color scheme for the plot is same as used before, blue shows minimum values while red shows maximum establishing a range between them. PB d, f, k, and m shows strong tendencies towards perseverance. PB g however, is the relatively less conserved and can transform to other PBs like,PB p, c, e, m, a, d, and n. This is reflected in the

Figure 2 . 8

 28 Figure 2.8 Evolution of clusters of PB a. The figure comprises of a table that provides details about the individual frequency of the cluster as well as static and dynamic structural properties. Values for both Neq are given; the average Neq (Neq) and Neq of the cluster (cl. Neq). The spread of each cluster of a is shown. As apparent, one 'stable' cluster herein, a1. While one entirely fuzzy cluster among the 5 clusters is expected given the increasing deformability. a4 shows the characteristics of the fuzzy cluster. Transformations among PB c, f, d are considered geometrical transitions based on their geometric resemblance.

Figure 2 .

 2 Figure 2.9 show the details about clusters of PB b. The cluster b1 (>97% of PB b) represents 79% of the original PB b with lowest rSA and lowest nRMSf but the second lowest nBfac (0.00

Figure 2 . 9

 29 Figure 2.9 Evolution of clusters of PB b. The figure comprises of a table that provides details about the individual frequency of the cluster as well as static and dynamic structural properties. Values for both Neq are given; the average Neq (Neq) and Neq of the cluster (cl. Neq). The spread of each cluster of b is shown. As apparent, one 'stable' cluster herein, b1. While one entirely fuzzy cluster among the 5 clusters is expected given the increasing deformability. b5 shows the characteristics of the fuzzy cluster. Transformations among PB c and d are considered geometrical transitions based on their geometric resemblance. However, cluster b3 shows a non-geometric transition from b to k but strikingly it has nBfac of 0 and very low nRMSf.

Figure 2 . 12

 212 Figure 2.12 Hierarchical clustering of the 5-clusters of each PBs. In blue are shown the fuzziest clusters and in red the first cluster associated to each PB.

  3a: Impact of post-translational modifications on protein backbone conformation 3a.1 Introduction 3a.1.1 Physiological role of Post-Translational Modifications After its synthesis, a protein can undergo reversible or irreversible covalent modifications, namely Post-Translational Modifications (PTMs). The modifications alter the physicochemical properties of the proteins and thereby regulate enzymatic activity, cellular localization and intermolecular interactions [260-262]. Additionally, a protein could be modified in many ways and at different residue positions over time. The same position may also undergo changes of different kinds.

  [264], to the attachment of bulkier oligosaccharide by glycosylation[265]. PTMs are essential to regulate biological functions, such as DNA transcription by histone methylation and demethylation, acetylation or phosphorylation[266,267], nuclear-cytosolic or extra-cytosolic transport bySUMOylation [268,269] or glycosylation[270,271], tagging proteins for degradation by ubiquitination[272], and regulation of kinase activity with phosphorylation [273]. Due to implications in all major physiological functions of the cell, PTMs are often associated with major human diseases such as cancer, diabetes, cardiovascular disorders and Alzheimer's disease [274-276]. 3a.1.2 The PTM code In context of protein function, such diverse roles may lead to cooperative mechanisms of PTMs such as competition for serine and threonine residues between phosphorylation and Oglycosylation [277]; ubiquitination favored over phosphorylation leading to protein degradation [278], or the interactions between PTMs regulating the activity of the p53 protein and Histones [279,280]. These observations suggest towards the existence of a PTM-code [281-283], which is based on the presence and association of several PTMs leading to the realization of particular functions (Fig 3a.1). Recently, the increasing number of annotations on PTMs have assisted in understanding the cross talk or direct / indirect influences among different types of PTMs [284-286] their competition for the same residue [287], or the co-evolution of different PTMs sites within the same protein [288,289].

+

  Images taken from: A)[289] and B [280] 3a.1.3 Effect of PTMs on protein structure

  which was recently proposed to function as protein interaction switches in more ordered regions [295]. 3a.1.4 Computational analysis of PTMs The available data on PTMs have increased drastically in the recent years due to improvements of mass spectrometry-based detection methods [296]. To acknowledge such expansion of data, many databases and prediction tools have been developed. They have enhanced the understanding of various PTMs in different organisms and simplified the analysis of complex PTM data [297].

  3a.1.5 PTM-SD Post Translational Modification Structural Database, abbreviated as PTM-SD (http://www.dsimb.inserm.fr/dsimb_tools/PTM-SD/) [305] was designed by Craveur et al. from our lab in 2014 [136]. PTM-SD (Fig 3a.2) is designed to give users a curated access to the proteins for which one or more Post Translational Modification(s) is (are) structurally resolved in the Protein Data Bank (PDB) and also experimentally annotated in dbPTM [299] and PTMCuration[306]. PTM-SD uses diverse set of rules to underline the discrepancies between annotation in the structure and the sequences owing to different sources. Also, PTM-SD allows the user to create customized PTM queries and perform different analyses on the returned hit. For example, computing distribution of organisms, proteins, PDB codes/chains, and PTM types, assigning PBs, computing Neq (section 1.6.3), highlighting discrepancies between PDB sequence and UniProt sequence, clustering for generation of non-redundant dataset, etc.

For example ,

 example Neq value around 6 would indicate that at the current position of interest, 6 different PBs are observed. An Neq exactly equal to 6 would mean that 6 different PBs are observed in equal proportions (1/6). By plotting the computed Neq value at each residue position (Fig 3a.3), it is possible to locate which protein regions have local conformational change, or in other words, which region of the structure represents backbone deformation.

Figure 3a. 3

 3 Figure 3a.3 Using Neq to understand protein backbone flexibility. A) The protein backbone is assigned with PB sequence. The structures are superposed in 3D thereby yielding a superposition in 1D, as a sequence of PBs (shown in B). C) The Neq is then calculated as the equivalent number of PB at a given position. The green color in the plot maps on to the green highlighted region in the (A) and (B).

Figure 3a. 4

 4 Figure 3a.4 Comparisons of PTM sites of N-glycosylation and Phosphorylation. The top panels show the PB profiles of A) N-glycosylation and B) Phosphorylation. The PBs are plotted on the Yaxis and PTM position in the chain at X-axis. The colors are encoded according to the intensities as mentioned by the vertical bar on the left with blue depicting the least and red depicting the max. intensities. The white color or absence of a PB intensity corresponds to the missing region in the PDB file. The lower panels show the Neq analysis of A) N-glycosylation and B) phosphorylation. The Neq values are plotted on X-axis. The red curve indicate the amount of data used to compute Neq values, or in other words the percentage of ordered residues at each position in the X-ray crystal.

  computed at the PTM position. Preceding positions, i.e. upstream -8 to -2 show even higher diversity. It is important to confirm that the absence of data in the surrounding positions is not the consequence of phosphorylation sites located at the N-or C-terminus; indeed, only 12 of them (out of 92) are close to the protein extremities. 3a.3.1.2 Analysing structural conformations using PBs A more precise analysis of the distribution of each type of PBs is depicted in (see Fig 3a.4 top panels). The intensity of the color at each position depicts percentage derived from the frequency of the local conformations occurring at the position. Resulting color underlines that Nglycosylation and phosphorylation sites are observed for all types of local conformations, almost any kind of PBs (except PBs g for both, and, h, j, and p for phosphorylation).The conformations of the N-glycosylation sites and their surrounding residues are mainly associated with the PBs d and m. However, this proportion does not exceed 31%. It is interesting to note that the positions +3 to +6 downstream of the N-glycosylation sites are significantly observed in a PB d conformation. This illustrates the fact that, ~1/3 of the times N-glycosylation site precedes a β-strand conformation.For phosphorylation, the modification sites have a preference of PB d, the cores of βstrands, in a little over 40% of the cases. The vicinity of the phosphorylation sites is also observed with a wide variety of conformations, however a slight preference was observed for the PBs b, c, d, f, l and m. It should be noted that more than 50% of the phosphorylation sites are separated by two residues of a PB d.It is important to understand that data used here provides information on the backbone conformation of PTM sites when the modifications are present, but do not obviously reflects the backbone in the absence of modifications. Additionally, while phospho-serine and phosphothreonine share similar PB profiles, they are distinct from phospho-tyrosine (see Fig 3a.5 and Table3a.2). The modified residues were observed in a large set of backbone conformations for all three cases, but the preferences for the core β-strand conformation (PB d) is greater in the case of Ser and Thr. On the contrary, Tyr does not show any clear preference for a local structure.

Figure 3a. 5

 5 Figure 3a.5 Flexibility profile for different types of Phosphorylation. Neq distribution curves (black) gives an insight into the extent of local structural changes at the phosphorylation site and its sequential neighborhood. The red curve represents the percentage of available data for calculating Neq at a position. Higher the percentage better is the confidence. As can be seen that the Neq profiles of phospho-serine (A) and phospho-threonine (B) are similar in topology while they both differ from the Neq profile of phospho-tyrosine (C).

Figure 3a. 6 N

 6 Figure 3a.6 N-glycosylatation on the Asn141 of the human renin endopeptidase (P00797). The Neq profiles are given at a local scale (A), for the surrounding positions of the PTM site (colored in green), and at a global scale (B), for all sequence positions. (C) The 80 structures used for the computation were aligned on the backbone, and represented in cartoon. The glycosylated position is shown in green sticks, the disulfide-bridges in blue spheres, and the DPF loop colored in red.

Figure 3a. 7

 7 Figure 3a.7 Local structure comparison of phosphorylation and methylation. Top panel shows the superposed chains containing phosphorylation (left) and methylation (right). The green color in the cartoon represented structure depicts the PTM. Lower panel, shows the Neq analysis of all chains containing phosphorylation and methylation. On x-axis is the Neq and residue positions (UniProt) is plotted on the y-axis. The red curve shows the %age of structural information available (ordered residues). The green trace marks the PTM and its neighbors. A) Threonine 160 phosphorylation in CDK2. B) Histidine 75 methylation in Actin.

Figure 3a. 8

 8 Figure 3a.8 Normalized B-Factor distribution for different proteins with and without PTMs. Each plot depicts normalized b-factor distribution of the protein with PTM (black line) and without PTM (yellow trace). The green colors highlights the PTM site and its neighborhood in structures with PTM (light green) and without PTM (pale green trace). The blue and red lines shows the position-wise data availability inPDB. The b-factor trends match with the trends observed in Neq analysis. Major differences are seen in B-factor values during phosphorylation while least difference is observed in N-glycosylations.

Figure 3a. 9

 9 Figure 3a.9 Impact of methylation on Actin and its ligand binding. Superimposed Actin structure along with its binding ligands. A) Actin with its ligand, without the methylation of H75. The ligands are represented as stick models. B) Actin with ligands while Histidine at 75 is methylated. Subtle

  proteins[315] by expanding their protein protein interactome. The selectivity of interacting partners and order-disorder transition of the protein structures is regulated by PTMs, and most of the times by phosphorylations.[316]. During the analysis of CDK2, identical structures with missing coordinates in the catalytic loop (functional domain) are found to be flagged as a Dual Personality Fragment[313,316]. However, this region gets ordered based on the phosphorylation of Thr 160. It may be suggested that this selectivity of interacting partners for CDK2 may well also be impacted by the number of phosphorylations in and around the catalytic domain. when using X-ray crystal data from Protein Data Bank (PDB) is related to missing regions or regions with no coordinates. The length of such regions range from a single amino acid to 20 to 25 residues and sometimes larger. Missing regions in a crystal structure arise when the X-ray diffraction pattern from the crystal is too ambiguous for a crystallographer to resolve to certain atom or molecule. Thus signifying regions in the protein structure that are highly mobile or deformed that they cannot be snapped by X-ray or CryoEM. These highly mobile, deformed regions existing as an interconverting ensemble of structures in a protein and are functionally attributed as natively unfolded or intrinsically unstructured or intrinsically disordered regions (IDRs)[317,318]. The IDRs have been shown to be of crucial importance in proteinprotein interactions since they can interact with more than one partners, given their structural malleability[319,320]. Some prominent examples of proteins that are natively unfolded are: Tau protein [321], MAP2 [322], α-synuclein [317], and Myelin Basic Protein [323].

  order and disorder as binary states is being challenged. Recent research defines the structural space as a continuum with structure and disorder being the two extremes[315]. The DPF lie at the center of this structure continuum. Figure 3b.1 shows an example that undergoes order -disorder transition [313].

