ACKNOWLE DGEMENTS

I gladly take this opportunity to acknowledge those great people who have inspired, supported and motivated me during the course of my research work. I find myself short of words while expressing my sense of gratitude and indebtedness towards my supervisors, Prof. Anna Bernardi, Prof. Laura Belvisi, Dr. Anne Imberty and Dr. Annabelle Varrot. It is my honour and rare privilege to have worked under the guidance and supervision of revered mentors. Their wise and timely advice, constant encouragement and pain staking supervision are greatly and generously recognized. I would like to express my sense of gratitude towards Dr. Martin Lepsik and Dr. Monica Civera for providing their suggestions, necessary help and support all the way through my project. I am highly obliged to Dr. Serge Perez, Prof. Francesca Vasile, Dr. Jonathan Cramer and Prof. Beat Ernst for their great collaboration, valuable suggestions and ever available help. I would like to express my appreciation to Dr. Marko Anderluh for being a great coordinator in the PhD4GlycoDrug network and for actively working for all the research fellows in the network. I'm thankful to all the research fellows in the network for being such a great co-worker. Their enthusiasm, energy and sense of humour have made my experience more memorable. I appreciate all the help, encouragement and moral support provided by them in all stages of this uphill task. I extend my sincere appreciation to Rafael Bermeo for amazing collaboration to achieve the planned objectives of the project. Special thanks to Valérie Chazalet and Emilie Gillon for their full hearted co-operation and support during my research work at the CERMAV. Last but not the least I am deeply respectful to my beloved parents and family members for their support and continuous remembrance in their prayers for my success.

Kanhaya Lal

Introduction 1.1 Antimicrobial resistance (AMR): A serious global public health threat

The emergence and spread of infectious diseases with pandemic potential have been reported in the history and some of them are still present in the current times. Several infectious diseases such as cholera, plague, flu, Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV) turned into epidemics and pandemics that have afflicted humanity. In the 20th century, the rapid development of several vaccines provided a prophylactic means to combat some of these infectious diseases. More importantly, the discovery of antibiotics paved the way for the direct fight against various bacterial infections. Thus, introduction of antibiotics has been a bedrock of greatest medical advances of the 20 th century. Because of these discoveries, deadly illnesses such as pneumonia and tuberculosis (TB) could be treated effectively, and a routine surgery was no longer potentially fatal. However, within a short period, it was also discovered that bacteria and other pathogens have evolved to resist the drugs used to combat them. Antimicrobial resistance (AMR) enables the pathogens to resist to the effects of an antibiotic or drug that would usually kill them or limit their growth. 2 Hence, all microbes that have the potential to mutate can render the drugs ineffective. Consequently, over several decades, pathogens causing common or severe infections have developed resistance (to various extent) to the new antibiotic coming to market. Penicillin, discovered by Alexander Fleming in 1928, was the first antibiotic to treat bacterial infections in soldiers during World War II. In the early 1940s, the use of penicillin to treat staphylococcal infection showed dramatic success. However, after a short period of time, in early 1942, penicillin-resistant staphylococci were reported. 3 Surprisingly, by the late 1960s, more than 80% of staphylococcal isolates were resistant to penicillin. Similar pattern of resistance, has been well-established with several antibiotics discovered later (Table 1.1). The period between 1960 and 1980 is known as the golden age of antibiotic discovery, as one-half of the drugs commonly used today were discovered in these two decades. After the 1980s, the rate of discovery of new antibiotic classes had dramatically decreased. The recent report from the World Health Organization (WHO) shows that almost all the new antibiotics that have obtained marketing authorisation in recent decades are the derivatives of the existing antibiotic classes. 5 However, two recently approved agents, vaborbactam (a βlactamase-inhibitor based on a cyclic boronate pharmacophore) and lefamulin (a pleuromutilin) belong to new drug classes. [5][6][7][8][9] In addition, the same report highlights that the antibacterial drugs already in the early stages of clinical development are not effective against extensively drug-resistant bacteria. 5 This analysis indicates a weak pipeline for antibiotic agents. Furthermore, due to the extensive use of antibiotics in 20 th century, several of the bacterial pathogens have evolved into multidrug-resistant (MDR) forms. Such microbes with enhanced morbidity and mortality due to multiple mutations, providing high levels of resistance to the antibiotics are called "superbugs". For instance, fluoroquinolones are antibiotics that target bacterial enzymes known as DNA topoisomerases II (DNA gyrase) and IV. These enzymes are essential for the supercoiling of bacterial DNA. Both enzymes consist of subunits which are encoded by gyrA and gyrB (for DNA gyrase) or parC and parE (for topoisomerase IV). 10 The resistance to fluoroquinolones is developed by accumulation of amino-acid substitutions in these subunits. 11 Likewise, linezolid is another example of antibiotic which belongs to oxazolidinone class. 12 This antibiotic prevents protein synthesis in bacteria by inhibiting formation of the 70S ribosomal initiation complex. Bacterial strains such as enterococci, Staphylococcus aureus and streptococci display resistance to linezolid. The resistance is mediated mainly by mutations in the genes that encode 23S rRNA. Particularly, G2576T mutation is common among the resistant clinical isolates. 13 These highly resistant strains sometimes show increased virulence and transmissibility. Infections to patients by such pathogens are usually hospital-linked as they are associated with several risk factors and comorbidities such as cancer, diabetes and immunosuppression etc. These patients with existing medical conditions are usually prone to the 'opportunistic' infections. [14][15][16] Streptococcus pneumoniae. 17 The emergence of AMR has challenged the effective prevention and treatment of an ever-increasing range of infections by these highly drug-resistant pathogens. It has emerged as one of the principal public health threat of the 21 st century. As per the estimates (Figure 1.1), at least 700,000 people die annually from drug-resistance infections with a future prediction of 10 million deaths per year by 2050. 1 This ten-fold increase can overtake cancer by 2050 to become one of the biggest threats to public health. In terms of economy, the loss due to AMR is predicted in trillions of US dollars which is estimated about 7 percent of the gross domestic product (GDP) of the world. As a consequence of increasing antimicrobial resistance and a narrow antimicrobial pipeline of new drugs, rise in bacterial infections due to multidrug-resistant or extensively drug-resistant pathogens has already rang the alarm bell. To counteract AMR, it is highly important to gear up the efforts toward the discovery and development of novel antimicrobials. Identification of new drug targets and use of innovative strategies to decrease the infections by drug-resistant pathogens are therefore a necessary public health concern.

An attractive strategy is the use of anti-adhesive agents that interfere with the ability of bacteria to adhere to host tissues, since bacterial adhesion is one of the initial stages of infections.

Anti-adhesion therapy: A promising alternative

Adhesion of pathogen to host cell is a universal prerequisite to efficiently deploy repertoire of virulence factors and exert effects on host cells. Upon encountering the host cell surface, the initial attachment of bacteria is mediated by weak non-specific interactions which depend on the physicochemical and electrostatic interaction between the bacteria and the substrate. Thus, the primary attachment during the planktonic phase is weak and reversible that relies on the environmental factors like pH, ionic forces and temperature. 18,19 Finally, irreversible attachment takes place with the help of fimbria. 20 The cells that attach irreversibly to surfaces (i.e. not removed by gentle rinsing) undergo cell division and form microcolonies.

This leads to the formation of extracellular polymers that define a biofilm. 20 These strong interactions are mediated by molecules on the host-bacterial surface which are mostly sugars, proteins or lipids. Subsequently, the mature microbial cells are detached and dispersed. This process continues as the released microbial cells from a mature biofilms attach to new surfaces. Figure 1.2 depicts the stages of biofilm formation.

After adhesion, bacterium's ability to colonize its host highly depends on the mechanisms to withstand the host's mechanical and immunological clearance mechanisms.

Different regions of the human body are equipped with a natural clearance mechanism which protects the body from infections. Airflow in the respiratory tract, mucus removal from the airway, urine flow in the urinary tract, lining of tracts and tissue with antibodies are some of the natural cleansing mechanisms of the host. All these mechanisms prevent the host cells from the bacterial adhesion. In addition, there are host-derived anti-adhesion molecules which can specifically bind to the entrapped pathogens and prevent their attachment to the underlying epithelial cells. [21][22] For example, sulfated gastric mucin can prevent the bacterial binding to host cells. 23 However, all these natural mechanisms of the host cell are not sufficient to prevent the bacterial infection because bacteria with their adhesives resist this Vasudevan (2014). 20 mechanism and causes infections. To facilitate the infection process, bacteria have to be able to quickly and effectively attach to host cells. Subsequently, through biofilm formation, the bacterial cells acquire essential nutrients by entrapping minerals and host components such as fibrin, red blood cells (RBCs), and platelets which further enhance their ability to survive and infect the host. 24 The direct contact between bacteria and host cells also facilitates translocation of effector proteins from the bacterial cytoplasm into the host cell's cytoplasm. 25 The direct interaction should be long enough to allow the transfer of proteins over the time. The studies have shown that the transfer of proteins between host and bacterial cell follow a sequence and do not occur simultaneously. [26][27] Therefore, if bacteria are removed from host cells prematurely, infection can be prevented. 28 Secondly, microbes which will be disabled to bind to the host cell will not be subjected to sustained selective pressure, that may occur with antibiotic therapy. Persistent use of antibiotics usually kills the non-resistant bacteria while a small population of bacteria with the mutation in the binding site residues can multiply further. Adhesion of these bacteria to host cells can resist the natural cleaning mechanisms of the body, therefore allowing the bacteria to reach a population whereby an infection can start. Anti-adhesion strategies can prevent these hostpathogen interactions and facilitate the removal by the host. Several strategies can be employed to counter with bacterial adhesion to the host cells 18 (Figure 1.3) which involved interfering with the receptor biosynthesis, 29 coating the target substrate, 30 use of antiadhesion antibodies 31 or adhesin analogs. 32 Thus, anti-adhesion therapy could be used as a novel approach to prevent or treat bacterial infections by targeting bacterial virulence properties (e.g. adhesion, colonization, invasion) as alternative strategy to antibiotic therapy. 33 The mechanism of bacterial adhesion has evolved with the better understanding of the host pathogen interactions. The role of different carbohydrate-binding molecules (lectins, toxins, adhesins) as virulence factors specifically targeting their corresponding epitopes in the host-pathogen interface has been already identified. 34 The studies show that microbial colonization is largely associated with the glycoconjugate decoration of the host cells, named the 'glycocalyx.' [35][36]

Carbohydrates and lectins in bacterial adhesion

Several microorganisms use glycans as target on the host cell surface to establish interactions and initiate infection. Proteins such as adhesins or lectins at the surface also mediate the binding to host cells. In addition, toxins secreted by pathogens also exploit the adhesion (E) using either soluble molecules or designer microbes (F). In order to block the surface epitopes (which are required for binding), antibodies against bacterial adhesins can be used (G). Adapted from Krachler and co-workers (2013). 18 surface glycan for internalization using various mechanisms. Thus, a large number of pathogenic species of microorganisms depend on these interactions for infection.

Pathogen strategies to invade cells

Viruses, bacteria, fungi and parasites, employ carbohydrate-binding molecules (lectins, toxins, adhesins) as virulence factors to infect the host cells. Complex carbohydrates, also known as glycans decorate the surfaces of host cells and viruses mediating interactions and thus, promoting viral pathogenesis. [37][38][39] The structural complexity and diversity of the glycans present on surface glycoproteins or at the host cell surface primarily arises from the complex biosynthetic mechanism involving several enzymes of the host cells that display different expression patterns. [39][40][41] As a result of post-translational modifications, the complex glycans are attached by N-or O-glycosidic bonds to proteins or they are attached to lipids.