Figure 3b . 1

 3b1 Figure 3b.1 An example of disorder to order transition. Shown here are the two crystal structure of same protein, Cyclin dependent Kinase 2 (CDK2). A) shows the CDK2 bound to inhibitor staurosporine (STU) thus deactivating the kinase. Thus PBDid 1AQ1:A is CDK2 in inactive state.Two regions are seen missing (represented by dashed lines). B) An active state structure of CDK2, identifiable by presence of its substrate peptide and ATP. The regions missing in A) can be seen in the active state (shown in red). + taken fromZhang et al, 2007, Structure [313]

3b. 1 . 3

 13 Characterization of DPF Due to lack of a definitive structure, the disorder regions (IDRs or IDPs) are characterized based on the sequence related properties. However, DPF can also express as structured regions and therefore can be characterized based on sequence as well as structural features. So far, only Zhang et al. have systematically characterized DPF in 2007 that is more than a decade ago. Following is a summary of their results and observations: A) 92.3% of DPF are less than 10 amino acid residues in length. B) No specificity in neighborhood of a DPF was observed as 50% of the dataset had a DPF in vicinity of a disordered region while rest 50% had an ordered region next to DPF. C) Structural analysis was performed just with DSSP assignments. It was observed that most of the DPF have been assigned as 'C' (coil) by DSSP. However, a striking 27% was assigned in regular secondary structures with 20% assigned as helices (collectively H, G, I) and 7% as sheets.

3b. 1 . 4

 14 Functional importance of DPFAfter characterising DPF based on certain sequence and structure features,Zhang et al., 2007, also characterized functional importance of DPF. They used ScanProSite to predict functionally important sites in their dataset. DPF were shown to be strongly associated with post-translational modifications. About 70% of DPF were tagged with predicted PTM sites by ScanProSite.

3b. 1 . 5

 15 Dynamics in DPFSo far, to the best of our knowledge, there have been no attempt to understand the role of flexibility in DPF or structural space lying at the center of the structural continuum. In the current context of our objectives, it becomes fitting to study the inherent role of flexibility in highly dynamic structures such as DPF. The analysis of local structure behavior in the DPF transitions can be very useful and pivotal in our understanding of structural flexibility. Therefore, it was decided to redo the structural and sequence characterization of DPF. Thereafter model the missing regions in disordered DPF to perform short molecular dynamics on both states of DPF to catalogue the local structure behaviors.

3b. 2 . 2 2 . 3 2 . 4

 222324 Feature assignmentAll the structures were assigned with different structural and sequence features in order to systematically analyse the DPF, order and disorder regions. Besides amino acid sequence, various features assigned for each structure includes: a) DSSP-All the 8 states of DSSP[144] was assigned for each residue. For methodological details, please refer to section I.6.2.1. b) Segno-Segno assignments do differ a lot from DSSP as both have an agreement of only ~82%[182]. Segno was used specifically for the assignment of Polyproline helices which is one of its nine state assignment. For methodological details, please refer to section S1.3. c) ProMotif-A 1995 program [186] is based on DSSP-like approach and efficiently assigns many structural motifs like, β and ɣ turns, β-bulges, β-hairpins, Ѱ loops, β-α-β units and disulfide bridges. It uses various geometrical properties based on hydrogen bonding pattern and distances between consecutive Cα atoms to identify these structural motifs. ProMotif v2.0 was used to specifically include β-turns information in DPF regions. d) Protein blocks-Besides, secondary structures PBs can provide precise approximation of the local structure which can be highly useful in characterizing DPF. Therefore, all the structures were also assigned with 16 PBs. For methodological details, please refer to section I.6.3 or 3a.2.2. e) B-factors-Crystallographic temperature factors were extracted from pdb files for all the structured regions.f) Relative solvent accessibility-The solvent accessibility of each structured residue was calculated using Naccess v2.1.1 (www.bioinf.manchester.ac.uk/naccess/) which provides both absolute as well as relative solvent accessibility for each residue. g) Disorder-As it is the unstructured region, the amino acid sequence was extracted by comparing the SEQRES and ATOM records of the pdb coordinate file. 3b.Pairwise sequence alignment Pali v3.0 structural alignment database (Phylogeny and Alignment of homologous protein structures) [326] contains the structure based, domain level pairwise alignment of all the homologous proteins. The proteins are decomposed into domains according to SCOPe v2.04. Therefore, alignments for 192163 structures were extracted from Pali. A total of 2168 pairwise alignments were obtained from Pali. 3b.Identification of DPF, Order and Disorder regions The different regions of interest namely, DPF, Order, Disorder were extracted from the Pali pairwise alignments. A group of Python methods were written to extract these regions by simply defining the following criterion:

Figure 3b . 2 1

 3b21 Figure 3b.2 Schematic flow of DPF, Order and Disorder identification from alignment. The image shows a pairwise structural alignment derived from Pali between PBDIDs-1H8Z:B and 2ZC6:D chains. The PB sequence (present as the second row in each PDB) is scanned for PB z that signifies undefined PB. If a region bound by two z is empty both in the hit and at the corresponding positions in the second structure, then it is identified as Disorder. If the 'z___z' is corresponded by a PB sequence in the counter-alignment, it is identified as a DPF. When the regions in both the pairs of alignment have a complete pb sequence, it is treated as order. Thus, in the above example, there are 1 disorder, 3 DPF (2 same strand, 1 on second pb strand), and 4 Order states. Rest of the annotations are extracted using the demarcations provided by PBs.

Figure 3b . 3

 3b3 Figure 3b.3 Length distribution of DPF across the dataset. The histogram shows the length of different DPFs identified on x-axis. The y-axis depicts the raw number of occurrences. The lengthiest DPF identified is 139 residues long while most DPFs have a length of 3 residues. The distribution is skewed towards smaller DPF lengths.

Figure 3b . 4

 3b4 Figure 3b.4 Number of DPF per structure. The histogram shows the number of DPFs per structure on x-axis while y-axis shows the raw occurrences. Mostly, there is a single DPF per structure but there can be 2 to 3 DPFs per structure. The most number of DPFs in a structure, in the dataset is 13. Only one structure have 13 DPFs in one pairwise alignment-2XVN:C and 4B3L:F Figure 3b.5 shows the amino acid distributions of all the 3 states of structural continuum (order, DPF, disorder) in one plot along with earlier trends as observed by DeForte and Uversky in 2016 [315]. These trends help understand the preferred amino acid propensities of the three structural states. A region consisting of high frequency of C, G, D, and K with low R, T, V, I frequencies can be a potential DPF. The DPF favored residues are a mix of hydrophobic and hydrophilic tendencies while except for Cys, rest are flexible. These can explain the diverse nature of DPF with some regions being found in membrane proteins and others in globular proteins. More flexibility is needed for maintaining different interactions.

Figure 3b . 5 3 . 3

 3b533 Figure 3b.5 Amino acid distributions in Order, DPF and Disorder. The plot shows three normalized frequency values (absolute) for each amino acid on x-axis. The gray bars represent Disorder, the red circles represent Order and blue circles represent DPF. The width of the circles is proportional to their normalized frequency value. C, G, D, E, K are the most abundant amino acids observed in DPF regions. Below the plot can be seen a legend with two amino acid scales for hydrophobicity and flexibility based on Kyle and Doolittle hydrophobicity and Vihinen's flexibility scales. The color scheme in the legend depicts order or disorder promoting residues. Please note that in legend the blue color represents disorder while in plot, blue represents DPF and gray represents Disorder. + Image graphic in lower portion is taken from [315]

  [START_REF] Jalkanen | Amino Acids and Small Peptides as Building Blocks for Proteins: Comparative Theoretical and Spectroscopic Studies[END_REF]. However, DPF relatively have more PBs k and l which ideally represent N-caps of an α-helix but will also qualify for β-turns since they also lie at terminus of α-helices. Both the states, DPF and order, have significant representation of β-sheets (PB c, and d) but order have more β-sheet character. A major difference is seen in the frequencies of PB f. DPF consists of more PB f conformation which vaguely resembles a β-sheet's C-cap but also resembles closely with β-bulges. Bulges are found located at the ends of antiparallel β-sheets and are important players in flexibility of the local structures. Contrary to the observations made with secondary structure distribution, β-bulges are prominently seen in DPF than in order. Thus making them a characteristic of DPF along with β-turns type IV1 and type IV3.

Figure 3b . 6

 3b6 Figure 3b.6 Secondary structure distribution between DPF and order regions. The x-axis represents the various secondary structure motifs like; different types of helices, β-turns, ɣ-turns, bulges, PPII, Bend, β-bridge, Extended strand and coil. These The y-axis represent the normalized frequencies of each secondary structure in DPF (blue) and Order (red). Helices and turns are collectively the most dominant secondary structures in DPF.

Figure 3b . 7

 3b7 Figure 3b.7 Protein blocks distribution of DPF and order regions. The x-axis represents the 16 PBs. The y-axis represents the normalized frequencies of each PB in DPF (blue) and Order (red). PB m is abundant in both the cases. While DPF has more representation of PB f, k, and l, Order regions have more number of sheets (PB c, d) besides having high helical content with PB m.

Figure 3b . 8 3 . 5

 3b835 Figure 3b.8 Normalized B-factor distribution of DPF and Order. The blue color represents the distribution of B-factors for DPF regions and red colored distribution represents B-factor values for Order. They both have a gumbel distribution with Order having slightly higher spread of data.

Figure 3b . 9

 3b9 Figure 3b.9 Relative solvent accessibility distribution for DPF and Order. The blue colored box represents the distribution of rSA for DPF regions and red colored boxplot represents rSA for Order. Overall, the range and spread of rSA for Order is smaller than DPF. It has very low Q1, around 2 that signifies deeply buried residues. While most of the distribution for DPF lies above 10% accessibility, i.e. all of the residues in DPF regions are accessible than compared to first quartile of Order having very low rSA.

  While studying the impact of modifications on protein backbone, certain long range interactions were observed. Such interactions are delocalized, i.e the point of impact is structurally distant from the region of the observed effect[328]. Usually, such long range interactions occur in multidomain proteins which goes through structural transitions essential for their regulatory functions[329].Transitioning of a structure from one structural state to another involves the changes in flexibility at a large scale. However, the driving forces during such transitions are the local structural regions.Subtle changes in these regions accumulates into large transitioning effects. Therefore, the next objective is to understand the behavior of structural flexibility in long range interactions in a multidomain protein. One of our close collaborator Dr. Vincent Jallu from the Platelet Lab, INTS, works with Integrins αIIbβ3 proteins that are implicated in rare bleeding disorders like Glanzmann Thrombasthenia (GT) and Fetal Neonatal Alloimmune Thrombocytopenia (FNAIT). The Integrin protein is a multi-domain heterodimer that is expressed on the platelet cells. It undergoes structural transition from closed to open state upon activation to bind with clotting factors and aggregate.Thus it plays an important role in the clotting pathway. During, GT the defects in Integrin αIIbβ3 leads to the failure in transition that results in absence of clotting, thus the patient can bleed to death in event of an injury. Besides, Integrin αIIbβ3 are also involved in FNAIT, another thrombocytopenic defect occurring in neonatal or fetal stage. It arises due to polymorphisms of amino acids. The polymorphism can cause the expression of Human Platelet Antigen (HPA) in embryo. If the mother lacks the antigen, her placental immune system will generate antibodies against HPA that will lead to destruction of platelet cells. Due to its structural properties and pathophysiology, Integrin αIIbβ3 becomes the protein of interest for studying local structural dynamics in multi-domain complexes.4.1.1 IntegrinsIntegrins are composed of a large family of heterodimeric complexes involved in cell adhesion that are expressed in different cell types. The heterodimer comprises of two large non-covalently associated, single-span type I transmembrane α and β subunits comprised of approximately 1000 and 800 residues respectively[330]. In humans, the integrins protein superfamily consists of 24 heterodimeric receptors resulting from different combinations of 18 α and 8 β subunits.

Figure 4

 4 representation of integrins superfamily and different kinds of integrins. The extracellular domain (ectodomain) of integrins comprise of recognition sites for extracellular matrix proteins and counter receptors. The specific binding of ectodomain to such extracellular proteins and receptors lead to aggregation, cell-matrix adhesion and cell-cell adhesion, respectively [330]. While on the intracellular side, short C-terminal cytoplasmic domains link ectodomain to the cytoskeleton. Thus leading to bidirectional transmission of force through single span type I transmembrane helices. Therefore, integrins functions are crucial to embryonic development, tissue repair, host defence, homeostasis as well as haemostasis.

Figure 4 . 1 .

 41 Figure 4.1. Family schematics of Integrins. Integrins are family of proteins involved in adhesion and aggregation functions. An Integrin is identified by its heterodimeric structural assembly comprising of an alpha and a beta subunit. Overall, there are 18 α and 8 β subunits which are shown here. Based on the subunits involved, Integrins are classified into collagen receptors, Laminin receptors, Leukocyte specific receptors and RGD (Arg, Gly, Asp) receptors. Integrin αIIbβ3 is a member of RGD receptors. Two β subunits (β1 and β7) bind to α subunits across classes. + Image taken from [330]

  primary haemostasis and thrombus formation[333]. Integrin αIIbβ3 has been shown to get activated by both outside-in and inside-out signaling[334]. Studies have shown that elevated levels of cytoplasmic Ca +2 leads to the binding of Talin protein on the cytoplasmic domain of β3 subunit. This binding causes allosteric changes in the ectodomain that transitions from closed to open state conformation thus making it activated to bind with fibrinogen or vWF. Multiple integrin αIIbβ3 aggregates upon a fibrinogen leading to thrombus formation.

Figure 4 .

 4 2 shows a diagram of the inside-out signaling of integrin αIIbβ3 [334].

Figure 4 . 2 .

 42 Figure 4.2. Inside-out signalling in Integrins. The image depicts the inside out signalling in Integrin αIIbβ3. First Talin is activated due to binding of Thrombin at PAR1 receptor. Talin then binds to cytoplasmic tails of β3 subunit that causes allosteric changes in the propeller domain that contains RGD. These events lead to opening of the structure.

Figure 4 . 4 . 1 . 3 . 1 4 . 1 . 4 4 . 1 . 5

 44131414415 3 depicts transition steps from the inactive conformations of αIIbβ3 (crystallized closed structure, PBDid: 3FCS) to its theoretical open liganded active form. A complete structure of the open forms of the ectodomain with or without ligand remains to be crystallized. The ectodomain is further divided into four regions: headpiece, knee, legs and tails. Headpiece: The headpiece that carries the ligand-binding site consists of the β-propeller domain of αIIb subunit and the β-I domain of β3 subunit. The αIIbβ3-propeller domain consists of a 7 bladed fold with four Ca 2+ ions coordinated with β-hairpin loops connecting the antiparallel βstrands (see Fig 4.3A). The β-I domain of β3 mainly consists of α helices and loops with coordinated metal ions Ca 2+ and Mg 2+ constituting a MIDAS (Metal Ion Dependent Adhesion Sites) with an ADMIDAS (Adjacent to MIDAS) and SyMBS (Synergistic Metal Binding Site).These sites play critical role in opening the αIIbβ3 binding site and helps in ligand binding[335].αIIb Leg and the Knee: Downstream the β-propeller is the αIIb leg, composed of the Thigh domain, the Genu (knee), the rigid Calf-1 and Calf-2 domains. The short loop of αIIb Genu coordinates with a divalent calcium[336]. This metal ion might help in stabilizing Calf-1 and Thigh domain interface during the opening of the structure following the activation process (angular shift at Genu). The αIIb leg is rigid and provides a framework to the entire ectodomain. grey dots) approaches the glycoprotein. All metal ions are shown as solid spheres with golden ones representing Ca 2+ while green representing Mg2+ . Polysaccharides (N-acetyl glucosamines and Mannose) are shown in ball and stick representations. Please notice that the open forms had been modelled from the closed structure according to expected conformations using Modeller_v_9.16[START_REF] Sali | Comparative protein modelling by satisfaction of spatial restraints[END_REF] and images are generated by PyMol_v_1.7.0 [352]. Structural transition from bent to extended state The activation state of αIIbβ3 controlled by inside -out signalling results from platelet activation by multiple exogenous factors (physiological plasmatic agonists, exposed subendothelial matrix) leading to the binding of Talin at β3 cytoplasmic tail. The αIIb headpiece opens up creating an angular shift between Thigh and Calf-1 domains (Fig 4.3C) meanwhile, the β3 leg and tail remains parallel to the αIIb leg. Thereafter, opening of the β3 headpiece pulls the β3 legs outward resulting into an extended open conformation (Figs 4.3D and 4.3E) that can bind plasmatic fibrinogen at the MIDAS, which is constituted by elements of both the headpieces [337]. Finally, fibrinogen binding leads to outside-in signaling and directing the platelet cells into close proximity of other platelets. Multiple platelets expressing αIIbβ3 binds to fibrinogen thus forming a thrombus leading to clot formation. Defective expression of integrin αIIbβ3 Upon activation the integrin αIIbβ3 binds plasmatic fibrinogen leading to platelet aggregation. However, a defect in the expression of αIIbβ3 or failure to open up or specific mutations can have disruptive results. Defective platelet aggregation leads to two severe life-threatening bleeding disorders: Glanzmann thrombasthenia (GT) and Fetal / neonatal alloimmune thrombocytopenia (FNAIT). GT is a rare autosomal recessive genetic disease associated with defective expression and / or function of αIIbβ3 [338] while FNAIT results from fetal / neonatal platelet destruction by maternal alloantibodies in mothers lacking the fetal platelet alloantigens inherited from the father. Clinical consequences of FNAIT range from no symptoms to intracranial hemorrhages with a risk of neurological sequel and/or fetal/neonatal death [339]. Both diseases result from αIIb and β3 gene polymorphisms.In GT, more than 300 mutations have been identified in αIIb or β3 genes. Most of them are reported in GT specific database: https://sinaicentral.mssm.edu/intranet/research/glanzmann. These mutations have distinguished effects on the αIIbβ3 phenotype. Many missense mutations cause defective expression of αIIbβ3 on the platelet cells. While certain silent mutations do not affect the phenotype instead can change the allosteric propagation of the transition sequence leading to lack of affinity for fibrinogen. However, in FNAIT, neither the expression nor function of αIIbβ3 is affected but single nucelotide polymorphisms (SNP) resulting in amino acid (aa) variations lead to sequence that defines Human Platelet Antigens (HPA). The effects of these amino acid substitutions on αIIbβ3 structure remain largely unknown. Most of the human platelet alloantigens are described in the HPA database http://www.ebi.ac.uk/ipd/hpa. Domains of interest Given the enormous size of the ectodomain of integrin αIIbβ3 and high number of mutations for GT and FNAIT pathologies, it seems logical to study one or two domains at a time. Previously, the collaboration with Dr. Jallu on FNAIT and GT have been fruitful. Using closed state ectodomain crystal structure from PBDID: 3FCS, it was shown that the β3 Lys253Met GT mutation impaired key ionic interactions between the αIIb β-propeller and the β3 β-I like domain[340]. Nonetheless, static models cannot depict all mutation-induced effects on a highly dynamic structure like integrins. Therefore, molecular dynamics (MD) simulations were used to study L33P substitution located in the PSI domain of β3 subunit. The L33P substitution is responsible for the HPA-1 system, clinically the most important one in Caucasian populations[255]. The dynamics of structures of PSI domain as L33 and P33 variants was compared to find that the mutation does have an impact on the conformations[255,341]. Later, a third variant with a Valine at position 33 (L33V) was also studied along the L33P mutation[341]. Although the 3 variants mostly shared common conformations, the P33 variant showed a higher mobility and specific conformations of IEGF-1, IEGF-2, and PSI domains. As shown in Figure4.4, the L33V substitution mainly displaced a dynamic equilibrium between common structures that could explain a variable reactivity of different anti-HPA-1a sera with the two β3 forms[341].As discussed in section 4.1.3 that the leg region of the αIIb subunit plays anchoring role during the structural transition and is known to consist the most rigid domains of the αIIbβ3 structure. However, Nussinov lab have discussed multiple times that rigid domains can have underlying allostery[342,343]. Therefore, the apparent rigidity of the leg domains, Calf-1 and Calf-2 became our domains of interest.

Figure 4 . 4 .

 44 Figure 4.4. Most frequent structures in L33-β3 and V33-β3 of residues 27 to 31 and 435 to 438. Secondary structures (light green) of the β3 knee of L33 and V33 variants are shown. Worm lines correspond to loops or extended conformations, arrows to β sheets, and ribbons to α-helices or βturns. Left panel, the dominant structure formed with residues 27-31 (grey) and 435-439 (red) adopting PB sequences 27bfkbc31 (loop) and 435fklmm439 (β-turn). The side chain of residue E29 is colored in blue. Right panel, one of the minor competing structures adopting extended conformations (shown here PB sequences: 27bdfbc31 and 435cbfbc439) for L33-β3 and in a lesser extent for V33-β3. The grey double arrows visualize for L33-β3 and V33-β3 the balance (and frequencies) existing between the dominant structure and all the minor structures whose only one is shown here-; (the thickness of the bar is proportional to the frequency). + Image taken from [341]

Figure 4 . 5

 45 Figure 4.5 Calf-1 and Calf-2 domains demarcation and structural organization. Shown are the isolated individual domains A) Calf-1 and B) Calf-2. Both the domains have a beta sandwich fold with anti-parallel running beta strands connected through loops. Calf-1 has a small missing region between β4 and β5, colored in gray. Calf-2 has two big regions of missing atomic coordinates, as marked by β1 -β2 (11 residues) and β5 -β6 (34 residues).

Figure 4 .

 4 Figure 4.5B. The C-ter of Calf-2 is binded with the single spanning TM-helix of αIIb subunit.

2 .

 2 The selected templates, 4G1E and 4NEH were used exclusively to model the missing regions.While the overall scaffold of the Calf-2 domain is provided by the αIIbβ3 self-template structure of 3FCS. Thus three template structures are used having PBDids; 3FCS (αIIbβ3), 4G1E (αVβ3), and 4NEH (αXβ2). However, the generated model with best DOPE score did not have convincing conformation for the 34 residue missing region. It modelled it as a highly disordered loop (based on the loop conformation in all the 100 models) which exhibited self-interactions. Given that the loop consists of FNAIT variants that will lead to expression of HPA and that the loop might interact with IEGF domains of β3 subunit, the loop confirmation is unacceptable. Therefore, based on the shorter yet complete loop structure of the leukocyte specific integrin, αXβ2 (4NEH) and the very small structural distance between G840 and Q873, structural constraints are designed for the missing region. Each 5 th residue in the loop should have a distance of 10Å while two 20Å distance restraints are put between 840 th to 850 th residue and 863 rd to 873 rd residue. The principle schematic of the restraints is shown in Figure4.6A. The principle of such structural restraints is to avoid the self interaction of the loop and an expanded conformation given its interactions with IEGF domains.Thus the final selected model has two loops and both exhibit small helical component at the most distal part of the loops, Fig 4.6B. Short molecular dynamics of 50 nsec confirmed the stability of the loop, although the loop 7 is highly more deformable.

Figure 4 . 6

 46 Figure 4.6 Completing the missing regions in Calf-2. A) schematic of restraints applied. B) the modelled loops based on the structural restraints.

Figure 4 . 7

 47 Figure 4.7 Comparison of the protein flexibility of Calf-1 through different metrics. 3D structures of Calf-1domain represented through (A) B-factor values, (B) RMSF values, and (C) N eq values. Local structure is ranked from rigid (thin blue line, a value of 0.0) to flexible (thick red line, a value of 4.0). Residues with completed missing atoms are in grey in the B-factor cartoon (A). (D) The Calf-1 amino acid sequence is placed in regards to its secondary structures assignment and to protein flexibility according to the B-factor, the RMSF or the Neq values. Blue, green, yellow, orange and red colours scale the structure from rigid to flexible. The loops are:

Figure 4 . 8 4 . 3 . 3

 48433 Figure 4.8 RMSD curves of the WT form of Calf-1 domain. Shown are curves of the 5 MD simulations performed for 50 nsec. All curves converge at 25000 picoseconds to reach a steady state.