The envelop proteins or spike glycoproteins on the surface of many viruses [38][39]42 recognised the glycan motifs on the host cells surface and establish specific interactions that contribute to virus entry into host cells and also play role in host tissue tropism. In addition, the host cell surface glycans act as general attachment factors or primary receptors for different viruses that ultimately mediate viral infection and entry. Generally, acidic glycans such as linear heparan sulfate glycosaminoglycan polysaccharides or branched glycans with terminal sialic acid on host cell surfaces may serve as initial attachment factors to the host cell. Influenza virus hemagglutinin is the well-studied example of a viral glycan-binding protein which binds to sialic acid-containing glycans present on the host cell surface. 43 The binding event involves fusion of the viral envelope with the endosomal membrane followed by internalization of the virus by endocytosis and subsequent release of the viral RNA into the cytosol. The host glycanhemagglutinin interaction shows different specificities depending on the subtypes of influenza virus. These differences are caused due to structural differences in the hemagglutinin. For example, human strains of influenza-A and -B viruses specifically bind to cells with receptor expressing N-acetylneuraminic acid (Neu5Acα)2-6Gal while avian influenza viruses specifically bind to receptors with Neu5Acα2-3Gal-. Likewise, porcine strains show binding to the receptors with both types of linkages.

Similar to viruses, adhesion to host cell surface is an essential step of bacterial infection and pathogenesis which helps in developing resistance to natural defence mechanism and mechanical stress. Bacteria usually express several types of adherence factors (adhesins) that bind to the carbohydrate motifs on glycoproteins or glycosphingolipids located at the cell-surface. On bacterial surface, the thread like protein appendages known as fimbriae, act as adhesins and mediate carbohydrate-specific binding to the cell surfaces. 44 For instance, Escherichia coli uses FimH (adhesin) in fimbriae to bind specifically to mannosylated residues on human epithelial cells, thus facilitating urinary tract infection (UTI). [44][45] In addition, these adhesins play role in 'catch bonds': bonds that are strengthened by tensile mechanical force. 34 For example, a catch bond has been reported in E. coli where FimH binds to mannose on epithelial cells. 46 The underlying mechanism involves force-induced structural alterations in the receptor protein from a low-to a high-affinity conformation. These catch bonds are known to mediate urinary tract infections by causing strong adhesion due to sheer stress induced during urine flow. 46 Other than adhesins, toxins and lectins are soluble molecules known to facilitate the infection process. Toxins are proteins which consist of different subunits. They usually have glycan-binding subunits that allow the toxin to combine with membrane glycoconjugates followed by internalization to deliver the functionally active toxic subunit across the membrane which ultimately leads to cell death. AB5 toxin family is a classic example of such toxins. AB5 toxins in organisms such as E. coli and Bordella pertussis consist of cytotoxic ADP-ribosyltransferase (A) domain linked to five (B5) lectin subunits which recognizes endothelial surfaces. [47][48] Few examples of adhesins are illustrated in section 1.3.3 of this chapter.

The interface of the host and pathogen also consists of specialized carbohydratespecific proteins which play the complementary role of 'reader'. They were first reported at the end of 19th century, 49 for their ability to agglutinate erythrocytes. Later, in 1954, the term 'lectin' was proposed for these substances with blood group-specific agglutination properties. 50 In the 1990s, this term was generalised for all proteins of non-immune origin that can agglutinate erythrocytes or any other cell types. With the advancement in the elucidation of structural details of lectins, they were later classified into different classes. The initial classification was based on their specificities for carbohydrates, structural features and similarity in carbohydrate recognition domains (CRDs), or simply called as carbohydrate binding sites. [51][52][53] The most recent classification built on three different levels involves 35 lectin domain folds, 109 classes with 20% sequence similarity and 350 families with at least 70% sequence similarity. 54 According to their structural characters, animal lectins are divided into five main groups: C-type lectins, galectins, I-type lectins, pentranxins and P-type lectins.

C-type lectins depend on the presence of Ca 2+ ions and have conserved carbohydrate recognition domains with different specificities while the galectins bind to β-galactosides. 55 Itype lectins have immunoglobulins (Ig) like carbohydrate recognition domain (CRD) and pentraxins are composed of five monomers that produce pentameric lectins. P-type are the lectins have multiple domains including terminal one that recognize mannose 6-phosphate.

However, with the increase in the number of described sequences, this classification includes several new groups. 56 UniLectin3D is a curated database with classification of lectins on the basis of origin, fold and their specificity towards carbohydrates. [56][57] Thus, lectins present different types of folds such as β-sandwich, C-type, I-type (Ig fold) etc. The basic folds can serve as a platform to form several types of carbohydrate recognition domains (CRDs) with their own characteristic properties (Figure 1.4). Usually, lectins have millimolar affinity for the monosaccharide ligands which is often compensated by establishing multivalent interactions, mediated by the presence of several binding sites. Likewise, the topology of the lectin binding sites in space is also of importance for generating high specificity for the biological function.

Recent studies have demonstrated the role of specific arrangements of lectin binding sites that favour glycolipid clusters and ultimately affect the structure and dynamics of the cell membranes. 58 Moreover, the specificity and topology in lectins have marked them as interesting tools for generating engineered 'neolectins' with applications in diagnostics, therapy and material science etc. 59 Lectins in bacteria often show specificity to sugar epitopes presented by the glycocalyx. In addition, they play other complex roles in biofilm formation, quorum sensing etc. [60][61] Several pathogens use these proteins on the surface to initiate the infection process by adhesion, infection and toxicity. Some of the examples are E.coli, Staphylococcus aureus, Pseudomonas aeruginosa, Vibrio cholera, Clostridium tetani, etc. 62 Few examples of lectins from bacteria are discussed in later section (1.3.3) of this chapter.

Similar to viruses and bacteria, fungi with a parasitic life cycle can act as pathogens for plants or animals. Many fungal lectins are also reported to play role in the early stages of infection. Lectins from microfungi have been shown to be directly involved in human pathogenesis. 63 For example, a GlcNAc binding lectin from Paracoccidioides brasiliensis, known as paracoccin, binds to laminin and stimulates the release of TNF-α and nitric oxide by macrophages that leads to paracoccidioidomycosis. 64 Similarly, lectins from A. fumigatus and C. glabrata which are responsible for major hospital-acquired diseases are reported to display specific binding to human oligosaccharides. 65 FleA (AFL) located on the surface of conidia in A. fumigatus shows binding to fucosylated human blood group oligosaccharides. The lectin demonstrated a strong pro-inflammatory effect on human bronchial cells that could be antagonised by addition of exogenous fucose. 65 Intercellular adhesion molecule-3-Grabbing Non-integrin) is a major player in the recognition of pathogenic viruses thus also involved in the pathology of HIV infections. [68][69][70] DC-SIGN mediate adhesion and internalization of virus particles that allow trafficking to non-lysosomal compartments and virus persistence in a protected intracellular environment. DC-SIGN is attached to the cell membrane by a hydrophobic neck domain and that consist of a carbohydrate recognition domain with the primary binding site containing calcium ion as a co-factor. DC-SIGN binds to N-linked high mannose oligosaccharides such as Man9GlcNAc2. 71- 73 These are abundantly present on viral envelop glycoproteins. In addition, DC-SIGN recognizes fucosylated glycans such as Lewis-type and ABO antigens. 68 Besides this, DC-SIGN is also known as a receptor for many other viruses, such as zika, dengue, Ebola and coronaviruses. 68 Therefore, carbohydrate molecules and glycomimetic drugs which have potential to interfere with the viral adhesion are interesting for the treatment of viral infection.

Glycocalyx at the surface of human cells

The glycocalyx is an extracellular compartment that covers the cell membranes in some bacteria, epithelia and other cells. 36 It comprises of a huge variety of different glycoconjugates and acts as anchoring platform for pathogens (Figure 1.6). The most important adhesion components, expressed at the host-pathogen interface by numerous bacteria are the surface lectins (carbohydrate-binding proteins), which can bind to glycoconjugates present in the glycocalyx and act as virulence factors. [74][75][76] The glycocalyx consists of glycoproteins, proteoglycans such as glypicans and syndecans and glycosaminoglycans (GAGs) including heparan sulfate and hyaluronic acid. [74][75][76] The sialic acid residues are often located at the termini of glycan chains in surface glycoproteins or glycolipids and are targeted by pathogens for host cell-recognition, attachment and host specificity. For example, the human pathogen Staphylococcus aureus secreted superantigenlike proteins (SSLs) known as SSL5 and SSL11 shows binding to granulocytes and monocytes in a sialic acid dependent manner and promote infection by subverting the host immune system. 77 Their structures in complex with sialyl Lewis X confirmed the presence of carbohydrate-binding site located in a V-shaped surface depression on the C-terminal β-grasp domain. [78][79] Likewise, sialic acid acts as attachment factor of parvoviruses. The crystal structure complex of the minute virus of mice prototype strain (MVMp) capsid in complex with sialic acid showed that the parvovirus bind to sialic acid using a binding site at the twofold axis called the dimple. 35 In addition, A, B, and H antigens are also complex fucosylated oligosaccharides located at endothelial cells and erythrocytes of all individuals (Figure 1.7). 81 These antigens are also expressed in saliva, tears and mucus secretions in the digestive tract of individuals. 82 The studies in past indicates the susceptibility to diseases in certain phenotypes such as higher susceptibility of O phenotype for plague and cholera. 83 Since ABO structures present on the intestinal mucus, they play role as receptors for a wide range of pathogens. [84][85] The studied on Norwalk virus (responsible for acute gastroenteritis) demonstrated the role of fucosylated epitopes (such as H-type oligosaccharides) for infection. 86 These blood-grouprelated epitopes are also located in lung mucus. The presence of oligosaccharides in lungs Adapted from Imberty and co-workers (2008). 62 also depends on certain conditions such as genetic disorders, chronic bronchitis, cystic fibrosis (CF) etc. In certain diseases, fucosylation is increased in epithelial glycoconjugates and also in mucus in airways that can increase susceptibility towards infection by opportunistic bacteria. 87 For example, P. aeruginosa produced a soluble fucose-binding lectin which acts a virulence factor and displays high affinity for fucose and fucose-containing oligosaccharides such as Lewis a trisaccharide. [88][89] Apart from microbial colonization, the latest research has demonstrated the role of glycocalyx in various cellular processes (Figure 1.6). [35][36] It acts as the additional molecular filter for endothelium and plays an important role in sensing and communicating with their environment. For instance, endothelial tissue assembly is ensured by glycocalyx-mediated communication. 36 Likewise, glycoconjugates assist the immune system to recognise own and foreign cells, and act accordingly. Moreover, alterations of the glycosylation are a hallmark of diseases such as cancer. [91][92] Similarly, bacteria are surrounded by glycocalyx-enclosed microcolony 93 to protect them from harmful phagocytes by creating capsules and also help them to attach to host cells or surfaces via biofilm formation. Therefore, glycocalyx can be directly targeted in therapeutic contexts. The oligo-and polysaccharide structures that are expressed on cell surfaces are recognised as a 'glycocode'. 94 Thus, every glycan of glycocalyx can be presented as a 'message' to its environment. Hence, carbohydrates are recognised as 3 rd alphabet of life in parallel to nucleobases and amino acids. 95 However, the vast information held by the glycocode is yet to be investigated.

Bacterial adhesins and soluble lectins involved in adhesion

On bacterial surface, adhesins and lectins mediate carbohydrate-specific binding to the cell surfaces. E. coli is a known pathogen to cause a range of diseases such as urinary tract infections (UTIs), enteritis and meningitis. 90 percent diagnosed cases of UTIs are reported to be caused by Uropathogenic E. coli (UPEC). [96][97] The infection process involves multi step cascade that has been demonstrated in mouse cystitis model and human UTIs. 98 UPEC adhesion is mediated by type 1 fimbriae (pili) that binds to mannosylated glycoprotein receptors such as uroplakin-la (UPIa) located on the surface of urinary bladder mucosa. 99 This event prevents clearance of UPEC by shear stress of urine flow and promotes bacterial invasion. The pilus rod forms right-handed helical structure which consists of numerous immunological-like (Ig) FimA subunits with terminal tip composed of FimF, FimG and lectin FimH (Figure 1.8A). 100 The binding preference of FimH towards oligomannose has been used as the basis for rational drug design of α-mannoside-based FimH antagonists that has been tested in mouse models for their potential as UTI therapeutics. [101][102][103] In addition, pathogenic E. coli strains use and UclD. [START_REF] Spaulding | Selective depletion of uropathogenic E. coli from the gut by a FimH antagonist[END_REF] The binding specificities of these adhesions determine host and tissue tropism.