  Figure 4.7[B to D] also indicate a good correlation between RMSF and Neq values. Indeed, highest Neq values are associated to flexible regions (as defined by B-factor and RMSF) with residues K678-T682 (loop 6) and N709-E712 (loop 8), but also with T619 (loop 2). Expectedly, some regions show higher Neq for some residues; G641-G643 (loop 3) and S728-N730 (loop 9). On the other hand, highly flexible region can also represent high local rigidity in terms of PBs, for instance, residues V666-F669 and E670 in loop 5(Fig 4.7C and 4.9). 4.3.3.1 Flexible yet rigid: Resolving ability of Neq Direct comparison of RMSF and Neq values (Fig 4.9A) clearly shows that E667 represents a high RMSF but a low Neq. This can be explained by its PB distribution (Fig 4.9B): E667, G668and F669 representing the highest RMSF values (and also B-factors), mainly adopted the PB sequence "hia" with respective occurrences of 86.2, 82.9 and 61.6%. A series of PB "hia" is a classical loop conformation but this region (in blue rectangle on Fig 4.9C) maintains a single conformation and is not really flexible. This apparent discrepancy can be explained by the insertion of the rigid stretch E667-F669 in a larger flexible (or more precisely deformable) loop N665-L672.Interestingly, the results reveal that a locally rigid aa stretch (few possible conformations/low Neq) can be a part of a large mobile loop involved in the global structural motions of the protein (high RMSF). Overall, the results show a good correlation between experimental data (B-factor), RMSF and Neq obtained from MD simulations. Although some discrepancies did exist, they are explained by local structure singularities. As expected in an all-β domain, rigid β-strands are linked by flexible loops.

Figure 4 . 9

 49 Figure 4.9 Local rigid conformation in a deformable loop, low Neq versus high RMSF. (A) Superimposed RMSF and Neq values (red and blue curves respectively) from residues N665 to G668, (B) The WebLogo 49 indicates the frequency of occurrences with respect to the PBs adopted (size of the letter) by a residue in MD simulations. Here, residues V666 to F669 mainly adopted the PBs profile "ehia" corresponding to low Neq for them. (C) 3D model of the Calf-1 domain and the frame magnified of two adopted by the loop conformations (red and yellow worm-lines) carrying the residue E667 (in blue) that keep a rigid structure relative to the mobile loop.

  Each system reached a plateau after 5 nsec with an average RMSD of 2Å (beginning of loop 1 and end of loop 10 excluded). All energetic and geometric parameters show a good evaluation for the 70 different simulations used in this study; no clashes are found. The Calf-1 domain stays consistent during the whole dynamics. Average RMSF from each variant and the WT were comparable (Fig 4.11). The most important variations observed in loop 2 (V625), loop 5 (E670), loop 8 (A713) and loop 9 (N732) did not lead to disordered patterns. Some variants showed specific higher or lower RMSF for some restricted positions like for C674R and L721V variants (Fig 4.11).

Figure 4 . 10

 410 Figure 4.10 Ribbon model of the Calf-1 domain showing the location of the studied variant residues. Strands are coloured in green and loops in yellow. Variant residues are identified as red balls. N-ter and C-ter ends are shown as yellow balls.

Figure 4 .

 4 Figure 4.11 Calf-1 RMSF of the different systems. By comparison, Calf-1 variant structures mainly behaved like the WT form (black curve). The noisy peaks for the N-ter first residues were discarded in the majority of the analyses since, in nature they lay at conjunction to the neighboring domain.

(Fig 4 .

 4 12A, B). S621 is located at the opposite side of the domain in reference to residue 724 (Fig 4.12E). In the variant structure, S621 mostly remained in a PB d (i.e., β-strand) conformation with however, a decreased frequency of occurrences. Besides, downstream P622 and L623 presented some lost conformers with increased frequencies of PBs e and h respectively. Very few typical backbone -backbone interactions of S621 with L623 and backbone -side chain interactions with N629 are replaced by a single bond between side chains with N629. Adding to this high mobility, S621 did not do consistent and sustainable interactions. This behavior is amplified in the Q724 variant with the most stable residue S621 in a naturally flexible region (loop 2), became one of the most deformable positions. 4.3.5.2 L653R This GT variant results from a L653R substitution in loop 4. The highest Neq variations (Fig 4.13A) affected residues G620 -P622 (loop 2), V630 -L631 (β-strand 2), E646 (loop 3), R671 (loop 5) and L710 (loop 8). As observed with the R724Q substitution, residues G620 -L623 gained slightly more flexibility. Conversely, residues L624 -D628 shows increased flexibility but with a limited impact (average ΔPB = 0.23) on the most frequent PBs (PB e for L624, h for V625 and i for G626 in Fig 4.13B). The mutated residue in position 653 (loop 4) is not subjected to any Neq modification. It conserves a strong local structural stability (Fig 4.13C) similar to its direct environment.The PB series at this position "dddeh" is even slightly more common in the variant than in the WT (64% and 59%, respectively).In the R653 variant, the 8 hydrophobic bonds of L653 disappeared in favour of new interactions between the R653 backbone and A657 and E676 side-chains (Fig 4.13E). The backbonebackbone interaction with R683 remained conserved. The mutation zone showed no conformational change as the loss of important specific interactions were partly compensated by new ones. Of the 9 original interactions only 1 is conserved while 3 new are created.

Figure 4 . 13

 413 Figure 4.13 The variant L653R. Panels (A and B) respectively show the Neq and the ΔPB curves of the L653 (WT) and R653 (variant) systems. Panels (C, D) respectively show the PB maps of residues 651 to 655 and 677 to 681 with the WT at the left and the variant form at the right. (E) Molecular interactions made by the residues L653or R653 and (F) by E679. For colour scales and residue presentation, see the legend of Figure 4.12.

Figure 4 . 14 4 . 3 . 5 . 3

 4144353 Figure 4.14 The variant C674R. Panels (A and B) respectively show the Neq and the ΔPB curves of the C674 (WT) and R674 (variant) systems. Panel C show the PB maps of residues 669 to 676 for the WT (above) and the variant forms (below). (D) Molecular interactions made by the residues C674 or R674 and (E) R671. For colour scales and residue presentation, see the legend of Fig 4.12.

Figure 4 . 15

 415 Figure 4.15 Comparative study of the ΔPB values for all WT-variant pairs. Histogram schema presenting the ΔPB value computed for each residue position (abscissa) from each variant and the WT systems. Green triangle indicates the aa variation position while the purple one position shows the position of maximal ΔPB. Residues from loops 2, 3 and 8 presenting common high ΔPB for all variants are boxed.

Figure 5 .

 5 Figure 5.1A. Plasmodium vivax has a high morbidity rate in the developing countries of south-east Asia, southern America and, Africa with upto 140 million cases of Plasmodium vivax malaria per year [361]. Of these 80% of infections are reported from Asia and south America, while only 12.4% are acquired in Africa. The populations of western sub-saharan Africa are resistant to Plasmodium vivax infections, Figure 5.1B [364]. This is attributed to a silencing mutation that selectively abolishes the expression of Duffy Antigen/Chemokine Receptor (DARC) on erythrocytes (Red Blood Cells).

Figure 5 . 1

 51 Figure 5.1 An affair of Plasmodium vivax and ACKR1. A) shows the endemicity of the Plasmodium vivax infected malaria with red depicting highest and cyan depicting lowest no. of cases. Most of the incidents are reported in the tropical and sub-tropical climate zones, except most of African continent. B) depicts the spatial distribution of Duffy negative population across the world. Ranges are shown as gradient of color red with pink being negligible while more red indicated more Duffy negative population. As can be seen from C) that the Duffy negativity totally complements Plasmodium vivax malaria trends, especially in African continent.+The images are generated from the Malaria Atlas project (https://map.ox.ac.uk/).

5. 1 . 2

 12 Plasmodium vivaxPlasmodium vivax, like other plasmodium parasites, have a dual phased, digenetic life cycle. The sexual cycle or schizogony is carried out in the host, female Anopheles ending in the generation of sporozoites[365]. The blood meal of the mosquito on human commences the asexual cycle or sporogony. This culminates with the production of gametocytes that are sucked by the host mosquito during blood meal. While in the asexual phase, the Plasmodium vivax merozoites undergo two stages of development in liver and three stages of infection in erythrocytes called erythrocytic cycle. Malarial merozoites must invade erythrocytes to begin the infection stage and this makes the invasion a critical step in the life cycle of Plasmodium vivax. A bottleneck in the merozoites entry process is the interaction of their micronemes with a septa-helical transmembrane, glycoprotein on erythrocytes known as DARC. However, in liver, the merozoites can also enter a dormant stage called hypnozoites. Figure 5.2 shows a schematic representation of Plasmodium vivax life cycle.

Figure 5 . 2

 52 Figure 5.2 Life cycle of Plasmodium. Parasites of Plasmodium genus have digenetic life-cycle. The sexual part of the life-cycle is carried out in vectors, mostly mosquito. The infectious stage is executed in the host cells, mostly Humans. After a blood meal the parasite is transferred to the host via salivary glands. The parasite multiplies in the hepatic cells but is asymptomatic and

+

  Image credits to Stephan Kappe, PhD, University of Washington 5.1.3 An introduction to DARC Duffy Antigen / Chemokine Receptor is a minor blood group antigen that expresses Human alloantigens, Fy a and Fy b in its N-terminal extracellular domain [366]. It was discovered in western Africa and is allegedly named after the individual whom it was discovered in [367]. Its official denomination is Atypical Chemokine Receptor 1 (ACKR1) while alternatively it had been previously termed as Fy glycoprotein (Fy) or Cluster of Differentiation 234 (CD234). ACKR1 is encoded by a single copy gene, DARC, located on chromosome 1 [368]. DARC gene exists as two co-dominant alleles Fy a and Fy b arising due to a base mutation, G125A [369]. In Fy a , the mutation in the 42 nd codon leads to the encoding of glycine while in Fy b it encodes aspartic acid, described by polymorphism-G42D [370]. These alleles are immunologically distinct and therefore would result into four Duffy blood group phenotypes: Fy a+b+ , Fy a+b-, Fy a-b+ and Fy a-b-. Some minor phenotypes have also been characterized namely, Fy3, Fy4, Fy5, Fy6 and Fy x (weak expression of Fy b ) [371]. Fy a-b-also called Fy-null phenotype, arises due to a polymorphism in Fy b at 46 th nucleotide (T46C) in the erythroid regulatory element of the DARC promoter region. The mutation leads to the disruption of the binding site for erythroid transcription factor, GATA1, in erythrocytes derived from hemopoietic lineage [372], as depicted in Fige 5.3. Therefore, the Duffy -ve or Fynull blood group phenotype would abolish the expression of ACKR1 on erythrocytic membrane. While, the Duffy -ve individuals can have ACKR1 expressed on the endothelial lining of postcapillary venules, epithelial cells of renal collecting ducts [373] and Purkinje cells [374]. Since Plasmodium vivax merozoites invade reticulocytes by interacting with ACKR1 (DARC), the duffy -ve population can avoid this parasitic invasion thus making them resistant to Plasmodium vivax infected malaria, Fig 5.1C.

Figure 5 . 3 1

 531 Figure 5.3 Disruption of GATA-1 box. A schematic description of the expression of DARC gene in normal phenotypes (left) and in Duffy -ve phenotypes (right). In the promoter region of the gene, the SNP T46C alters the recognition site of GATA-1 thus leading to loss of expression of DARC gene. The SNP is predominant in the Fy b allele lineage that expresses DARC on reticulocytes.Therefore, loss of DARC on RBC, will hinder the interaction with Plasmodium vivax DARC binding proteins.

Figure 5 . 4 .Figure 5 . 5 .

 5455 Figure 5.4. ACKR1. A) a schematic representation of ACKR1 (uniprot ID: Q16570) highlighting the important epitopes for blood Fy antibodies. The SNP that leads to Fya and Fyb allele lineages is also shown. The conserved cysteines that forms the disulfide bridges are highlighted in green. The intracellular loop 2 is labelled as ICD2 and as seen, DRYLAIV motif is missing. B) shows a tentative model structure of ACKR1 monomer that shows the structural placement of conserved motifs and epitopes. G-proteins natural docking site is also shown but as ACKR1 lacks the DRY motif, G-proteins do not couple. + Image credits: A) taken from [374], B) adapted from [430]

Figure 5 . 5

 55 Figure 5.5 Chemokine system signalling. The cartoon picture shows the various pathways that can be triggered by chemokine binding to a chemokine receptor. Shown are the chemokine receptors, CXCR3, CXCR4, a dimer of CXCR4 and CXCR7 (ACKR3), and CXCR7 (ACKR3). All these receptors can be activated by either CXCL12 or CXCL11. The signalling of ACKR3 shows no Gαi signalling as it has variations in the DRY motif.