PapG adhesin is located at the tips of the fimbriae that mediates UPEC adhesion to host cell by recognizing the Galα1-4Gal epitope in the globo-series of glycolipids. [106][107][108] Similarly, a pilus adhesion known as UclD also has lectin function in E.coli. that interacts with O-linked glycans on host cells. [START_REF] Spaulding | Selective depletion of uropathogenic E. coli from the gut by a FimH antagonist[END_REF] Helicobacter pylori is another Gram-negative bacterium that colonizes the human gastric mucosa and causes symptomatic infection. 109 In some individuals, the infection can develop into gastric cancer, thus H. pylori is considered a carcinogen by the WHO. [START_REF] Dunne | Factors that mediate colonization of the human stomach by Helicobacter pylori[END_REF][111] It uses lectins to bind the glycosylated human gastic mucosa. The two lectins known as -blood group antigen-binding adhesin BabA and sialic acid (neuraminic acid)-binding adhesin SabA. BabA shows specificity towards fucosylated glycoconjugates such as Lewis B 112 (Figure 1.8B), while SabA binds to Lewis blood group antigens sialyl Lewis X and sialyl Lewis A. 113 The stomach lining displays increased expression of sialic acid after H. pylori has established a chronic infection. 111 LabA (also known as LacdiNAc-binding adhesin) is another adhesin of H. pylori that binds to LacdiNAc structures (GalNAcβ1-4GlcNAc) in the gastric mucosal layer. 114 This adhesion helps in retaining H. pylori in the mucosal layer and thus have role in persistence. 114 Streptococcus pneumoniae (pneumococcus) is an opportunistic pathogen responsible for infections like community acquired pneumonia, otitis media and bacteremia. The Pilus-1 proteins, RrgA, RrgB and RrgC of S. pneumoniae are known to play role in adhesion and infection. [115][116] The adhesin RrgA was shown to bind to α/β linked galactose, maltose/cellobiose and blood group A and H antigens as RrgC whilst RrgB shows binding to mannose. 117 In addition, the pathogen contains many complex surface proteins such as betagalactosidase BgaA, which specifically binds to terminal galactose residues with beta-1-4 linkage to glucose or N-acetylglucosamine in host cell and known to play an important role in bacterial adhesion. [118][START_REF] Limoli | BgaA acts as an adhesin to mediate attachment of some pneumococcal strains to human epithelial cells[END_REF][START_REF] Singh | Unravelling the Multiple Functions of the Architecturally Intricate Streptococcus pneumoniae β-galactosidase, BgaA[END_REF] Pseudomonas aeruginosa is also a Gram-negative opportunistic bacterium that is involved in acute and chronic lung infections. [START_REF] De Bentzmann | The Pseudomonas aeruginosa opportunistic pathogen and human infections[END_REF] This pathogen is highly resistant to many antibiotics, thus limiting the therapeutic options for the infections. P. aeruginosa utilizes lectins and adhesins for host-pathogen adhesion which is a critical step in initiating P.

aeruginosa pathogenesis. The soluble lectins LecA and LecB specifically bind to galactosides and fucosides, respectively. [START_REF] Imberty | Structures of the lectins from Pseudomonas aeruginosa: insight into the molecular basis for host glycan recognition[END_REF][START_REF] Cioci | Structural basis of calcium and galactose recognition by the lectin PA-IL of Pseudomonas aeruginosa[END_REF][START_REF] Mitchell | Structural basis for oligosaccharide-mediated adhesion of Pseudomonas aeruginosa in the lungs of cystic fibrosis patients[END_REF] Both lectins act as virulence factors and show cytotoxic effect on respiratory epithelial cells. [START_REF] Bajolet-Laudinat | Cytotoxicity of Pseudomonas aeruginosa internal lectin PA-I to respiratory epithelial cells in primary culture[END_REF] LecA interacts with α-galactosylated glycosphingolipids present in the lung epithelial cell membranes, while LecB binds to the fucosylated and mannosylated epitopes but preferentially to the Lewis a oligosaccharides. [START_REF] Perret | Structural basis for the interaction between human milk oligosaccharides and the bacterial lectin PA-IIL of Pseudomonas aeruginosa[END_REF] LecA and LecB are tetrameric lectins that require Ca 2+ ion for carbohydrate binding. LecA binds to α-D-galactosides with a submicromolar binding affinity thanks to one Ca 2+ ion while LecB shows strong micromolar affinity for L-fucose thanks to two Ca 2+ ions. Another wellknown example of bacterial soluble lectin is BambL from Burkholderia ambifaria. [START_REF] Audfray | Fucose-binding lectin from opportunistic pathogen Burkholderia ambifaria binds to both plant and human oligosaccharidic epitopes[END_REF] BambL is a fucose-binding lectin with binding affinity in low micromolar range for monosaccharide. Like P. aeruginosa, this pathogen is also responsible for opportunistic infections that affect immune-compromised or cystitis fibrosis patients. Given an increasing rate of antibiotic resistance, these lectins are interesting targets to block bacterial adhesion using anti-adhesive molecules and treat drug resistant infections. [START_REF] Ligeour | Mannose-centered aromatic galactoclusters inhibit the biofilm formation of Pseudomonas aeruginosa[END_REF][START_REF] Wagner | Novel Strategies for the Treatment of Pseudomonas aeruginosa Infections[END_REF][START_REF] Meiers | Directing Drugs to Bugs: Antibiotic-Carbohydrate Conjugates Targeting Biofilm-Associated Lectins of Pseudomonas aeruginosa[END_REF] The presence of surface adhesins, lectins and toxins in pathogens formed the basis of anti-adhesion approach to counteract the drug-resistant infections at the initial stage. One of the basic examples of anti-adhesive therapy that exists in Nature is related to high concentrations of human milk oligosaccharides (HMOs) in milk from breast-feeding women.

The HMOs among other function, compete with glycoconjugates in infant guts for binding to receptors on pathogens and protect the breast-fed infants. [START_REF] Sharon | Carbohydrates as future anti-adhesion drugs for infectious diseases[END_REF] Very recent studies also demonstrated that SARS-CoV-2 uses its spike glycoprotein to target human lectin DC-SIGN and others. 132-133 In another study, it was also shown that the recognition of spike glycoprotein by DC-SIGN can be inhibited by a glycomimetic antagonist. 134 The anti-adhesive strategies aimed at blocking the interaction between host and pathogen offer an attractive means of preventing infection at an early stage. Therefore, highaffinity anti-adhesive therapeutic agents can be designed to mimic and compete against epitopes targeted by virulence factors. In case of lectins, glycomimetic ligands can act efficiently by competitive inhibition to impede the adhesion of pathogens.

Glycans and glycomimetics as anti-adhesion compounds

It was more than four decades ago when mannose receptor in host cell were reported to mediate adhesion of E. coli to human mucosal cells. [START_REF] Ofek | Adherence of Escherichia coli to human mucosal cells mediated by mannose receptors[END_REF] Since then, the carbohydrate specificities of many pathogens have been determined, which provided the bases for the rational design of anti-adhesive molecules. [START_REF] Sharon | Carbohydrates as future anti-adhesion drugs for infectious diseases[END_REF] While carbohydrates are exciting targets for drug design/development, native carbohydrates also suffer from a number of drawbacks when considered as therapeutic agents [START_REF] Zhang | Carbohydrate drugs: current status and development prospect[END_REF][START_REF] Ernst | From carbohydrate leads to glycomimetic drugs[END_REF][START_REF] Hevey | Strategies for the Development of Glycomimetic Drug Candidates[END_REF] such as poor pharmacokinetic properties [START_REF] Ernst | From carbohydrate leads to glycomimetic drugs[END_REF][START_REF] Magnani | Glycomimetic drugs--a new source of therapeutic opportunities[END_REF] and poor or limited oral bioavailability. The passive permeation through intestinal enterocyte layer requires drug-like molecules with acceptable molecular mass, limited polar surface area and low numbers of H-bond donors and acceptors as per the Lipinski and Veber rules. [START_REF] Lipinski | Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings[END_REF][START_REF] Veber | Molecular properties that influence the oral bioavailability of drug candidates[END_REF] Due to high polarity of carbohydrates, they are unable to cross passively through epithelial layer in the small intestine that results into poor oral bioavailability. In addition, bulk solvent can easily displace the native carbohydrate ligands that often bind to shallow binding sites of the lectins. The weak binding results into higher koff rates of lectin interactions that leads to a short residence time. In addition, once available in bloodstream by parenteral administration, carbohydrates undergo fast renal excretion, which contributes to poor pharmacokinetic profile.

Various strategies to design glycomimetic agents have been used to address and overcome these properties of carbohydrates. Glycomimetics are compounds which mimic the structure and function of native carbohydrates, developed for their application as therapeutic candidates for carbohydrate-binding proteins such as lectins. [START_REF] Ernst | From carbohydrate leads to glycomimetic drugs[END_REF][START_REF] Magnani | Glycomimetic drugs--a new source of therapeutic opportunities[END_REF] A rational approach to design glycomimetic agents can enhance the binding affinities, bioavailability and also improve plasma half-lives of the molecules. To improve the binding-affinity and selectivity against a target, glycomimetics usually establish additional interactions which are not present in the native counterpart. [START_REF] Hevey | Strategies for the Development of Glycomimetic Drug Candidates[END_REF] Additional strategies such as reducing ligand polarity, ligand preorganization, optimization of entropic and enthalpic binding components can be employed to overcome the poor drug-like properties of the carbohydrates. [START_REF] Hevey | Strategies for the Development of Glycomimetic Drug Candidates[END_REF] A rational approach for glycomimetic design needs sufficient information about the targets to understand the protein-ligand interactions. [START_REF] Ernst | From carbohydrate leads to glycomimetic drugs[END_REF] The most sophisticated methods to study ligand-protein binding involves X-ray crystallography, nuclear magnetic resonance (NMR) experiments and molecular modelling methods. [START_REF] De Bentzmann | Monitoring lectin interactions with carbohydrates[END_REF][START_REF] Pérez | Carbohydrate-protein interactions: molecular modeling insights[END_REF][START_REF] Del | Protein-carbohydrate interactions studied by NMR: from molecular recognition to drug design[END_REF] The structural details convey important information about the ligand binding mode and important moieties/functional groups that are available for further modifications.