5 . 1 . 4

 514 [384]. The physiological function of ACKR1 in erythrocytes is yet unclear. As it binds to 20 chemokines of different types, it supposedly functions as a scavenger and regulate the inflammatory pathways[385][386]. Due to lack of an active response to chemokine binding, ACKR1 is also termed as a silent or decoy receptor. In non-erythrocytic cells like those in venular endothelium, cerebellar neurons and Purkinje fibers, ACKR1 is expressed by the allele Fy a[373,387]. In endothelial cells, it mediates chemokine transcytosis. Wherein, ACKR1 internalizes the chemokines and migrate them from luminal to extravascular space, to induce leukocytes migration and thus regulate inflammatory response[388]. While the physiological function of ACKR1 in erythrocytes is still under consideration, it plays a critical part in entry of Plasmodium vivax micronemes[389]. It has been shown that DARC dimerizes during its interaction with Plasmodium vivax proteins. This makes ACKR1 a.k.a DARC, a protein of interest for our case study.5.1.3.3 Structure of ACKR1A decoy receptor like ACKR1 that is also the point of entry for Plasmodium vivax in Humans, is indeed an intriguing case of host-pathogen biology. Thus, enriching literature is available about the cell biology of ACKR1 and Plasmodium vivax DARC Binding Protein (PvDBP). Numerous studies also identifies critical residues and motifs involved in both the proteins [390]. However, scarce information is available about the molecular structure of ACKR1 while structure of P.knowlesi DBP was published in 2005 by Singh et al, highlighting the crucial insights into DARC -DBP interactions [390]. The lack of structural information can be attributed to the challenge of crystallizing membrane bound proteins like GPCR. Currently, in PDB, 115 crystal structure of GPCR proteins are available [391], of which only 20 have a resolution lesser than 2.5 Å. This is indeed a small number for popular drug targets like GPCR. Availability of only 115 of ~800 GPCR proteins also estimates the challenges of experimental structure determination for membrane bound GPCRs. Chemokine receptors are Class A GPCR and have only 8 structures available in PDB with only 1 qualifying the resolution and r-free value thresholds. Homology modeling of DARC In the absence of an experimentally determined structure of DARC, it is logical to perform homology modelling to study the structural aspects of DARC. It is mentionworthy that our group have generated a structural model for DARC in 2005 and is the only one since then. It has a monomeric assembly modelled on a template from a very distant relative (sequence identity 12%) belonging to Rhodopsin family (Bovine Rhodopsin, PBDid: 1F88:A) [241]. With technological advancement in molecular biophysics, there is much more information available about chemokine receptors than in 2005 [377]. Therefore, we decided to remodel DARC using comparative modeling coupled with knowledge based restraints. In the last decade, evidence of oligomerization of chemokine receptors have gained enormous support. It has been reported that chemokine receptors often exist as homo-or heterodimers as well as oligomers with members outside chemokine receptor family [377]. Chemokine receptors have conserved 7-TM helices and a variable length N-terminal domain, ECD1, which is mostly disordered [392]. The disorder in ECD1 accounts for the functional diversity along with promiscuous binding network of chemokine ligands with chemokine receptors [377]. However, failure of drug candidates due to nonspecificity towards target chemokine receptor leads to the notion of redundancy in chemokine receptors. In the last decade, these notions have been critically challenged by reports of oligomerization in chemokine receptors that diversifies the functional spectrum of the family [377]. There are profoundly three types of oligomeric structures in chemokine receptors: CC oligomers, CXC oligomers, and heteromers formed predominantly by the members of either CCRs or CXCRs and with other TM receptors [377]. However, an XCR and two CX3CR also exist. The major difference among chemokine receptors is among the sequential difference between cysteines and a tyrosine residue that can undergo sulfonation [393]. The dimerization of chemokine

  was shown interacting with either monomer(2:1) or dimer(2:2) of DARC based on which it is termed as trimeric or tetrameric. ITC (Isothermal Titration Calorimetry) results postulate the trimeric assembly as an intermediate structure in a stepwise binding process. Thus proposing a hypothesis that PvDBL homodimerizes under the effect of ACKR1 as depicted in Figure5.6A.The structure provides crucial details about the interaction of ECD1 (DARC's N-terminus) and PvDBL but do not have coordinates for the transmembrane structure or other interface and noninterface domains. The 2.6 Angstrom structure shows the dimerization of the regions of two distinct PvDBL namely PvDBL-RII (cysteine rich region II) and dimerization of ECD1 of DARC.Since the interaction takes place at the central region of DARC's ectodomain (ECD1) therefore, only residues 19-30 have sufficient electron density, Figure5.6B. This also validates that the Nterminal ECD1 is indeed an IDR (Intrinsically Disordered Region) that acquires structure upon dimerization and interaction with Plasmodium vivax. The work of Batchelor et al, 2014 is highly pivotal in finding answers to the questions about homology modelling of DARC. Based on the findings above, DARC was decided to be modelled as a homodimer.

Figure 5 . 6

 56 Figure 5.6 Plasmodium vivax and ACKR1 interaction. A) shows the pathway followed by Plasmodium vivax DARC binding ligand (PvDBL) to bind with ACKR1 expressed on the reticulocytes of Duffy positive individuals. A monomer of PvDBL interacts with homodimer of ACKR1 that causes the dimerization of the PvDBL. Once dimerized, the N-terminal residues 19-30 of the ECD1 of ACKR1 docks irreversibly into binding pockets of PvDBL dimer. This is termed as a hetero-tetramer assembly (PBDID: 4NUV). B) shows the zoomed in version of the heterotetramer interaction. The otherwise disordered residues 19-30 of ACKR1 forms a well defined helix while rest of the ECD1 does not have definitive electron density.+ Image adapted from[389]

Table 5 . 1

 51 Three Tier Method for filtering template structures. All vs all structural comparison of tentative template structures is done using TM-align and ProFit. Tier 1: indicates the TM-scores of pairwise structural alignments. A TM-score above 0.8 indicates same family and a TM-score > 0.9 indicates highly similar structures (highlighted in green). Tier 2: indicates the RMSD in (Å). Half matrix is shown as the RMSD does not change with change of reference structure. The low RMSD values of the range 0.65 -4.97 are shown. 3ODU and 4RWS are potential templates given their overall low RMSD values. Tier 3: is the alignment coverage during the pairwise structural alignment. It helps in justifying the high structural deviation values in Tier 1 and 2.

  PPM server hosted online by OPM database that hosts the membrane orientations of 3426 TM proteins from PDB. The structural model was then submitted to the Membrane Builder of CHARMM-GUI [417]. The objective was to embed the structural model of DARC in a mimic reticulocyte membrane. The composition of the RBC membrane during reticulocyte stage as well as mature stage, was estimated by doing extensive literature survey. Following information was extracted about the RBC membrane:a) The physiological pH of the membrane ranges from6.3 to 7.9 [418]. b) Cholesterol (CHL) seems equally distributed between the inner and outer halves[419]. c) Phospholipids are asymmetrically distributed between the leaves and is crucial for red cell physiology[420]. Changes in membrane lipids can affect the RBC shape by perturbing the balance in inner and outer leaflet lipids[421,422].d) Fluidity of the bilayer is determined by molar ratio of cholesterol to phospholipids, degree of unsaturation of phospholipid acyl chains, and phosphatidylcholine tosphingomyelin ratio[423]. In reticulocyte membrane, since the phospholipid and cholesterol is synthesized from glycerol and acetate, their concentration are expected to be in nearly equal proportions[424]. e) While phosphatidylcholine (PC) forms highly fluid lipid regions, sphingomyelin (SM) induces rigidity [423]. f) PC and SM are located in the outer leaflet while; phosphatidylinositols (PI), phosphatidylethanolamine (PE), and phosphatidylserine (PS) occur mostly in the inner leaflet [425]. g) Most of all PC lipids are in the outer leaflet while most of all PS lipids are in the inner leaflet [422],[418]. h) Redistribution of membrane phospholipids may trigger clotting cascade [426].

  phylogenetic information is used. A phylogram for 21 Human chemokine receptors was generated as shown in Figure5.7. DARC (mentioned as ACKR1 in the tree), has the longest distance from the root at 5.97 branch length units. Only one receptor comes closer to DARC with 5.20 units distance from the root, GPR35 or potentially CXCR8[427]. However, GPR35 does not belong to the same clade as DARC. The distance of DARC from its nearest clade is 4.92 units. According to the tree topology, CCR10 seems closest to DARC, in the clade. Interestingly, the distance of CCR10 from DARC is 3.10 units which quantifies the observation that there is huge evolutionary gap between DARC and its closest neighbor. This observation also intensifies the curiosity in the evolution of DARC which is handled later in details (see chapter 6). Based on the branch lengths and tree topology, CXCR3 (3.97 units away from DARC), CXCR5 (3.70 units away) and CXCR4 (4.16 units away) are the next closest neighbor of DARC, in that particular order. Of these, only CXCR4 has a crystal structure available in PDB. Since, CCR10 is the closest neighbor of DARC and the crystal structure of CCR5 is known, the proximity between CCR10 and CCR5 was also tested. Unfortunately, CCR5 is 1.42 units and seven clades away from CCR10 while it has a distance of 5.52 units from DARC, see Figure5.7. Therefore, CXCR4 clearly qualifies as the selected template for modelling a structure for DARC.

Figure 5 . 7 5 . 3 . 2 Figure 5 . 8 .

 5753258 Figure 5.7 Phylogenetic placement of ACKR1. The tree topology as generated by IQtree. The green colour of branches indicates high bootstrap values (of 1000). The branch lengths are indicated on the branches approximated to two decimal places. The green coloured labels indicate members belonging to the clade of ACKR1. The green asterisks mark the availability of structure in PDB. As observed, ACKR1 is highly distant from rest of the clade at a branch length of 3.66

Figure 5 . 8

 58 Figure 5.8 Structural model for ACKR1. With the help of T20 approach for model selection, best model was selected and validated. A) The important cysteines are found to be conserved and forms the disulfide bridges (Cys in red and S-S bond is shown in orange). The terminals are shown in black, ball and stick models. ICD2 (loop that contains DARC) is shown in grey colour. B) shows the top view of the dimer with respective helices marked. The orientation symmetry of the helices is mirrored and the ECD1 seems to move towards each other, as expected. The top view also shows the open interface and binding pocket for the ligand. C) Bottom view of the dimer shows the inwards tilting and gives a better perspective on the orientation of the ICD2 (grey).

Figure 5 . 9

 59 Figure 5.9 Structural comparison between old and new models of DARC. A) Superposed structural model of DARCold (yellow) and ACKR1monomeric model (green). The gray color of the ECD1 and ICD4 signifies that these were not included in the alignment using TM-align. B) Superposed structural models of ACKR1 monomer (green) and more extended of the DARCold models. The loops of ECD1 are not not shown here since they were not used in the alignment. These loops can be seen in C) forming an anti-parallel beta sheet (in blue). In C) the helices orientation can be appreciated. In A and B, the deviation of yellow and blue model from green

Figure 5 . 10 5 . 3 . 4

 510534 Figure 5.10 Membrane embedded ACKR1 dimer. A) shows the orientation of the helices in a dummy membrane boundary (shown as cluster of dots). The width of the TM region is also shown. Red colour highlights the disulfide bridges and grey colour on the intracellular face depicts ICD2. B) The complete model system with dimer embedded in an RBC mimic membrane (shades of yellow), sandwiched by water layers of thickness 10Å containing 22 neutralizing K+ ions. Cholesterol is coloured separately from the phospholipids to depict its equal distribution across leaves. Terminals are coloured in black. C) A top view of the membrane system with SS bridges highlighted in red colour. The top view gives a better perspective of outward tilting and binding pocket. Cholesterol is shown in yellow as ball and stick model.

Fig 5 .

 5 Fig 5.11). The Arg in DRY either forms an ionic bond with Asp of DRY in inactive state or

Figure 5 . 11

 511 Figure 5.11 Dimer interface. The figure shows 7 + 7 TM-helices as cylinders embedded in phospholopid membrane (shown as transparent surface). The helices are marked with their numbers. The interacting residues are coloured in yellow (hydrophobic), blue (polar), and red (charged interactions). TM5 and TM6 covers the major interface area on extracellular side. However, ICD2 also have charged interaction (Arg) forming a smaller interface at intracellular face.

  by the phylogenetic placement of ACKR1 among human chemokine receptors. The resulting tree topology depicted ACKR1 to be highly distant from rest of the clade. Thus leaving a big phylogenetic gap between ACKR1 and its closest neighbor. Also, recently it has been established that the silent mutation in Duffy negative of Western African population have reached fixation levels [438]. This mutation grants them natural immunity against Plasmodium vivax Malaria and thus is seen as a striking example of natural selection of genetic traits. The function of ACKR1 on erythroid cells as well as the underlying mechanisms of its promiscuous behavior towards chemokines and P.vivax DBL is not clearly established. Moreover, the chemokine receptor family consist of many anomalies. Besides, ACKR1 interaction with Plasmodium vivax; CCR5, CCR3 and CXCR4 also plays an important role in the entry of the virus during HIV-1 infection [439,440]. Interestingly, there are reported incidents of gene piracy in the chemokine receptors. The large DNA viruses copy the encoding regions of host chemokine receptors and use them against the host machinery to either bypass the immune response or cellular reprogramming or cell entry [441]. Several viral homologues of chemokine receptors have been identified in Humans, mostly encoded by the members of Poxviridae and Herpesviridae families

Figure 6 . 1

 61 Figure 6.1 Chemokines structure and classification. A) shows the structural scaffold of chemokines. The sites of two disulfide bridges is also shown. N-terminal domain ends before the first Cys residue. N-loop plays important role in binding to the receptor. B) schematic diagrams of chemokines according to their classes. The number of residues between the two Cys between Ntermimal domain and N-loop forms the basis of classification of chemokines. C) The table shows

Figure 6 . 2

 62 Figure 6.2 Chemokines receptors. The schematic representation of basic chemokine receptor scaffold. Important residues are marked with green, orange and pink color. Blue denotes the residues involved in disulfide bridges. Pink represents the most conserved residue in a TM helix while orange indicates the microswitch residues. Yellow tabs show the important motifs involved during the activation of the chemokine receptor. Arrows indicate the interactions, (solidpermanent, dotted-transient).

  also interact with the Gα subunit of the heterotrimeric G-proteins. The extracellular loops (ECD2, ECD3, ECD4) as well as N-terminal regions of each TM helix interacts with the binding chemokine [465]. Figures 6.2 and 6.3 highlights the interacting residues and 3-D structural orientations during chemokine ligand receptor interaction.

Figure 6 . 3 3

 633 Figure 6.3 Structural organization of a chemokine receptor. The 7TM helices forms a classical helical bundle with three interconnecting loops, facing each extracellular and intracellular sides. Helices are marked as numbers. Chemokine binding pocket is shown along with the specific epitopes present in ECD1 for ligand identification. In the current image, given the epitopes, the chemokine receptor is ACKR1. The crucial disulfide bridges is also shown. The disulfide bridge between N-terminal domain and last extracellular loop is crucial for ligand binding pocket formation. + Image taken from [372]

Figure 6 . 4 6 . 2 . 6 . 2 . 6 . 1 . 5

 646262615 Figure 6.4 Chemokine system signalling pathway. The complete signalling scheme for chemokine system. The left side shows normal G-protein signalling via Gαί, Gβ४, and Gαq pathways. Upon activation, the GDP is exchanged for GTP thus dissociating the heterotrimeric G-proteins. The

Figure 6 . 5 6 . 1 . 5 . 2

 656152 Figure 6.5 Chemokines and their receptors. A) shows the share space of the chemokine and their receptors. Some of the receptors are shared among many chemokines for eg. CCL5 binds with 5 receptors. While lower sector of (A) shows the receptors specific to single chemokine. B) shows details about the ACKRs. ACKR1 is the only motif that completely lacks the DRY motif and also binds to two classes of chemokines. + Image A is taken from [467]

Figure 6 .

 6 6 shows the conservation profile of functionally important sites in 118 chemokine receptors. All the default settings were used for hmmbuild v3.1b2 with --amino tag. Using the HMM profile generated from a carefully curated MSA enhances the confidence in the quality of the hits obtained by hmmsearch (v3.1b).A total of 10332 hits were obtained. The output was filtered based on high domain wise e-values and high bias to score ratios. After removing 3936 sequences, a data-set of 6404 sequences was obtained.

Figure 6 . 6

 66 Figure 6.6 MSA used for HMM building. The multiple sequence alignment of 118 chemokine receptor homologs. The alignment is colored according to residue identity. The intensity of blue color signifies the extent of conservation of a single amino acid. Conservation of important microswitch motifs is also shown. The blue dots signify high conservation of important residues for chemokine receptors, e.g. Proline.

6. 2 . 3

 23 Multiple Sequence Alignment (MSA):MAFFT v7.27 was used to align 3129 homologs of chemokine receptors. Since all the sequences obtained are expected to belong to class A GPCR family, the 7 transmembrane helices provide a strong control. To exploit this feature, an MSA of 21 human chemokine receptors, including ACKR was generated. The alignment was manually edited to conserve the 7TM boundaries as extracted from CXCR4 crystal structure[394] and JPred secondary structure predictions[486].

  matrices -madd EHO,EX2,EX3,UL2,UL3,EX_EHO,LG4X Tree refinement options -wbtl, -bb 1000, -abayes, -con All the default substitution matrices available in IQ-Tree like Dayhoff, JTT, DCMut, Poisson, WAG, etc, are optimized on globular proteins. Shortly after the first JTT matrix in 1992, Jones et al published another matrix in 1994 that was based on transmembrane proteins and showed that the substitution rates are different than the classical Dayhoff matrices [488]. Therefore, a new substitution matrix based on class A GPCR specific substitution rates, GPCRtm, was used. The GPCRtm had been shown to outperform JTT-tm matrix [489].

6. 3 . 1 . 1

 311 Viral Chemokine Receptors:The initial dataset contained 1074 viral chemokine receptor hits. However, 1051 were rejected based on short lengths and could not pass the first stage of filtering. The rejected receptors mostly belonged to Equine herpesvirus (EPV), Sheep poxvirus (CPV), and Fowl poxvirus (APV) while most of the Human pox and herpes virus (like HCMV, HHPV) were among the 23 receptors selected. Post clustering, these were reduced to 7 sequences as 9 were rejected as singletons. The singletons also belonged to Human Epstein Barr virus (BILF-EBV), Equine EPV, Capri CPV and Aves APV classes. The BILF receptor from EBV was investigated for its rejection and it was found out that it is a non-chemokine receptor encoded in the virus genome. Therefore, it's rejection as a singleton can be justified. The final 7 viral chemokine receptors belonged to Yaba-like disease virus, Yaba monkey tumor virus, Tanapox virus, Human cytomegalovirus (HCMV), and Swine poxvirus. Except HCMV, rest all belong to poxviridae family. Two receptors from HCMV (beta herpesvirus), unique short 28

  7consensus row. For instance, in motif NPxxY, there are two gaps between the 'x' and 'x' making the motif as NPx--xY. The insertion includes a glycine (G) and tyrosine (Y) residue from a predicted GCPR35 receptor in nine banded Armadillo (NCBI Acc: XP_012378710.1). It is also noticed that the motif is not at all conserved in the GCPR35 sequence, where it reads: DAxGYxY instead of NPxxY. Therefore, it can be safely considered as an artifact especially when removing such sequences reveal absolutely conserved motifs like NPxxY, CWxP, TxP. The most validating observation about the MSA is very high or near complete conservation of DRY motif with D, R, and Y occuring more than 90% of times at the aligned position. Moreover, the DRY motif is completely absent in all the ACKR1 sequences and have substitutions like E, K, and F (respectively) in some viral and decoy receptors. The conservation profiles of these important regions is shown in logo in consensus row and quality row in Figure6.7.

Figure 6 . 7

 67 Figure 6.7 MSA used for tree generation. The multiple sequence alignment of 3129 homologs of human chemokine receptors. The intensity of blue color signifies the extent of conservation of a single amino acid. The blue ticks on the top denotes the collapsed alignment between two points. The overall conservation is depicted as consensus logo along with the quality index of alignment. Conservation of important microswitch motifs is shown. The blue dots signify high conservation of important residues for chemokine receptors, e.g. Proline.

8 .

 8 Overall the tree topology is in accordance with the classical nomenclature scheme of chemokine receptors. Most of the CCR receptors makes a single super-clade while most of CXCRs also lie in clades adjacent to one another. The ACKRs, however, do not form isolated clades, rather are found to be placed according to their old names. For instance, ACKR2 is taxonomically related to CCR super-clade containing CCR6, CCR7, CCR9, and CCR10 while ACKR4 is placed next to CXCR6. ACKR3 is phylogenetically most related to GPR35 which have been characterized as CXCR8 and interestingly the old name of ACKR3 is CXCR7. These observations indicate the selective evolutionary pressures on the chemokine receptors based on their functions, i.e, binding to chemokines. The classical nomenclature of chemokine receptors is based on the type of chemokines that bind to a receptor. The phylogenetic placement of these receptors seems to follow the trend. For instance, the clade of CXCR2 has high overlaps with the adjacent clade of CXCR1 so much so that it is difficult to consider CXCR2 as a separate clade, seeFig 6.7. It is also known that CXCL1 (IL-8) can bind to both CXCR1 as well as CXCR2 with different affinities[444].Moreover, the ligand pool of CXCR1 and CXCR2 consists of CXCL2, CXCL1 and CXCL8; the genes of these chemokines lie on Human chromosome 4. Similar overlaps are also observed in the clades of CCR2 and CCR5. While both CCR2 and CCR5 have exclusive set of ligands but their encoding genes are located on Human chr 17. Besides, all the ligands in ligand pool of CCR2 and CCR5 have macrophage regulatory function. Therefore, further validations on their chemokine signaling can propose a merging of the two chemokine receptors. A caveat of proposing it from the current study is that the chemokine signaling of all the chemokine receptors have to be analysed in context of their individual species.6.3.3.1 Evolutionary placement of ACKR1ACKR1 forms a distant isolated clade placed at extremely large branch length of more than 7 units from the root. The closest clade to ACKR1 is GPR35 (or CXCR8) at 5.4 branch units from the root (as seen inFig 6.8). Therefore, ACKR1 is highly distant from rest of the tree with a distance of 4.7 units from its branching node. The super-clade (sharing the same node) of ACKR1 consists of three clades ACKR1, GPR35, and ACKR3 and can be represented as (ACKR1(GPR35, ACKR3)). The clade of CXCR4 lies adjacent to this super-clade sharing the parent node with it. It can be represented as ((ACKR1(GPR35, ACKR3)) CXCR4). Since, no crystal structure is available for either GPR35 or ACKR3, CXCR4 becomes the closest clade that have its molecular structure determined, experimentally. This observation supports the choice of CXCR4 as a potential structural template for modelling ACKR1 structure. The same observation was also made from the phylogenetic tree generated with 21 seed sequences.

Figure 6 . 8

 68 Figure 6.8 Evolutionary perspective on chemokine receptors. The tree topology generated from IQtree for 3129 chemokine receptor sequences. The different clades are colored under the shades of same color. For instance, all CCR have shades of blue while CXCR and ACKR have shades of green and red, respectively. The clade distances are based on the bootstrapped branch lengths. Most populous species from each clade is shown as a cartoon. The walking hippo denote Mammals. The ACKR1 clade is expanded to fullest to show the location of primates in the tree. Yellow coloured branches signify the presence of viral chemokine receptors. They form outlier groups in each of the CCR8, ACKR4, and CCR10 clades.

  3.1.1 that there are 7 viral chemokine receptors that are included for the MSA. vChemR are guided by evolutionary pressures different from those on chemokine receptors and therefore, it is interesting to see their placement in the tree. Rather than forming a separate clade, the vChemR are found in the clades of chemokine receptors like CCR8, CCR10 and ACKR4 placed as outliers in their respective clades. Yatapox viruses like Tanapox virus (TPV), Yaba-like disease virus (YDV), and Yaba monkey tumour virus (YTC) are clustered in the same clade located in the CCR8 clade. TPV and YDV are immediate neighbors sharing the same branch as shown in Figure6.8. While, YTV branches off just before the branching node of TPV, YDV forming a representation as (YTV (TPV, YDV)). The clade is placed at a distance of 1.8 units from the average distance of CCR8 members from the root thus making it an outlier to the group. vChemR belonging to Human Cytomegalovirus (HCMV) US28 and UL33 are located within the clade of ACKR4 at a distance of 0.8 away from the average distance of ACKR4 from the root. The phylogenetic distance between US28 and UL33 is 0.24 units while the distance to their closest ACKR4 neighbor is 0.25. The other pox viruses, i.e, the swinepox virus are found in the CCR10 clade occupying a single isolated branch. The branch is 0.5 units away from the average distance of CCR10 members from the root and 0.05 units away from closest CCR10 neighbors.