In addition, protein crystal structures also provide information about the amino acid residues in the vicinity of the binding site which can be explored to establish additional interactions with the ligands. In the absence of crystal structure, computational approaches such as development of homology models can be useful whenever reasonable because of high sequence identity with proteins with known 3D structure. In addition, information related to ligand binding epitope and conformation can be obtained from saturation transfer difference (STD) NMR and transfer Nuclear Overhauser Effect (NOE) methods. [START_REF] Hemmi | NMR Analysis of Carbohydrate-Binding Interactions in Solution: An Approach Using Analysis of Saturation Transfer Difference NMR Spectroscopy[END_REF] Other than direct interactions between the ligand and protein surface, particularly in the targets like lectins, it is important to consider the role of structural waters on ligand binding. Usually, the presence of structural water molecules, which are highly ordered and present in the binding site can afford favourable enthalpic gains, thus they are important for the ligand design. [START_REF] Modenutti | Using crystallographic water properties for the analysis and prediction of lectin-carbohydrate complex structures[END_REF][START_REF] Sager | What contributes to an effective mannose recognition domain?[END_REF] While the weakly bound water molecules in the binding site can have significant entropic penalties by restricting their movement in the bound state. Therefore, highly conserved water molecules are usually considered important in glycomimetic design against several carbohydrate-binding proteins, for example L-arabinose binding protein and FimH. [START_REF] Modenutti | Using crystallographic water properties for the analysis and prediction of lectin-carbohydrate complex structures[END_REF][START_REF] Quiocho | Substrate specificity and affinity of a protein modulated by bound water molecules[END_REF] By combining information from multiple approaches, appropriate strategies like bioisostere replacement, derivatization, or deoxygenation etc. can be used for the development of glycomimetics. [START_REF] Hevey | Strategies for the Development of Glycomimetic Drug Candidates[END_REF] In addition, ligand design strategies related to the development of covalent inhibitors can be used for the enhancement of the binding affinity of glycomimetic ligands. [START_REF] Wagner | Covalent Lectin Inhibition and Application in Bacterial Biofilm Imaging[END_REF] Likewise, multivalency can also enhance the binding affinity of the ligands by mimicking the multivalent presentation of native ligands. [START_REF] Imberty | Glycomimetics and Glycodendrimers as High Affinity Microbial Anti-adhesins[END_REF][START_REF] Bertolotti | Polyvalent C-glycomimetics based on l-fucose or d-mannose as potent DC-SIGN antagonists[END_REF][START_REF] Bücher | Heteromultivalent Glycooligomers as Mimetics of Blood Group Antigens[END_REF][START_REF] Cecioni | Glycomimetics versus multivalent glycoconjugates for the design of high affinity lectin ligands[END_REF][START_REF] Moog | Polymeric Selectin Ligands Mimicking Complex Carbohydrates: From Selectin Binders to Modifiers of Macrophage Migration[END_REF][START_REF] Zhang | Synthesis of Fucosylated Chondroitin Sulfate Glycoclusters: A Robust Route to New Anticoagulant Agents[END_REF] The approach has been implemented in designing multivalent inhibitors, against the targets in pathogens such as H. pylori, [START_REF] Simon | Inhibition of Helicobacter pylori binding to gastrointestinal epithelial cells by sialic acid-containing oligosaccharides[END_REF] E.coli and P. aeruginosa. [START_REF] Horst | Binding inhibition of type 1 fimbriae to human granulocytes: a flow cytometric inhibition assay using trivalent cluster mannosides[END_REF][START_REF] Palmioli | Targeting Bacterial Biofilm: A New LecA Multivalent Ligand with Inhibitory Activity[END_REF] Multivalency usually enhances the binding affinity and selectivity of the ligands using different mechanisms which involved chelation, statistical rebinding effects or clustering of soluble binding partners. [START_REF] García-Moreno | The Impact of Heteromultivalency in Lectin Recognition and Glycosidase Inhibition: An Integrated Mechanistic Study[END_REF][START_REF] Ordanini | Designing nanomolar antagonists of DC-SIGN-mediated HIV infection: ligand presentation using molecular rods[END_REF] In chelation, the molecule can engage two or more binding sites of a target simultaneously. In some cases, multivalency improves binding by increasing local concentration of the ligand which is called statistical rebinding. This mechanism reduces the off-rate of the ligands and thus increases the binding affinity. In addition, receptor clustering also plays role in multivalency. In this case, the multivalent ligands lead to recruitment and aggregation or precipitation of masses of protein and also known to elicit signalling cascades. [START_REF] Lundquist | The cluster glycoside effect[END_REF][START_REF] Kiessling | Synthetic multivalent ligands as probes of signal transduction[END_REF] The multivalent scaffolds of the ligands should be carefully designed in order to have flexibility and spacing that allows correct fit into the binding site and also minimize the entropic costs of ligand binding. A recent study has demonstrated the role of scaffold hydrophobicity in the affinity of multivalent constructs. [START_REF] Boden | Sequence-Defined Introduction of Hydrophobic Motifs and Effects in Lectin Binding of Precision Glycomacromolecules[END_REF] In another excellent study on the design of multivalent glycomimetic antagonists of DC-SIGN using molecular rods, antagonists with nanomolar binding affinity have been reported. [START_REF] Ordanini | Designing nanomolar antagonists of DC-SIGN-mediated HIV infection: ligand presentation using molecular rods[END_REF][START_REF] Berzi | Pseudo-Mannosylated DC-SIGN Ligands as Immunomodulants[END_REF] In this approach, a rigidified core based on phenylene-ethynylene units were designed to an ideal length (approx. 4 nm) for chelation to bridge the carbohydrate recognition domains (CRD) on the neighbouring subunits of DC-SIGN. The excellently designed construct was also able to individually probe the effects of ligand, rigid rod, and proximity etc.

The development of glycomimetics as therapeutic agents have been successful for several drug targets. These drug molecules already reached the market after successful clinical trials. For example, Oseltamivir (Tamiflu), Zanamivir (Relenza) used in the treatment of influenza infection (Figure 1.9) are glycomimetic inhibitors of influenza neuraminidase. 165- 166 Another drug named Miglustat is a glycomimetics inhibitor of glucosylceramide synthase used in the treatment of type I Gaucher disease to prevent the harmful accumulation of glucosylceramide. [START_REF] Cox | Novel oral treatment of Gaucher's disease with N-butyldeoxynojirimycin (OGT 918) to decrease substrate biosynthesis[END_REF] The drugs used as α-glucosidase inhibitors (miglitol, voglibose, acarbose)

for the treatment of diabetes and lysosomal storage disorders [START_REF] Campbell | Miglitol: assessment of its role in the treatment of patients with diabetes mellitus[END_REF][START_REF] Chen | Voglibose (Basen®, AO-128), One of the Most Important α-Glucosidase Inhibitors[END_REF][START_REF] Truscheit | Chemistry and Biochemistry of Microbial α-Glucosidase Inhibitors[END_REF] are also some examples of glycomimetics. Various studies on design of multivalent constructs are focused towards fucose-binding soluble receptors in pathogens, in order to have improved therapeutic effect on patients with cystic fibrosis (CF). Alternatively, these studies can also afford design and synthesis of simplified mimetics of sialyl Lewis X that can mimic its native structure. 154

FimH and LecA antagonists as anti-adhesion compounds

In 1977, the anti-adhesive activity of mannose was described in detail by Ofek, et al.

for E. coli. [START_REF] Ofek | Adherence of Escherichia coli to human mucosal cells mediated by mannose receptors[END_REF] Later in vivo activity of methyl α-D-mannoside was reported in a UTI mouse model. [START_REF] Aronson | Prevention of colonization of the urinary tract of mice with Escherichia coli by blocking of bacterial adherence with methyl alpha-D-mannopyranoside[END_REF] Since FimH was identified as an important target for the development of antiadhesive therapeutic strategies, various studies were performed to investigate the effects of FimH antagonists. [START_REF] Waksman | Structural biology of the chaperone-usher pathway of pilus biogenesis[END_REF][START_REF] Firon | Carbohydrate specificity of the surface lectins of Escherichia coli, Klebsiella pneumoniae, and Salmonella typhimurium[END_REF][START_REF] Neeser | Oligomannoside-type glycopeptides inhibiting adhesion of Escherichia coli strains mediated by type 1 pili: preparation of potent inhibitors from plant glycoproteins[END_REF] In the following years, with an aim to improve binding affinity, various structural modifications resulted mono-and polyvalent inhibitors involving chemically modified derivatives of α-D-mannosides. For example, Bouckert et al. reported a series of alkyl α-D-mannosides as potent FimH antagonists (1 Figure 1.10). [START_REF] Bouckaert | Receptor binding studies disclose a novel class of high-affinity inhibitors of the Escherichia coli FimH adhesin[END_REF] The best representative of alkyl mannosides series, n-heptyl α-D-mannoside, was used later as reference compound. The studies involving aromatic aglycones provided the basis for design of molecules to reach the hydrophobic tyrosine gate formed by Tyr48, Tyr137 and Ile52 (2, Figure 1.10). [START_REF] Firon | Aromatic alpha-glycosides of mannose are powerful inhibitors of the adherence of type 1 fimbriated Escherichia coli to yeast and intestinal epithelial cells[END_REF] These Adapted from Hevey (2019). [START_REF] Hevey | Strategies for the Development of Glycomimetic Drug Candidates[END_REF] finding were further rationalized to design squaric acid derivatives (3, Figure 1.10). [START_REF] Sperling | Evaluation of the carbohydrate recognition domain of the bacterial adhesin FimH: Design, synthesis and binding properties of mannoside ligands[END_REF] Later on the basis of crystallized complexes with biphenyl mannosides (showing π-π stacking with Tyr48, Tyr137), potent inhibitors were obtained (4, Figure 1.10). [START_REF] Han | Structure-based drug design and optimization of mannoside bacterial FimH antagonists[END_REF] Further, substitution and extended aromatic moieties showed improvement in the binding affinity. The final ligands showed affinities in low nanomolar range however indicated poor oral bioavailability (5, 6, Figure 1.10). [START_REF] Jiang | Antiadhesion Therapy for Urinary Tract Infections-A Balanced PK/PD Profile Proved To Be Key for Success[END_REF][START_REF] Pang | FimH antagonists -solubility vs. permeability[END_REF] The attempts to optimize oral bioavailability using prodrug approach and structural modifications such as biosisosteric replacement improved the oral bioavailability in vivo. However, the final molecules with better therapeutic potential showed poor solubility. Further structural modifications improved the solubility and the final antagonist GSK3882347 (structure not disclosed) has already entered Phase 1 clinical trial.

LecA is another interesting target for the design of anti-adhesive molecules against P.

aeruginosa. In Nature, LecA displays binding affinity towards α-linked galactosides, whereas studies using synthetic glycomimetics showed that it can bind to β-aryl galactosides (1, Figure 1.11). [START_REF] Pang | FimH antagonists -solubility vs. permeability[END_REF][START_REF] Rodrigue | Aromatic thioglycoside inhibitors against the virulence factor LecA from Pseudomonas aeruginosa[END_REF][START_REF] Kadam | Structure-Based Optimization of the Terminal Tripeptide in Glycopeptide Dendrimer Inhibitors of Pseudomonas aeruginosa Biofilms Targeting LecA[END_REF] Based on rational drug-design approaches, various mono-and multivalent glycomimetics have been designed for LecA. The ligands are composed of a galactose moiety

Burkholderia cenocepacia: An opportunistic pathogen that targets glycans 1.5.1 The Burkholderia cepacia complex

Opportunistic infections usually occur due to a declined in innate or adaptive immune responses. This situation allows organisms with usually low virulence, to initiate infection which can be life threatening. [START_REF] José | Opportunistic bacterial, viral and fungal infections of the lung[END_REF][START_REF] Zanoni | Update on opportunistic infections in the era of effective antiretroviral therapy[END_REF] The most common type of infection caused by these opportunistic pathogens involves lung infection that is a major cause of morbidity and mortality for immunocompromised patients having other medical conditions like HIV, haematological malignancy, aplastic anaemia, chemotherapy treatment, cystic fibrosis (CF), recipients of solid-organ or stem cell transplants etc. The infection caused by such opportunistic pathogens depends on the type and level of immune defect in the host.