Figure 6 . 9

 69 Figure 6.9 Taxonomic distribution of the different chemokine receptor clades. Relative frequencies of different taxas is plotted on each clade. Mammalia is shown to be the most populist group. However, in CXCR7, CXCR4, CCR8, CCR4, dominant groups are Fishes, Reptilia, and birds respectively. ACKR1 has occurrence of a sequence that is annotated as "UnDefined protein". Few synthetic constructs are also observed in CXCR4 and CXCR2 clades. The values for CCR11 should be clubbed with those of ACKR2.

Figure 6 . 11

 611 Figure 6.11 Topology of the ACKR1 clade. Figure shows the phylogenetic relationships among class Mammalia for ACKR1. Taxonomic orders are depicted as cartoon of its most known species.The branch distance from the root can be estimated using the radiating scale values given on the right side. The numbers shown on the nodes signifies that the nodes after that can be collapsed under same order. For eg.[START_REF] Nishizuka | The role of protein kinase C in cell surface signal transduction and tumour promotion[END_REF] corresponds to superorder Cetartiodactyla,[START_REF] Agarwal | Role of Protein Dynamics in Reaction Rate Enhancement by Enzymes[END_REF] will collapse all bats species under chiroptera. Although the ACKR1 clade has a distance of more than 7.0 from the root, yet the whole clade, except marsupials, have evolved within 1.0 units.

Figure 6 .

 6 Figure 6.12 Tracing ACKR1 evolutionary developments. The branch lengths of the ACKR1 clade is converted to age values (from the root). The larger the value, more ancient is the branch leaf. A) shows the evolution of ACKR1 in order primates with respect to other taxonomic families. B) shows the detailed evolution of ACKR1 protein in primates. The youngest species that acquired ACKR1 is highlighted in blue dotted lines and its representative is shown as cartoon. The most ancient species is marked by red dotted lines. Squirrel monkeys (new world monkeys) are the youngest while Tamarins are the oldest in primate sub-clade. C) Similar analysis for Rodentia.

Table 6 . 1 .

 61 Structural relatedness among different clades. The TM-score profile of 18 PDB structures representing major clades of the tree. For many clades, no PDB structure was found. The PDB id and the chain id used is represented on both axes under the name of the clade it represents. These represent 9 clades from the tree. The gradient green, from light to dark indicates higher structural similarity or better TM-score. The intensity of grey shows least TM-score and no relation. Both row-wise and column-wise averages are calculated and represented as a dark green bars.

6. 4

 4 Conclusions and PerspectivesChemokine receptor family is comprised of a diverse set of sequences classified into various subgroups like CCR-, CXC-, CX3C, or XC-Receptors. The current classification is based on the class of chemokine ligand binding to the receptor, therefore an α-chemokine binding receptor is named CXCR while a β-chemokine binding receptor is termed CCR. Such a premise, however may change with the enhancing knowledge about the hetero-oligomerization in chemokine receptors and discovery of more virally encoded chemokine receptors. Therefore, it may become necessary to understand the evolutionary perspective of chemokine receptors to assess their phylogenetic relations along side their functional relationships. The presented phylogenetic study is based on the most comprehensive data (till date) on chemokine receptors. The data-set contains 3129 sequences of chemokine receptors and few other class A GPCRs. Strict controls and filters have prevented the contamination of the dataset by unrelated or highly redundant sequences. The tree is also supported by a robust multiple sequence alignment founded on a seed alignment of 118 known chemokine receptors. Moreover, the MSA is also validated by the conservation of all the important functional and structural sites. These initial checks and validations accompanied by the use of GPCR specific substitution model for tree building enhances the confidence in the phylogram.

carboxylesterase 1 and

 1 Renin endopeptidase, threonine phosphorylation in Cyclin dependent kinase 2 and histidine methylation in Actin. The backbone analysis using PB derived entropy function (Neq) of N-glycosylation showed that the addition of the glycan neither impact the local nor the global backbone conformation of the proteins. However, the methylation on actin structure induced a local increase of the backbone diversity at the PTM site region, thus highlighting a higher deformation of this part of the protein. However, no effect on the intrinsic mobility of this region was observed as the structure with and without PTM had same B-factor profiles. Neq as well as normalized B-factor values revealed that the phosphorylation site and its neighborhood positions display a significant backbone diversity. The comparison among modified and unmodified structures of CDK2 revealed that the phosphorylation on the activation loop at Thr 160 have several local effects. It rigidifies the backbone locally while increasing the deformation at two distant regions both of which are also important sites for PTM.Despite the intrinsic link between PTM and protein function, the molecular effects of the modifications on the protein structures and dynamics remain poorly understood. Therefore, molecular modeling of PTMs combined with molecular dynamic simulation is an interesting alternative. It is mention-worthy here that I also work on understanding dynamics of active to inactive transformation in protein kinases in collaboration with Prof. N. Srinivasan of IISc

  only a single research article by Adam Godzik's lab in 2007. They tried to identify and characterize DPF and proposed that DPF differs from the disorder and order in their specific sequence composition. The DPF characteristic amino acid signature, as proposed by[START_REF] Zhang | Between order and disorder in protein structures: analysis of 'dual personality' fragments in proteins[END_REF] is, Thr, Arg, Gly, Asn, Pro, and Asp. Though a one of its kind and a benchmark study, they focus mostly on sequence based characterization of DPF like most disorder related articles. DPF transit from disorder to order and thus will have structural information available. We decided to exploit this structural information to characterize DPF along with sequence features. Based on our analyses with PB, secondary structures, B-factors, and solvent accessibility we characterize DPF.High frequency of Cys, Gly, Asp, and Lys in a region can be an indicative of a DPF. Two of these residues, Cys and Gly are rigid and moderately flexible while rest two are highly flexible.

  chapter. The preliminary NMA results indicate that the two subunits of the dimer have different structural fluctuations rather they are shown to be negatively correlated. Exploration of these results can provide insights into the individual and concerted dynamics of the dimer. Further, a perturbation response study of key residues has also been carried out. The preliminary results show that the interface residues are the most effected one and the TM1 and TM7 are the most exposed and sensitive regions. Any perturbation in the interface residue leads to increased fluctuation in the overall structure, especially in TM1 and TM7. The further analysis of these results can help us understand the role of allostery in the 7TM structure of ACKR1. Of course, the conclusive remarks on the dynamics of the local structures in the homodimeric, membrane embedded, assembly of ACKR1 requires all atom molecular dynamics. However, given the enormous size of the system the computational cost is expensive and I hope I will have time to analyze these results in subsequent months of finishing this document.

  Therefore, we decided to collaborate with Dr. Sophie Abby from Austria to study the molecular phylogenetics of the chemokine receptor family. Foremost, this required a basic understanding of chemokines and their receptors, their sequence, structure and function characteristics. The current classification of chemokine receptors is based on the class of chemokine ligand binding to the receptor. An α-chemokine (CXC type) binding receptor is named CXCR while a β-chemokine (CC type) binding receptor is termed as CCR. Such a premise, however may change with the enhancing knowledge about the hetero-oligomerization of chemokine receptors and discovery of more virally encoded chemokine receptors. The information gathering process during this project was fascinating for instance, I learned for the very first time about the existence of virally encoded chemokine receptors. The concept of gene piracy and how smartly does viruses use chemokine receptor mimics of their host to escape the immune response.

Table I . 1 .

 I1 .1. Characteristics of different types of helices found in proteins.

	Helix type	Phi (Φ)	Psi (Ψ)	Description
	α-helix (R)	-57	-47	Right handed α-helix
	310-helix	-49	-26	Right-handed 310-helix
	π-helix	-57	-80	Right-handed π-helix

  the following equation is less than -0.5 kcal/mol. Based on the identified hydrogen bonds, eight types of secondary structure are assigned. These eight types are usually grouped into three larger classes: helix (G, H and I), strand (E and B) and loop (S, T, and C, where C sometimes is represented also as blank space).

	I.6.1.2 STRIDE -STRuctural Identification
	STRIDE is also a secondary structure assignment tool like DSSP but instead of
	using only the hydrogen bond potential, it also includes dihedral angle potentials to define
	secondary structures within a protein. Hence, its criteria for defining individual secondary
	structures are more complex than those of DSSP. The STRIDE energy function contains a

The DSSP program was designed by Wolfgang Kabsch and Chris Sander. It identifies the intra-backbone hydrogen bonds of the protein using a purely electrostatic definition. A hydrogen bond is identified if E in hydrogen-bond term containing a Lennard-Jones-like 8-6 distance-dependent potential and two angular dependence factors reflecting the planarity of the optimized hydrogen bond geometry. The criteria for individual secondary structural elements, which are divided into the same groups as those reported by DSSP, also contain statistical probability values derived from empirical examinations of solved structures. There have been comparisons

Table 3a .1 The Dataset for PTM analysis: Using

 3a The non-redundant dataset consisted of 348 N-glycosylation on 156 PDB chains from different organisms, 92 phosphorylations on 76 structures from 12 different organisms and methylations on 15 structures from 9 distinct organisms, details in Table3a.1.Similar datasets were also generated for the analysis of different types of phosphorylations. Dataset was selected based on the amino acid residue phosphorylated. 84 serine modifications on 59 pdb chains while 51 phosphothreonine and 42 phosphotyrosine are found on 38 and 36 unique pdb chains. Tabular details are provided in; Table3a.2.

PTM-SD, a comprehensive structural dataset is prepared with PTMs, N-glycosylation, phosphorylation and methylation. The table indicates the details of the dataset with diversity indicated as number of different source organisms, size depicted by the no. of chains and quality of data is indicated by the number of PTM. Similar statistic is also given for the derived non-redundant dataset (in columns 5 to 7).

Table 3a .2 Dataset for phosphorylation analysis

 3a

. The table represents the details of the dataset comprising of different kind of phosphorylation modifications, built using PTM-SD. The diversity of the data is indicated by the number of different source organisms, size depicted by the no. of chains and quality of data is indicated by the number of PTM. Similar statistics is also given for the derived non-redundant dataset (in columns 5 to 7).

3 Results and discussions 3a

  .3.1 Impact of PTM on overall protein backbone conformational diversityUsing PTM-SD [136], the two most frequent PTMs were focused upon, N-glycosylation and phosphorylation. 3,092 and 1,307 chains were found containing 7,110 N-glycosylations and 1,873 phosphorylations in 100 and 22 organisms respectively. A non-redundant dataset, with less than 25% of identity between the corresponding UniProt sequences, was generated, resulting in the selection of 348 N-glycosylations (for 156 protein chains in 41 organisms) and 92 phosphorylations (for 75 protein chains in 12 organisms, see Table3a.1).

3a.3.1.1 Neq analysis Based on 16 PBs, Neq underlines the diversity of local conformation in a finer manner than the classical secondary structures (see Methods 3a.2.3). Figure 3a.4 shows the variations of PBs around the two PTMs ー N-glycosylation and phosphorylation. It is observed that the PTM sites do not exhibit any significant preferences for a particular local structure conformation. The Neq values are very high, ranging from 9.03 to 11.44 for N-glycosylation, and from 5.95 to 11.41 for phosphorylation, implying that these two modifications are observed in widely diverse structural contexts. Nonetheless, it is interesting to note that both types of PTMs have an overall different Neq profiles (see black curve in Figure 3a.4).

  Although the diversity in mammalian chemokine receptors is considerable yet there are some species that dominate others by their contribution to the sequence pool. Therefore, in almost all the clades, four major species are observed to have majority contribution to the chemokine receptor pool. Most of the sequences in each clade belong to Primates, Rodentia (Muridae family), Moles (Talpidae family), and Bats (order Chiroptera). These four species contribute towards 50% of the sequence pool in each clade. Also, overall the most contributing genera are: Rodentia with ~20% of sequences belonging to it and ~19% of receptor sequences originating from Primates. With 3.12% and 2.08% of sequences belonging to Apes and Humans, the hominidae contribution to the chemokine receptor sequences amounts to 5.2%.In CCR1, 26% of sequences are contributed by Rodentia while 13% belong to Primates and 9% from Bats (Chiroptera) and Moles each, thus comprising more than 50% of the clade population. Similar trend is observed in other clades with slight exceptions in CCR5, CXCR1,

	17% or second largest contribution to the CCR5 sequences. The values for CCR11 should be
	clubbed with those of ACKR2.
	While the clade has small
	number of sequences from class Fishes and Reptilia contributing to 4% and 6% to the CCR4
	sequence pool. Similarly, in clades of CCR8, 49% of receptors are avian while 43% are from
	Mammalia and 8% from Amphibia, Fishes, and Reptilia combined. Sequence pool of CXCR4 has
	significant contributions from all the major taxonomic classes. Reptilia have dominating
	contribution to CXCR4 sequences with 37% succeeded by 26% from Mammalia. CXCR4 also
	have 13% of sequences belonging to Amphibia which is also the most dominant contribution from
	class Amphibia to a chemokine receptor clade. In other clades, Amphibia contributes only 1% to
	2% to the sequences. Clade CXCR7 have an equal population of Fishes and Mammalia with 43%

ACKNOWLEDGEMENTS

The current analysis and the one carried out in chapter 1 would have not been completed without the immense support from M. Nicolas Shinada and Dr. Alexandre de Brevern. A special thanks to Dr. de Brevern is required for his sincere help in shaping and compiling the chapter.