B. cenocepacia is a Gram negative bacterium which is a member of the Burkholderia cepacia complex (Bcc), [START_REF] De Smet | Burkholderia stagnalis sp. nov. and Burkholderia territorii sp. nov., two novel Burkholderia cepacia complex species from environmental and human sources[END_REF][START_REF] Ong | Burkholderia paludis sp. nov., an Antibiotic-Siderophore Producing Novel Burkholderia cepacia Complex Species, Isolated from Malaysian Tropical Peat Swamp Soil[END_REF][START_REF] Vanlaere | Burkholderia latens sp. nov., Burkholderia diffusa sp. nov., Burkholderia arboris sp. nov., Burkholderia seminalis sp. nov. and Burkholderia metallica sp. nov., novel species within the Burkholderia cepacia complex[END_REF][START_REF] Weber | Volcanic Soils as Sources of Novel CO-Oxidizing Paraburkholderia and Burkholderia: Paraburkholderia hiiakae sp. nov., Paraburkholderia metrosideri sp. nov., Paraburkholderia paradisi sp. nov., Paraburkholderia peleae sp. nov., and Burkholderia alpina sp. nov. a Member of the Burkholderia cepacia Complex[END_REF] a group of 22 bacterial species. Bcc species can be found in natural sources including water, soil and vegetation, and thus, are widely spread in the Nature. These bacteria can have both beneficial and detrimental effects on plants [START_REF] Mahenthiralingam | The multifarious, multireplicon Burkholderia cepacia complex[END_REF] but they are also identified as opportunistic human pathogens. In particular, B. cenocepacia is responsible for deadly infections in patients with immunocompromised conditions like chronic granulomatous disease [START_REF] Winkelstein | Chronic granulomatous disease. Report on a national registry of 368 patients[END_REF] and cystic fibrosis (CF) resulting in severe decline in lung functions. [START_REF] Butler | Burkholderia cepacia and cystic fibrosis: do natural environments present a potential hazard?[END_REF] Further progression involves systemic infection known as cepacia syndrome which is characterized by an uncontrolled deterioration of lungs with septicaemia and necrotizing pneumonia that usually leads to early death. [START_REF] Mahenthiralingam | The multifarious, multireplicon Burkholderia cepacia complex[END_REF] B. cenocepacia are highly resistant to different classes of the existing antibiotics. [START_REF] Mahenthiralingam | The multifarious, multireplicon Burkholderia cepacia complex[END_REF] The pathogenicity of these bacteria is caused by several virulence determinants. [START_REF] Loutet | A decade of Burkholderia cenocepacia virulence determinant research[END_REF][START_REF] Sousa | Burkholderia cepacia Complex: Emerging Multihost Pathogens Equipped with a Wide Range of Virulence Factors and Determinants[END_REF] In addition, they can adapt to different environmental changes inside the infected lungs resulting in more challenging treatments of infections. Inside the lungs, these pathogenic bacteria get exposed to different stressful conditions as consequence of host immune responses, antimicrobials, fluctuation in nutrient oxygen level, low pH and the presence of co-infecting microbes. [START_REF] Hogardt | Adaptation of Pseudomonas aeruginosa during persistence in the cystic fibrosis lung[END_REF][START_REF] Döring | Differential adaptation of microbial pathogens to airways of patients with cystic fibrosis and chronic obstructive pulmonary disease[END_REF][START_REF] Cullen | Bacterial Adaptation during Chronic Respiratory Infections[END_REF] Therefore, due to longterm infection, the bacterial population undergoes transcriptional reprogramming that causes genetic and phenotypic alterations leading to heterogeneous bacterial community which is very difficult to eradicate with the existing drugs. [START_REF] Lieberman | Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes[END_REF][START_REF] Madeira | Quantitative proteomics (2-D DIGE) reveals molecular strategies employed by Burkholderia cenocepacia to adapt to the airways of cystic fibrosis patients under antimicrobial therapy[END_REF][START_REF] Moreira | Burkholderia dolosa phenotypic variation during the decline in lung function of a cystic fibrosis patient during 5.5 years of chronic colonization[END_REF][START_REF] Nunvar | What matters in chronic Burkholderia cenocepacia infection in cystic fibrosis: Insights from comparative genomics[END_REF][START_REF] Silva | Mucoid morphotype variation of Burkholderia multivorans during chronic cystic fibrosis lung infection is correlated with changes in metabolism, motility, biofilm formation and virulence[END_REF][START_REF] Silva | Long-Term Evolution of Burkholderia multivorans during a Chronic Cystic Fibrosis Infection Reveals Shifting Forces of Selection[END_REF] Therefore, it is highly important to identify new drug targets and apply a rational approach to design new drug molecules to effectively treat the infections caused by these drug resistant pathogens. The information from the available mutant library and genomic studies can help in the identification of essential genes responsible for virulence, adaptation and antimicrobial resistance. [START_REF] Gislason | Competitive Growth Enhances Conditional Growth Mutant Sensitivity to Antibiotics and Exposes a Two-Component System as an Emerging Antibacterial Target in Burkholderia cenocepacia[END_REF][START_REF] Wong | Candidate Essential Genes in Burkholderia cenocepacia J2315 Identified by Genome-Wide TraDIS[END_REF] It can be very useful to identify new drug targets for the purpose of antimicrobial drug discovery against these pathogenic bacteria.

Lectins from Burkholderia cenocepacia

A similarity search based on the lecB sequence (from P. aeruginosa) identified the genes coding for related proteins in the genomes of Burkholderia species belonging to the Bcc and also reported in CF isolates. [START_REF] Lameignere | Structural basis for mannose recognition by a lectin from opportunistic bacteria Burkholderia cenocepacia[END_REF] The analysis of B. cenocepacia strain J2315 identified three genes coding for LecB-like proteins on chromosome 2 with sizes of 384, 732 and 816 base pairs (bp). [START_REF] Lameignere | Structural basis for mannose recognition by a lectin from opportunistic bacteria Burkholderia cenocepacia[END_REF] A fourth one (877 bp) has been identified on chromosome 3 of B. cepacia R18194-1TCC17660. These four putative lectins are referred to as BC2L-A (128 aa), BC2L-B (244 aa), BC2L-C (272 aa), and BC2L-D (289 aa). [START_REF] Lameignere | Structural basis for mannose recognition by a lectin from opportunistic bacteria Burkholderia cenocepacia[END_REF][START_REF] Sulák | Burkholderia cenocepacia BC2L-C is a super lectin with dual specificity and proinflammatory activity[END_REF] All four polypeptides display a LecB-like domain at the C-terminal domain (Figure 1.12). An additional domain (120-160 aa) at the Nterminus was also identified in three polypeptides (except BC2L-A). These three N-terminal domains were different from each other and do not show similarity to any other protein as well. The N-terminal domain of BC2L-C has been characterized as a novel fucose binding domain with a TNF-α-like fold. [START_REF] Sulák | A TNF-like trimeric lectin domain from Burkholderia cenocepacia with specificity for fucosylated human histo-blood group antigens[END_REF] The initial studies of these lecB-like lectin family was done for BC2L-A. [START_REF] Lameignere | Structural basis for mannose recognition by a lectin from opportunistic bacteria Burkholderia cenocepacia[END_REF] The lecB-like gene, bclA was identified on the six Burkholderia strains, showed 32% similarity with lecB. [START_REF] Lameignere | Structural basis for mannose recognition by a lectin from opportunistic bacteria Burkholderia cenocepacia[END_REF] BC2L-A have successfully purified n native form from B. cenocepacia strain J2315, and later cloned in E. coli to produce recombinant form. The experimental studies also revealed that BC2L-A specifically binds to mannose. [START_REF] Lameignere | Structural basis for mannose recognition by a lectin from opportunistic bacteria Burkholderia cenocepacia[END_REF] Similar to LecB lectin from P. aeruginosa. [START_REF] Mitchell | Structural basis for oligosaccharide-mediated adhesion of Pseudomonas aeruginosa in the lungs of cystic fibrosis patients[END_REF] it consists of one carbohydrate recognition domain with two Ca 2+ ions directly involved in ligand binding. The difference in their specificities has been reported due to the presence of different residues in a loop featuring two serine residues (22 & 23) in LecB, and two alanine residues (29 & 30) in BC2L-A. [START_REF] Adam | Engineering of PA-IIL lectin from Pseudomonas aeruginosa -Unravelling the role of the specificity loop for sugar preference[END_REF] Moreover, structural comparison of both the lectins shows different features (Figure 1.13). The lectin LecB from P. aeruginosa forms homotetramers while the Burkholderia lectins BC2L-A and the C-terminal domain of BC2L-C are known to form homodimer [START_REF] Lameignere | Structural basis for mannose recognition by a lectin from opportunistic bacteria Burkholderia cenocepacia[END_REF][START_REF] Sulák | A TNF-like trimeric lectin domain from Burkholderia cenocepacia with specificity for fucosylated human histo-blood group antigens[END_REF] (Figure 1. 13,A,B,C).

In order to investigate the location of these lectins, studies have been already performed which indicate that they are present in the extracellular medium, although the secretion system is not identified. [START_REF] Sulák | A TNF-like trimeric lectin domain from Burkholderia cenocepacia with specificity for fucosylated human histo-blood group antigens[END_REF] In particular, these studies demonstrated that BC2L-B and -C lectins are released from the bacterial cells upon mannose treatment. These results provide the important clue that they are located at the external envelope of the bacteria. However, BC2L-A was not detected on the surface probably due to its much lower expression cenocepacia indicating its presence in the biofilm and possible role in cell adhesion. In addition, it has been reported that BC2L-A and BC2L-C bind to the epitopes [START_REF] Sulák | A TNF-like trimeric lectin domain from Burkholderia cenocepacia with specificity for fucosylated human histo-blood group antigens[END_REF] which are abundantly found as a component of the B. cenocepacia lipopolysaccharide (LPS) [START_REF] De Soyza | Chemical and biological features of Burkholderia cepacia complex lipopolysaccharides[END_REF] or other Gram negative bacteria. [START_REF] Silipo | The diversity of the core oligosaccharide in lipopolysaccharides[END_REF] The structural and functional details of BC2L-A was further used to probe the mannosides and glycomimetics for the successful inhibition. [START_REF] Beshr | Development of a competitive binding assay for the Burkholderia cenocepacia lectin BC2L-A and structure activity relationship of natural and synthetic inhibitors[END_REF][START_REF] Csávás | Tri-and tetravalent mannoclusters cross-link and aggregate BC2L-A lectin from Burkholderia cenocepacia[END_REF][START_REF] Lameignere | Structural basis of the affinity for oligomannosides and analogs displayed by BC2L-A, a Burkholderia cenocepacia soluble lectin[END_REF] Moreover, BC2L-A has been used as a model to perform optimization of multivalent glycomimetic design. [START_REF] Reynolds | Influence of ligand presentation density on the molecular recognition of mannose-functionalised glyconanoparticles by bacterial lectin BC2L-A[END_REF] These studies have been extended to understand the role of BC2L-B and -C in the virulence of B. cenocepacia. The transcriptomic analysis along the chronic infection suggests that of genes for BC2L-B and -C were up regulated while the corresponding gene for BC2L-A was down regulated. These results indicate the possibility of secondary roles of the additional N-termini in BC2L-B and -C. [START_REF] Inhülsen | Identification of functions linking quorum sensing with biofilm formation in Burkholderia cenocepacia H111[END_REF][START_REF] Schmid | The AHL-and BDSF-dependent quorum sensing systems control specific and overlapping sets of genes in Burkholderia cenocepacia H111[END_REF] The experimental studies demonstrated that operon bclACB coding for the three lectins (BC2L-A, -B, -C) is regulated by quorum sensing which ultimately regulates the biofilm formation and plays an important role in maintaining the structure of biofilm. [START_REF] Inhülsen | Identification of functions linking quorum sensing with biofilm formation in Burkholderia cenocepacia H111[END_REF][START_REF] Schmid | The AHL-and BDSF-dependent quorum sensing systems control specific and overlapping sets of genes in Burkholderia cenocepacia H111[END_REF] Further, gene knock-out strategies confirmed that lack of any one of the lectins led to defective biofilm formation. [START_REF] Diggle | The galactophilic lectin, LecA, contributes to biofilm development in Pseudomonas aeruginosa[END_REF] The results from these studies indicate that these lectins could be interesting targets for drug design.

To further decipher the strategies used by B. cenocepacia to recognize glycans, the investigation of the N-terminal domain of BC2L-C was also important. These studies revealed a superlectin with dual specificity and proinflammatory activity. [START_REF] Sulák | Burkholderia cenocepacia BC2L-C is a super lectin with dual specificity and proinflammatory activity[END_REF] BC2L-C consists of lectin domains with 272 amino acids long peptide sequence. The C-terminal domain consists of 116 residues that shows 43% sequence identity with LecB and uses two-calcium ions for sugar binding, hence considered as "Lec-B like" lectin. In addition, the presence of Ala-Ala-Asn sequence in the "specificity loop" suggests its specificity for mannosides. [START_REF] Adam | Engineering of PA-IIL lectin from Pseudomonas aeruginosa -Unravelling the role of the specificity loop for sugar preference[END_REF] The N-terminal domain of BC2L-C (BC2L-C-nt) consists of 130 amino acids which are separated from the LecBlike C-terminal domain by a 26 amino acid linker rich in glycine and serine residues. This domain displays sequence identity up to 92 percent with other species of Burkholderia and only one hypothetical protein from another bacterium Photorhabdus luminescens, displayed 59 % sequence identity. [START_REF] Duchaud | The genome sequence of the entomopathogenic bacterium Photorhabdus luminescens[END_REF] In order to study its structure and function, Šulák and co-workers expressed the recombinant form of BC2L-C-nt in E.coli. The initial design consisted of the sequence coding for the 156 amino acids of BC2L-C-nt (including linker) and a C-terminal histidine (HisTag) to facilitate purification, which resulted a 187 amino acid long polypeptide (19.26 kDa). The size exclusion chromatography eluted a 58 kDa protein which indicated a homotrimeric assembly. Further characterization of the protein revealed its specific millimolar affinity towards L-fucose by surface plasmon resonance (SPR). The construct was further probed against a glycan array, indicating the preference for fucosylated histo-blood group epitopes. In addition, isothermal titration calorimetry (ITC) showed human oligosaccharides binding affinities in micromolar range (Table 1.2). Greek-key topology usually reported for human tumor necrosis factor (TNF)-like proteins, [START_REF] Réty | The Crystal Structure of the <em>Bacillus anthracis Spore Surface Protein BclA Shows Remarkable Similarity to Mammalian Proteins[END_REF] despite no sequence identity. In the trimeric structure, three identical binding sites could be identified at each interface between two adjacent monomers (Figure 1.

14). The key residues

Tyr48, Ser82, Thr83, Arg85 from one chain and Tyr58, Thr74, Tyr75, Arg111 from the neighbouring protomer play an important role in the ligand binding. In addition, water mediated interactions to bridge the sugar and the protein have been reported. Other than MeSe-α-L-Fuc, crystallized complexes with larger ligands were not available.

In another study from Šulák and co-workers attempted to uncover the whole architecture of superlectin, recombinant C-terminal domain (BC2L-C-ct) and full protein were expressed and probed for the specificities towards sugars. The protein showed structural similarities with BC2L-A, and also displayed affinity for different mannosides and manno-configured heptose ligands (Table 1.2 ). [START_REF] Sulák | Burkholderia cenocepacia BC2L-C is a super lectin with dual specificity and proinflammatory activity[END_REF] Thus, experimental studies showed that the whole lectin also binds to both types of oligosaccharides. In addition, a crystal structure of the recombinant BC2L-C-ct dimeric lectin domain in the apo form was obtained (PDB code 2XR4,

Figure 1.13 C).

In order to understand the binding of mannosides, the crystal structure was further used for computational docking to generate the complex. [START_REF] Sulák | Burkholderia cenocepacia BC2L-C is a super lectin with dual specificity and proinflammatory activity[END_REF] Structural elucidation by small angle X-ray scattering (SAXS) and electron microscopy (EM) revealed a flexible hexameric arrangement of the lectin, which accounted for the (three) dimeric C-terminal and [START_REF] Sulák | A TNF-like trimeric lectin domain from Burkholderia cenocepacia with specificity for fucosylated human histo-blood group antigens[END_REF] (two) trimeric N-terminal domains (Figure 1.15). This unique hexameric architecture appears suitable for cross-linking between bacteria and epithelial cells. The flexible linker connecting two domains may also assist in adapting a suitable conformation under shear force and allow tight binding as already reported for some adhesins. [START_REF] Thomas | Bacterial adhesion to target cells enhanced by shear force[END_REF] As already discussed, BC2L-C is reported to be released into extracellular matrix and play role in bacterial adhesion. [START_REF] Sulák | Burkholderia cenocepacia BC2L-C is a super lectin with dual specificity and proinflammatory activity[END_REF][START_REF] Inhülsen | Identification of functions linking quorum sensing with biofilm formation in Burkholderia cenocepacia H111[END_REF][START_REF] Marchetti | Burkholderia cenocepacia lectin A binding to heptoses from the bacterial lipopolysaccharide[END_REF] Further studies based on gene knock-out strategies confirmed that the lack of any one of the lectins (BC2L-A, -B, -C) led to defective biofilm formation. [START_REF] Inhülsen | Identification of functions linking quorum sensing with biofilm formation in Burkholderia cenocepacia H111[END_REF] Thus, BC2L-C could be an interesting target for anti-adhesive therapy. of pathogen to host cell. These studies opened the route for the design of anti-adhesion therapy which aims to disrupt the virulence mechanisms of the bacterium. In particular, the superlectin BC2L-C is an interesting target, which shows similarities with the known virulence factors through C-terminus and also composed of an additional N-terminal domain with unique structural features. Since the N-terminal domain presents a novel lectin domain and already reported with a high resolution crystal structure, it provides an opportunity to explore this domain for structure based drug-design of small-molecule antagonists to block lectinmediated adhesion.

Thesis objective

There are following objectives of the project:

 Virtual screening of fragment library at identified sites.

 Experimental validation of fragment binding.

 Identification of strategies to connect selected fragments to the sugar core to design glycomimetic ligands.

 Molecular dynamics simulation studies of the designed ligands.

 Biophysical evaluation of affinity and identification of binding mode for fragments and designed ligands (in collaboration with Rafael Bermeo at CERMAV-CNRS, Grenoble Alps University and the University of Milan.

 Structure-based elaboration of selected compounds into high affinity ligands.

The initial studies in the project involve computational analysis of the target to identify druggable regions that could host drug like molecules. In addition, the project involves validation of the interaction/binding of small fragments at the identified site using biophysical assays. The identification of these new sites and small fragments will enable the rational design of glycomimetic ligands. The second part of the project is based on the computational design and modelling of the glycomimetic ligands which involves studies of the interactions of the ligands with the target to prioritize the best ligands for synthesis in collaboration with Rafael Bermeo. The additional studies are focused towards the structure-based strategies to improve the binding affinity of glycomimetic ligands.

The PhD4GlycoDrug consortium

Objectives of this thesis have been completed under the framework of the

Research methodology 2.1 Introduction: The domain of structure-based drug design

The past few decades have experienced a fundamental change in preclinical drug discovery with structure-based drug design (SBDD) growing in popularity while the traditional methods of high-throughput screening (HTS) have continued to witness unsatisfactory results. 1 One of the disadvantages of using HTS is that it does not provide mechanistic information about ligand-receptor interactions. Likewise, HTS screening can have influence of physical artefact such as precipitation or any component of the assay. Moreover, the results of HTS screening in form of large amount of data require significant efforts to be analyzed. SBDD approach plays an important role in investigating the interactions between the ligands and receptors and contributes towards in-depth understanding of molecular recognition/interactions. This also helps to optimize lead molecule and bridge the gap between an identified hit to a preclinical drug candidate. 1 The SBDD is based on the hypothesis that a molecule's ability to exert a desired biologic effect depends on its ability to favourably interact with a particular binding site on a protein. Molecules that share those favourable interactions usually exert similar biological effects. The initial projects based on SBDD were under progress in the mid-80s, and by the early 1990s few success stories were published. [2][3] Structural information about the target is a prerequisite for SBDD project.

Recent advancements in the field of genomics and proteomics have led to the discovery of a large number of drug targets. [4][5] Efficient use of advanced biophysical techniques such as Xray crystallography and NMR spectroscopy has also increased the motivation for elucidation of the structures of a large number of protein from pathogenic microorganisms. 6 Consequently, the field of structure-based drug design became an integral part of many industrial drug discovery projects and a major subject of research in academic laboratories. 7 Different methods involving computational and experimental techniques to underpin the structure-based drug design are discussed in this chapter. These methods are particularly useful to design the ligands and to further investigate the ligand-protein interactions. They are divided into two categories given below.

Computational methods to identify druggable sites in proteins and to investigate ligandprotein interactions: The specific protocols and materials used for experiments will be discussed under a relevant section in each chapter.



Prediction of druggable sites using SiteMap

The ligand binding site on a receptor such as a protein is usually known from the structure of the receptor in complex with a ligand. In order to enhance the binding affinity of ligands, understanding of ligands and receptor complementarity is highly important. This can further guide in extending the ligands into adjacent regions near binding site to promote efficient binding. Therefore, initial investigation of the receptor is required to study and identify nearby sites that might be useful for fragment/ligand binding. In some cases, the apo form of the receptor is available which lacks the information about binding sites for proteinligand interactions. In this situation, computational studies can help to suggest likely binding sites, and even to predict the druggability/ligandability of the site. In literature, such approaches have been already reported. [8][9] SiteMap 10 is a tool from Schrödinger 11 that generates important information on the key properties of binding sites in proteins. This tool uses a search and analysis approach and generates important information which can be visualised in Maestro. 11 The calculations involve initial search stage to determine different druggable regions on or near the protein surface which are known as sites. These regions that can be suitable for ligand binding are represented by grid of points, called site points. The site-finding algorithm places a 1-Å grid of possible site points around the entire protein or a ligand. Identification of sites involves different steps. In the first step, grid points are categorized as being "inside" or "outside" the protein. The distance of grid point from the protein atoms is compared to the van der Waals radius of each protein atom. In case the ratio of the squares of the distances is greater than a threshold distance, the grid point is considered outside the protein. Next, the "outside" points are analysed to understand good van der Waals contact with the receptor as well as their enclosure by the receptor to serve as site points. To define the enclosure, all directions are sampled from the grid point to determine the fraction of directions that strike the surface within a defined distance. If the fraction is higher than a defined threshold, the point is sufficiently enclosed and considered as a site point. At a site point, if the magnitude of van der Waals interaction energy is too small, the point is rejected. Only site points that meet the defined criteria are added to the list. In the third step, site points are combined into different site-point groups. A group must have a minimum number of candidate site points within a given distance otherwise they are discarded. Finally, site-point groups (with small distances between them) located in a solvent-exposed region are merged. In the next stage, 3D grid is generated by placing a probe (that simulates water) at each of the grid point to calculate van der Waals and electrostatic interactions. Thereafter, contour maps (site maps) displaying hydrophobic and hydrophilic maps are generated. Further, the hydrophilic maps can be shown as donor and acceptor, and metal-binding regions etc. The results are evaluated based on the calculations which involve assessment of each site for various properties. This usually includes the different features like, van der Waals interaction energy, depth, size, hydrophilicity and hydrophobicity that contribute to the druggability of a protein region.

Based on these characteristics, a single scoring function called SiteScore is assigned to potential druggable regions. SiteMap can assist in the design of high-affinity ligands, by predicting druggable regions that can accommodate the desired groups, for example, larger hydrophobic groups of ligands in the hydrophobic regions. In addition, it can also be useful to select the appropriate site for ligand docking using docking tools like Glide 12 followed by evaluation of docking hits for their complementarity to the receptor. Generally, the regions that are neither very hydrophobic nor very hydrophilic are interesting for drug design purpose because these sites allow further modifications in the physical properties of the ligand. For instance, changing the solubility might have minimal effect on the binding affinity of the ligands. The results of the SiteMap depend on the site as a whole and explicitly show the shape and extent of hydrophilic and hydrophobic regions, which is not possible with a surfacebased display.

Virtual screening: docking studies

Most of the biological processes involve the interaction between two or more biological systems. The molecular characterization of these recognition processes is important to understand various mechanisms including disease research and the development of drugs. [13][14] These important tasks can be accomplished computationally with the help of molecular docking approaches. Molecular docking is the computer-aided prediction of the bound geometry by utilizing the coordinates of the unbound components (which can be proteins, carbohydrates, peptides or small molecules).

The initial coordinates for the receptor (proteins) are usually available from crystallographic and NMR experiments or from homology models. Likewise, initial conformation of ligands or small molecules can be created using computational tools. 15 The docking procedure usually generates several possible complexes known as docking poses.

These multiple poses represent the different local minima. The docking procedures aim to achieve free energy minimum corresponding to the native binding mode of the ligands.

The docking methods usually include two steps: the first step involves conformational sampling of the ligand in the active site of the protein while in the second step, conformations are ranked via a scoring function. The sampling step employs a search algorithm that generates several possible binding modes of the ligands by varying their conformations (flexible docking) and orientations in the binding site of the receptor. The scoring procedure is based on scoring functions which approximate the binding affinity between two molecules and allow ranking the binding poses. These two steps (sampling and scoring) are iterated to converge to a solution of minimum energy. In addition to prediction of ligand-binding poses, docking can be used for different studies based on drug-receptor interactions such as lead optimization, virtual screening and library design. In the field of computer-aided drug design, docking software are used for virtual screening campaigns as well as in the lead optimization of the compounds. 16 The description of molecular interactions is useful to identify key residues involved in the ligand binding. [17][18] Likewise, analysis of the mutations in the receptor is helpful to investigate the bases of drug-resistance. 15 Docking techniques also assist in the understanding of the molecular mechanisms of selectivity, by exploring the interactions of the same molecule with different receptor targets. 19 Various software packages are devoted to perform molecular docking with different scoring functions and sampling methods. 12,[20][21][22][23][24][25] As discussed above, docking is based on two steps involving a sampling procedure to generate a wide variety of possible binding modes and a scoring step that aims to rank the poses. 16,26

Sampling

In general, the binding process involves changes in the respective orientation of the ligand and receptor as well as the conformational degrees of freedom of ligand and protein can generate several conformations. Consequently, a huge number of binding modes are possible between protein and ligand. Unfortunately, it would be highly expensive from the computational point of view to exhaustively sample all the possible binding modes. To tackle this issue, three strategies with a different degree of exhaustiveness are usually adopted in the docking algorithms. The first approach is based on rigid ligand and rigid receptor, in which search space is very limited by exploring only the six rotational and translational degrees of freedom while both the receptor and the ligand are treated as rigid bodies. In this approach, pre-computed set of ligand conformations can be used for docking calculations. Second method is based on rigid receptor and flexible ligand approach in which the conformational degrees of freedom of the ligand are sampled. The third approach is based on fully or partially flexible protein and flexible ligand in which all the degrees of freedom of the ligand are investigated while all or only few relevant conformational degrees of freedom of the protein are sampled. 27 In order to balance the speed and the accuracy of the docking calculations, currently most popular docking methods treat the ligand as flexible and the receptor as a rigid-body.