ACKNOWLEDGEMENTS

I would like to thank Dr. Pierrick Craveur for developing PTM-SD that is quintessential to this study. I also thank him and Dr. Joseph Rebehmed for extracting the data and their critical advices and suggestions during the analysis.

ACKNOWLEDGEMENTS

The systematic analysis would have not been possible without the data-set prepared by Dr. Agnel Praveen Joseph and preliminary data refinement by Dr. Pierrick Craveur. The fruitful suggestions and insights into statistical methods by Dr. Alexandre de Brevern have been instrumental in the development of the study.

ACKNOWLEDGEMENTS

All the MDs were run by four master students, Matthieu Goguet (M2), Sali Anies (M2), Sarah Kaddah (M1), and Soubika Bisoo (M2) under my supervision. Without their sincere efforts, the simulations might have not been finished during my thesis tenure. Therefore, a very warm thanks to them. Sincere hardwork done by Dr. Vincent Jallu in finding and selecting different variants is incredible and is fundamental to the project.

ACKNOWLEDGEMENTS

Given that almost little to nothing is known about the structure of ACKR1, I would really like to acknowledge the patience and support given by Dr. Sophie Abby, Dr. Alexandre de Brevern, Dr. Agata Kranjc Pietrucci. A special mention is needed for Dr. Olivier Bertrand for sharing his immense experience with DARC through insightful discussions.

ACKNOWLEDGEMENTS

All the support and guidance required for the phylogenetic analysis was provided by our collabolator and friend from Austria, Dr. Sophie Abby. Her inputs have been pivotal in designing and shaping the work. It is her expertise in molecular phylogenetics that suggested using MSA of 118 chemokine receptors as seed for the MSA of 3129 sequences. It is my pleasure to have found a mentor like Sophie.

Dissemination of results

The results from chapter 1 were published as: Narwani T.J., Craveur P., Shinada N.K., Santuz H., Rebehmed J., Etchebest C., de Brevern A.G. Dynamics and Deformability of α-, 310-and π-Helices. Archives of Biological Sciences (2018) 70 [START_REF] Nishizuka | The role of protein kinase C in cell surface signal transduction and tumour promotion[END_REF]: [START_REF] Richardson | The beta bulge: a common small unit of nonrepetitive protein structure[END_REF][START_REF] Venkatachalam | Stereochemical criteria for polypeptides and proteins. V. Conformation of a system of three linked peptide units[END_REF][START_REF] Harrison | DNA Recognition By Proteins With The Helix-Turn-Helix Motif[END_REF][START_REF] Lewit-Bentley | EF-hand calcium-binding proteins[END_REF][START_REF] Sibanda | β-Hairpin families in globular proteins[END_REF][START_REF] Teichmann | Evolution and dynamics of protein complexes[END_REF][START_REF] Fanelli | Hemoglobin and Myoglobin[END_REF][START_REF] Dayhoff | The origin and evolution of protein superfamilies[END_REF][START_REF] Hubbard | SCOP: a Structural Classification of Proteins database[END_REF][START_REF] Johnson | Amphitropic proteins: regulation by reversible membrane interactions (Review)[END_REF][START_REF] Kendrew | A three-dimensional model of the myoglobin molecule obtained by X-ray analysis[END_REF].

Dissemination of results

The information from chapter S1 was archived as a mini review article: Narwani T.J., Santuz H., Shinada N.K., Melarkode Vattekatte A., Ghouzam Y., Srinivasan N., Gelly J.-C., de Brevern A.G.

Recent advances on polyproline II. Amino Acids (2017) 49( 4):705-713.

Dissemination of results

The results of the chapter 3b have been published as a scientific poster at ADELIH conference, PTM-from bench to bed side, held in Paris in Oct, 2016. It got the best poster award.

The latest development is that the manuscript for a research article consisting of results from Chapter 3a have been written and revised. We are waiting for one of the authors final comments before sending it to journals. The work will be published as:

Dissemination of results

The resuts from chapter 3b were presented in the form of a scientific poster at IDP-2017 (Intrinsically Disordered Proteins) Another manuscript emphasizing on the modelling approach for long missing regions of Calf-2 and its dynamics is under preparation.

Dissemination of results

The results from the structural study of ACKR1 have been published as a scientific poster at ISMB-3dSIG conference held at Prague in July 2017. A more recent and updated poster is to be presented at EMBL BioMalPar XIV conference to be held at Heidelberg in May 2018. The conference hosts a section dedicated to computational approcahes and therefore can nourish intense discussion on all the known chemokines and their receptors. The agonist and antaonist activities are in reference to the type of response illicit by the receptor after binding to chemokine. ACKR1 is the only receptor that binds to chemokines from both the classes. Also ACKR1 binds only to inflammatory chemokines.

+

Image A taken from [444], + Image C taken from [378] All chemokines adopt a similar structure with 3 antiparallel beta-strands and an alpha helix as depicted in Figure 6.1A. The N-terminus is a long deformed region that contains two of the four cysteine residues. The position of the two cysteines in the N-terminal loop are important in the nomenclature of the chemokines. The loop region from second cysteine residue uptil a short, single turn 310-helix is called as an N-loop, followed by three antiparallel beta strands (β1, β2, β3) succeeded by a C-terminal ɑ-helix. The turn connecting β1 to β2 is termed as 30's loop, while the turn between β2 and β3 is called 40's loop. The 50's loop joins the β3 strand to the α-helix. The The availability of the crystal structures of active state class A GPCRs (like Bovine Rhodopsin) by the advent of new millenia, encouraged numerous biomolecular and biophysical studies. These lead to a practical understanding of the activation mechanism of GPCRs. Later, in 2010 the first crystal structure of a chemokine receptor was solved [395]. Numerous studies supported that the activation of the chemokine receptor by the chemokine binding is accompanied by a see-saw tilting of the 7TM helices [468,469]. The extracellular portions of the helices supported by the disulfide bridges lead to an inward tilting of the helices. Thereby, causing an outward tilting of the intracellular regions of the TM helices. This leads to the interaction of the ICDs with G-proteins.

The key residues involved in such a switching of the structure, post activation are called molecular microswitches. Following are the well known microswitches in chemokine receptors.

(i) DRY motif-The conserved three residue motif composed of Asp, Arg and Tyr is located at the junction of TM3 and ICD2. The motif is positionally conserved throughout the chemokine receptor family with one exception. The arginine residue in the motif functions as a microswitch.

In the inactive state, it forms an ionic lock with the Asp [380]. Post ligand binding, the pKa of the arginine changes consequently leading to the disruption of the ionic lock. The loss of interaction is compensated by new interactions with the well conserved Tyr residue of the following intracellular loop-ICD3 and Gα subunit of the G-proteins [381]. This leads to the outward tilt of the TM3 helix towards the heterotrimeric G-proteins. The Arg residue is highly conserved throughout the chemokine receptors as well as the class A GPCRs. However, in some class A GPCRs like Rhodopsin, the 'D' of the motif is replaced by Glu (E) residue. Therefore, the motif is also known as E/DRY motif [381].

(ii) CWxP motif: Located in the TM6 helix the short motif is composed of residues Cys, Trp succeeded by any residue and a Pro. As mentioned in chapter 1.2 that proline residues are helix breaker due to their cyclic backbone. Therefore Pro in case of CWxP motif causes a kink in 6.1.

Atypical chemokine receptors

The chemokine receptor binds to a chemokine ligand and illicit cellular pathways using Gproteins that finally lead to leukocyte migration for homeostasis or inflammation. A chemokine receptor that bind to chemokines but does not signal is termed as an atypical chemokine receptor (ACKR) [384]. There can be more than one reasons for the failed signal transduction for example, lack of microswitches, or mutations in such motifs, or alternate signaling using β-arrestins. Among the known chemokine receptors, it was found that CXCR7 and CCR11 cannot signal through Gproteins and therefore fail to induce leukocyte migration [473]. Apart from these, Duffy antigen discovered in 1950 would be later classified as an ACKR along with a β-chemokine receptor D6 [386,474]. These comprises a sub-family of four 7TM receptors under canonical chemokine receptors. The four ACKR are named as ACKR1 (previously Duffy Antigen for Chemokine Receptors), ACKR2 (previously D6 or CCBP2), ACKR3 (previously CXCR7), and ACKR4 (previously CCR11).

All ACKR are expressed on non-leukocyte cells. While ACKR1 is also expressed on vascular endothelium and erythrocytes, the rest of ACKR members are expressed on lymphatic endothelial cells [473]. All ACKRs except ACKR1, are known to signal via G-proteins independent pathways like biased signaling using β-arrestins [475]. Therefore, ACKR1 cannot signal either through canonical or alternate signaling mechanism. The reason for such behavior could be attributed to ACKR1 complete lack of the DRY motif, especially the Arg microswitch [381]. Therefore, it cannot couple to G-proteins or β-arrestins. In other ACKRs the Arg microswitch is conserved although the overall DRY-LAIV motif contain variations. Another starking difference between ACKR1 and rest of the ACKRs is that ACKR1 is the only member that binds to both αand β-chemokines.

Conclusive Outline:

Before starting my PhD with Alex, I was gifted a book from my previous scientific advisor, Dr. Srikrishna Subramanian. The book was a series of inspiring lectures from famous physicist Dr.

Richard Feynman titled 'The pleasure of finding things out'. The book gives insights into Dr.

Feynman's approach towards science that can be summarized as simply as curiosity.

During the course of these three years of research work under PhD tenure, I got to learn more than just the meaning of pleasure of finding things out. Perhaps, Alex designed the flow of my PhD, titled Dynamics of protein structures and its impact on local structural behaviors, in this specific manner to help me learn and grow. The first chapter focuses on a portion of structural space, Helices and asks very simple question on how do they behave in dynamics. While chapter 5 that forms the penultimate chapter for my thesis deals with a complex structure assembly that too in a membrane environment. In hindsight, I can see the underlying plan of my thesis as I realize that each chapter exploits the information gained from its preceding chapters. The inclusion of chapter 6 was unavoidable because of its close association with chapter 5 of the thesis but it also helped me employ different in silico techniques to answer pertinent questions.

Chapter 1 is based on the objective to understand local structural behaviors in helices. Therefore, the first task was to check the persistence of helices in their original or starting conformation (during MD simulations). It was observed that more than 3/4 th of α-helices persist thus indicating the order in their structures. However, 310-helices changed much frequently with more than 40.5% of time the residues assigned as 310-helices changed to either a helical or non-helical conformation.

The π-helices were observed to be the most deformed as very few π-helices persisted as π-helices during the collective simulation time of 150ns. The α-helix showed good correlation among their stability and flexibility in terms of B-factors, RMSf and surface accessible area. The unsupervised clustering of different helical conformations and use of PB and related statistics, Neq showed that the α-helix also have a higher tendency to assume β-turn conformations than either of the two other helical forms. The individual clusters of 310-and π-helix revealed their tendency to transit to αhelix. However, the 310-helix that transformed to α-helix showed different characteristics. It depicted higher B-factor and RMSf values than the average values in its cluster, thus revealing that

Abstract in English:

Dynamics of protein structures and its impact on local structural behaviors Protein structures are highly dynamic in nature contrary to their depiction in crystal structures. A major component of structural dynamics is the inherent protein flexibility. The prime objective of this thesis is to understand the role of the inherent dynamics in protein structures and its propagation. Protein flexibility is analyzed at various levels of structural complexity, from primary to quaternary levels of organization. Each of the first five chapters' deal with a different level of local structural organization with first chapter dealing with classical secondary structures while the second one analysis the same using a structural alphabet -Protein Blocks. The third chapter focuses on the impact of special physiological events like post-