Various sampling algorithms have been developed and being used in different molecular docking software. 26 Systematic and stochastic sampling are two methods that are generally used to perform ligand sampling. In the systematic sampling, incremental variations in each structural parameter explores the free-energy landscape to achieve convergence to minimum energy conformation. 28 However, this method has higher computational cost while handling highly flexible ligands due to an enormous increase in the combination of structural parameters. In opposite, the stochastic methodologies involve a random change in the structural parameters at each step that generates a wide variety of possible solutions. These methods either accept or reject the proposed solutions according as per the requirement, thus limit the computational cost. Monte Carlo and genetic algorithms are examples of stochastic methods. However, this methodology may not guarantee the convergence to the global minimum, thus multiple independent runs are required to achieve the optimal solution. 27 In addition to the ligand conformations, receptor flexibility also constitutes one of the major challenges in the docking methodologies. It also plays an essential role in the process of ligand binding as the receptor undergoes structural rearrangements due to induced effect. These changes in the receptor can have small effects limited to binding site region or larger effects that may result in conformational rearrangements affecting the whole receptor. 28 The flexibility of receptor is also crucial when the free receptor could exist in multiple conformational states. 29 Therefore, various methods can be adopted to deal with the receptor flexibility. These methods differ in the degree of exhaustiveness and their accuracy. 27,30 In Soft-docking approach, the repulsive contribution of the van der Waals potential is softened to allow small atoms overlaps, thus ligands get accommodated more easily in the binding site. This method is applicable when small local receptor motions occur.

Another method involves side-chain flexibility by sampling the side chain conformations that can be employed only for local motions of the receptor. Likewise, methods based on postprocessing involve molecular relaxation using Monte Carlo or MD simulations. Additional methods involve ensemble docking, in which multiple conformations of the receptor are generated to dock the ligand. These putative conformations of the receptor can come from experimental methods like NMR and X-ray crystallography or from computational models (e.g. molecular modelling and MD). However, this method has limitations when receptor undergoes large structural rearrangements. Likewise, approaches based on collective degrees of freedom consider the full flexibility of the receptor by reducing its high-dimensional conformational landscape that capture only the dominant motion modes. This can be achieved by using methods such as normal mode analysis or principal component analysis.

Scoring

The purpose of the scoring function is to rank the binding poses that are generated during the sampling of molecules. The scoring functions estimate the binding affinity between the protein and ligand by adopting various assumptions and simplifications and perform the calculations in a reasonable time to save on computation cost. 27 They can guide the search methods towards relevant ligand conformations and distinguish the experimentally observed binding modes from all the other predicted poses. The scoring functions are mathematical equations that consist of different terms representing physical properties of the two interacting molecules. For instance, simple scoring functions may consider different interaction terms such as hydrogen bonds, hydrophobic contacts and salt bridges, etc. 16 Conversely, more complicated and time-consuming functions involve additional terms, for instance, contribution of entropic effects and desolvation. 28,31 Scoring functions can be classified into three types: force-field-based, empirical and knowledge-based. 32 Force field-based functions are derived from a classical force field where the binding energy is computed as the sum of bonded (bond stretching, angle bending and torsional energy) and non-bonded terms (electrostatic and van der Waals interactions). For example, D-score involves van der Waals and electrostatic energy terms to describe interactions between ligand and receptor (equation 2.1 and 2.2). 33 The van der Waals energy term is given by a Lennard-Jones potential function.

𝐷 -𝑆𝑐𝑜𝑟𝑒 = 𝐸 𝑣𝑑𝑤 + 𝐸 𝑒𝑙𝑒𝑐𝑡𝑟𝑜𝑠𝑡𝑎𝑡𝑖𝑐 (2.1) ∑ ∑ [( 𝐴 𝑖𝑗 𝑑 𝑖𝑗 12 - 𝐵 𝑖𝑗 𝑑 𝑖𝑗 6 ) + 332.0 𝑞 𝑖 𝑞 𝑗 ∈(𝑑 𝑖𝑗 )𝑑 𝑖𝑗 ] 𝑙𝑖𝑔 𝑝𝑟𝑜𝑡 (2.2)
where Eelectrostatic, represents Coulomb energy term and EvdW is van der Waals energy term, i and j are two atoms, Aij and Bij are van der Waals parameters for given atom types, dij is the interatomic distance, qi and qj are atomic partial charges, and ε(dij) is a distance-dependent dielectric function. One of the limitations of these scoring functions is that they often overestimate the interactions between charged atoms. 27 Further extension of these scoring functions includes desolvation and entropic effects which are described by desolvation energy and conformational entropy terms, respectively. 33 Moreover, the recent developments involve quantum mechanics (QM) to address the challenges of covalent interactions, charge transfer and polarization in docking. However, the QM-based scoring functions have greater computational cost. 27,33 A second class of scoring-functions involves empirical methods. [34][35][36][37][38] In this approach, the scoring-function is based on sum of the terms which reflect the general features of the complex such as hydrogen bond, ionic interaction, hydrophobic effect and binding entropy etc. 27 Each component of the scoring function is simplified and weighted with proper coefficients, and then summed up to give a final score to the complexes. The coefficients for these scoring functions are usually obtained from regression analysis or machine learning approaches. 15 Machine-learning-based scoring functions employ a variety of algorithms, such as neural network, deep-learning, support vector machine that usually rely on the training dataset. Thus, accuracy of these scoring functions is usually related to the quality of the training set. 15 Knowledge-based scoring functions rely on statistical analysis of experimentally determined receptor-ligand structures by obtaining interatomic contact frequencies and/or distances between the ligand and protein. These scoring functions are based on the assumption that the frequent contacts appearing in several complexes, correspond to favourable interactions. These frequency distributions are further used to construct the pairwise atom-type potentials. The calculation of final score is based on the favourable contacts and unfavourable repulsive interactions between each atom in the ligand and protein. These scoring-functions are fast and their interaction potential also includes the features that cannot be modelled explicitly. 16 However, the accuracy of such methods rely on the number and diversity of the training set employed to create the potential.

All the scoring functions have their limitations, therefore in order to improve the performance of these scoring-functions, consensus scoring approach is usually recommended. This strategy involves the use of multiple docking software and scoring functions. Finally, the scores obtained are combined through a consensus scheme. This approach usually provides notable improvement in terms of accuracy. 39 At present, most scoring-functions are able to predict native or near-native binding modes, however accurate estimation of the free energy of binding and ranking of different compounds toward a target receptor is still a challenging task. Consequently, the available scoring functions show a weak correlation with experimental binding affinities and target dependent performance. 31 These challenges in reproducing experimental results are because of several reasons, for example, poor quality of the input ligand and receptor structures, improper treatment of long-range interactions, an inappropriate handling of solvent and entropic effects. 40 To avoid the computational cost, contribution of conformational entropy in the determination of binding free energy is usually neglected or oversimplified in most of the docking software. 27 However, methodologies to deal with solvent effects have been developed. In this regards, structural waters are considered for the solvent effect, which play important role in mediating receptorligand interactions. 15 The consideration of structural waters in docking calculations significantly improves the accuracy of result. 15,28 Some methods that explicitly account for water molecules during the docking process have been proposed. [41][42][43][44] However, such methods are computationally expensive due to the large number of degrees of freedom associated to the solvent molecules. To reduce the computational time, some methods treat the water molecules implicitly by representing them as a continuum dielectric medium.

Molecular Mechanics generalized-Born/surface area (MM-GB/SA) and Molecular Mechanics Poisson-Boltzmann surface area (MM-PB/SA) are the most widely used methods based on this strategy. [45][46][47] In order to improve the accuracy of ranking and binding energy predictions, these approaches are generally employed as post-processing step to rescore the docking poses. MM-GB/SA and MM-PB/SA have been successfully employed to estimate the free energy of binding in different studies on protein-ligand interactions. [48][49]

Glide software

Glide (Grid-based Ligand Docking with Energetics) 12 is a ligand docking program for predicting protein-ligand binding poses and ranking them after virtual screening. This program uses systematic conformational search and employs a scoring-function that relies on the empirical and force-field-based terms. Glide uses three different docking protocols with different accuracies. The Standard-Precision (SP) approach employs a soft scoring function which can identify a wide pool of possible binders and minimize false negatives. Similar to SP, HTVS (high-throughput virtual screening) approach uses the same scoring function but it decreases the number of intermediate conformations by reducing the thoroughness of sampling and final torsional refinement. However, the algorithm itself is essentially the same.

Another docking approach known as Extra-Precision (XP) uses more extensive sampling than SP and is characterized as harder scoring-function with greater requirements for ligandreceptor shape complementarity. This attempts to minimize the false positives by including additional penalties. 12,21,50 This weeds out false positives that are passed through SP approach.

Glide uses a series of filters to generate the best binding pose of the ligand in the binding-site region of the receptor. At a first stage, several conformations of the ligands are generated by exploring its torsional angle space. The conformations with lower torsional energy are selected using a model energy function that uses force-field (OPLS) based terms. 12 In the second step, these conformers are screened on the binding site of the receptor to identify their possible positions and orientations. The placement of the conformations is then evaluated by assigning energetic-like properties of the protein on a grid which assigns a precomputed score (derived from a discretized version of the empirical ChemScore function) 51 to boxes of 1 Å 3 dimensions. The penalties are also assigned to the scores in case of steric clashes. This score is able to recognize favourable hydrophobic interactions, hydrogen-bonds and metal-ligation interactions.

In the next stage, the best poses of the ligands are minimized in the field of the receptor, using a molecular mechanics scoring function and a multi-grid strategy. In this approach, the side-dimension of the boxes, which store the Coulomb/van der Waals fields of the receptor, are gradually decreased in the area where the two molecules are in contact that improves the accuracy of the calculations. The best three to six lowest-energy poses are minimized with a Monte Carlo approach. This last phase plays an important role in the accurate prediction of docking poses. Finally, GlideScore is used to re-score the minimized poses. GlideScore is based on ChemScore 51 and includes terms such as steric-clash term, rewards, penalties for electrostatic mismatches, amide twist penalties, hydrophobic enclosure terms etc. The components of GlideScore (GScore) can be described as:

𝐺𝑆𝑐𝑜𝑟𝑒 = 0.05 * 𝑣𝑑𝑊 + 0.15 * 𝐶𝑜𝑢𝑙 + 𝐿𝑖𝑝𝑜 + 𝐻𝑏𝑜𝑛𝑑 + 𝑀𝑒𝑡𝑎𝑙 + 𝑅𝑒𝑤𝑎𝑟𝑑𝑠 + 𝑅𝑜𝑡𝐵 + 𝑆𝑖𝑡𝑒 (2.3)
where vdW is Van der Waals energy, Coul is Coulomb energy, Lipo is lipophilic term, HBond is hydrogen-bonding term, RotB is the penalty awarded for freezing rotatable bonds, Metal is metal-binding term, Site is the term for polar interactions in the active site, Rewards represents the rewards and penalties for hydrophobic enclosure, buried polar groups, amide twists. Hydrophobic enclosure is a reward for the displacement of water molecules by a ligand from the binding site region with many proximal lipophilic protein atoms. Formation of protein-ligand hydrogen bonds within regions of hydrophobic enclosure are beneficial to binding. Likewise, electrostatic mismatches are penalties for buried polar terms and amide twists are energy penalties for the non-planar conformation of amides. This penalty is applied to twisted and cis conformations. Therefore, it is possible to obtain poses with twisted amides if there are compensating interactions.

Molecular dynamics (MD) simulations

The molecular dynamics method was introduced in the late 1950's. 52 The next major advancement took place in 1964 when first simulation for liquid argon was performed using a realistic potential. 53 Subsequently, the first realistic system was simulated for liquid water in 1974. 54 Later on 1977, protein simulation of the bovine pancreatic trypsin inhibitor was reported. 55 The advancements in the MD simulation methods and computation have made it possible to expand these methods for biological complexes addressing the thermodynamics of ligand binding and the folding of small proteins. Increasing computational power has also advanced the molecular simulations studies allowing simulations up to millisecond timescales. 56 Similarly, emerging new protocols for MD simulations studies have also increased scientific interest in correlation of molecular simulations results with experimental data. 57 These new molecular simulation protocols can be used for the better understanding of conformational dynamics of ligand-receptor complexes.

Statistical mechanics

The aim of a molecular dynamics simulation is to infer the macroscopic properties of a system from studies of its microscopic behaviour. For example, calculation of changes in the binding free energy of a particular drug candidate or the study of the energetics and mechanisms of conformational change. 58 Statistical mechanics make a connection between microscopic and macroscopic properties by providing the mathematical expressions that relate macroscopic properties to the distribution and motion of the atoms and molecules of the N body system. 58 Therefore statistical mechanics is the branch of physical sciences which deals with the study of macroscopic systems from molecular point of view.

Thermodynamic state, microscopic state and ensembles

Thermodynamic state of a system can be defined by a set of parameters, the temperature, T, the pressure, P, and the number of particles, N. 58 The microscopic state for each atom in a system of N particles is can be defined by the atomic positions, r N , and momenta p N ; these can also be considered as coordinates in a multidimensional space also known as phase space. For a system of N particles, this space has 6N dimensions. 58 A single point in phase space, denoted by Γ, describes the state of the system Γ = (p N , r N ).

An ensemble is a collection of all possible systems which have different microscopic states but an identical macroscopic or thermodynamic state. 58 Therefore, it is a collection of points in phase space satisfying the conditions of a particular thermodynamic state. 58 Microcanonical ensemble (NVE) is characterized by a fixed number of atoms, N, a constant volume, V, and a constant energy, E. This ensemble represents an isolated system. 58 Another important ensemble known as canonical ensemble (NVT) is a collection of all microstates whose thermodynamic state is characterized by a constant number of atoms, N, a constant volume, V, and a constant temperature, T. In this ensemble temperature is regulated by thermostats. 58 Isobaric-Isothermal Ensemble (NPT) ensemble consists of a fixed number of atoms, N, a constant, P, and a constant temperature, T where temperature and pressure are regulated by thermostats and barostats respectively. 59-60

Calculation of averages from molecular dynamics simulations

In statistical mechanics, macroscopic quantities are defined as averages over ensembles of microstates. 58 The ensemble average of a property A is given by equation 2.4 :

〈𝐴〉 𝑒𝑛𝑠𝑒𝑚𝑏𝑙𝑒 = ∬ 𝑑𝒑 𝑁 𝑑𝒓 𝑁 𝐴( 𝒑 𝑁 , 𝒓 𝑁 )𝜌(𝒑 𝑁 , 𝒓 𝑁 ) (2.4)
where A(p N ,r N ) is the observable of interest and it is expressed as a function of the momenta, p, and the positions, r, of the system, ρ is the probability density. 58 The probability density of finding the ensemble with momenta, p, and the positions, r is calculated as:

𝜌(𝒑 𝑁 , 𝒓 𝑁 )= 1 𝑄 exp[-𝐻(𝒑 𝑁 , 𝒓 𝑁 )/𝑘 𝐵 𝑇] (2.5)
where H is the Hamiltonian, T is the temperature, kB is Boltzmann's constant and Q is the partition function given by equation 2.6. 58

𝑄 = ∬ 𝑑𝒑 𝑁 𝑑𝒓 𝑁 𝑒𝑥𝑝[-𝐻(𝒑 𝑁 , 𝒓 𝑁 )/𝑘 𝐵 𝑇] (2.6)
In a molecular dynamics simulation, the points in the ensemble are calculated sequentially in time; therefore, to calculate an ensemble average, the molecular dynamics simulations must pass through all possible states corresponding to the particular thermodynamic constraints.

Alternatively, it is possible to determine a time average of A, which is expressed in equation 2.7:

〈𝐴〉 𝑡𝑖𝑚𝑒 = 𝑙𝑖𝑚 𝑛→∞ 1 𝜏 ∫ 𝐴(𝒑 𝑁 𝜏 𝑡=0 (𝑡), 𝒓 𝑁 (𝑡)) 𝑑𝑡 ≈ 1 𝑀 ∑ 𝐴(𝒑 𝑁 (𝑡), 𝒓 𝑁 (𝑡) 𝑀 𝑡=1 ) (2.7)
where t is the simulation time, M is the number of time steps in the simulation and A(p N (t),r N (t)) is the instantaneous value of A. n also represents the number of observations (time steps) that goes to infinity. 58 The experimental observables are assumed to be ensemble averages.

This leads a fundamental principle of statistical mechanics, called ergodic hypothesis, which states that an ensemble average of an observable is equivalent to the time average of an observable. 58 〈𝐴〉 𝑒𝑛𝑠𝑒𝑚𝑏𝑙𝑒 = 〈𝐴〉 𝑡𝑖𝑚𝑒

The general idea is that if system is allowed to evolve in time indefinitely, it will eventually pass through all possible states. Therefore, it is advisable to generate enough representative conformations during MD simulations.

Classical mechanics

The molecular dynamics simulation method is based on Newton's second law which is also known as the equation of motion (equation 2.8). 58,61 𝑭 𝒊 = 𝑚 𝑖 𝒂 𝒊 (2.8)

Where F is the force acting on a particle i, mi is its mass and ai is acceleration of the particle.

It is possible to determine the acceleration of each atom in the system from the knowledge of the force on each atom. The equations of motion are integrated to yield a trajectory that describes the positions, velocities and accelerations of the particles. 58,61 The trajectory generated is averaged to determine the properties. Once the positions and velocity of atoms are known, the state of the system can be computed at any time (future or past). 58,61 The force can be expressed as negative of the gradient of the potential energy (equation 2.7). 58 𝑭 𝒊 = -𝛻 𝑖 𝑉 (2.9)

The above equations (2.6 and 2.7) can be combined to rewrite as follows:

- 𝑑𝑉 𝑑 𝒓 𝒊 = 𝑚 𝑖 𝑑 2 𝒓 𝒊 𝑑𝑡 2 (2.10)
where V is the potential energy of the system. Newton's equation of motion can relate the derivative of the potential energy to the changes in position as a function of time. In MD simulations the initial distribution of velocities is usually computed from a random Maxwell-Boltzmann distribution with the magnitudes conforming to the required temperature and corrected so that overall momentum (P) is zero. 58

𝑷 = ∑ 𝑚 𝑖 𝒗 𝒊 𝑁 𝑖=1 = 0 (2.11)
The velocities, vi are often chosen randomly from a Maxwell-Boltzmann distribution at a given temperature (equation 2.12). 58

𝑝(𝒗 𝑖𝑥 ) = √ 𝑚 𝑖 2𝜋𝑘 𝐵 𝑇 𝑒𝑥𝑝 [- 1 2 𝑚 𝑖 𝒗 2 𝑖𝑥 2𝑘 𝐵 𝑇 ] (2.12)
Where p is the probability of atom i which has mass mi, a velocity vi in the x direction at a temperature T.

Integration algorithms

The MD trajectories describe the time evolution of the system in phase space which is defined by both position and velocity vectors. Therefore, integrators are used for the propagation of the positions and velocities with a finite time interval. Various numerical algorithms have been developed for integrating the equations of motion. 58 There is no analytical solution to the equations of motion because of the complexity of the function.

Therefore, numerical methods have been developed for integrating the equations of motion.

The integration algorithms work on the assumption that positions, velocities and acceleration can be approximated by a Taylor series. 58 where r is the position, v is the velocity, a is the acceleration (second derivative with respect to time). The disadvantages of this method are that the algorithm is not self-starting because estimate is required for the initial position and the results are also of moderate precision.

Therefore, an alternative algorithm called velocity Verlet algorithm that uses the velocity to yield positions, velocities, and acceleration is more frequently used (equation 2.16 and 2.17 thermostat is introduced to modulate the temperature of a system in some fashion. Various methods are available to add and remove energy from the boundaries of an MD system to approximate the ensemble. The goal of introducing thermostats is to ensure the desired average temperature of a system.

The temperature of the system is related to the time average of kinetic energy (K) which can be represented by equation 2.18:

〈𝐾〉 𝑁𝑉𝑇 = 3 2 𝑁𝑘 𝐵 𝑇 (2.18)
the most intuitive strategy to keep the temperature constant would be to multiply at every step the velocity of the particles by a scaling factor (λ) given in equation 2.19. 60

𝜆 = √ 𝑇 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑇 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 (2.19)
Where Tcurrent is the current temperature and Trequired is the desired temperature. This however suppresses fluctuations in instantaneous temperature and does not lead to simulation of a correct canonical ensemble. An alternative way to maintain the temperature is to couple the system to an external heat bath that is fixed at desired temperature. 62 The bath supplies or removes the heat from the system as required. The velocities are scaled at each step so that the rate of change of temperature is proportional to the difference in temperature between the bath and the system. Scaling factor for the velocities is given by equation 2.20:

𝜆 2 = 1 + 𝛿𝑡 𝜏 ( 𝑇 𝑏𝑎𝑡ℎ 𝑇 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 -1) (2.20)
where τ is coupling parameter which determines how tightly is the system and bath coupled together, Tbath is bath temperature and 𝛿t is the time step. 62 This simple method, known as Berendsen thermostat does not generate a rigorous canonical ensemble. 62 Therefore alternative stochastic collision and extended system methods are usually implemented for this purpose. In the stochastic collision method, a particle is randomly chosen at intervals and its velocity is reassigned by random selection from Maxwell-Boltzmann distribution. 63 The method represents the system in contact with heat bath emitting thermal particles which collide with atoms in the system. The system is simulated at constant energy while each collision takes place which overall represents a number of microcanonical simulations at slightly different energies. In this approach, momentum transfer is destroyed because of the random velocities therefore it is unadvisable to use this method to compute diffusion coefficients. Similarly, a trajectory generated with this method is not smooth which might be another disadvantage.

Other methods for performing constant temperature molecular dynamics are called extended system methods. 59,64 In this method a thermal reservoir is introduced in the system to provide additional degree of freedom (s). The reservoir has potential energy (P) given by equation 2.21:

𝑃 = (𝑓 + 1)𝑘 𝐵 𝑇𝑙𝑛𝑠 (2.21)
where f is the number of degree of freedom in physical system and T is the desired temperature. 64 The kinetic energy (K) of reservoir can be given as equation 2.22:

𝐾 = ( 𝑄 2 ) ( 𝑑𝑠 𝑑𝑡 ) 2 (2.22)
where Q is the fictitious mass of the extra degree of freedom. The magnitude of Q also determines the coupling between reservoir and real system. 64

Constant pressure dynamics

A macroscopic system can maintain constant pressure by changing its volume. In isothermal-isobaric ensemble (NPT), the constant pressure is maintained by changing the volume of simulation cell. The amount of volume fluctuations depends on the isothermal compressibility. The isothermal compressibility (k) is related to mean square volume (V) displacement given by equation 2.23:

𝑘 = 1 𝑘 𝐵 𝑇 〈𝑉 2 〉-〈𝑉〉 2 〈𝑉 2 〉 (2.23)
most of the methods used for the control of pressure are analogous to those used for the constant temperature. The rate of change of pressure is given by equation 2.24:

𝑑𝑃(𝑡) 𝑑𝑡 = 1 𝜏 𝑝 (𝑃 𝑏𝑎𝑡ℎ -𝑃(𝑡)) (2.24)
where τp is the coupling constant, Pbath is the pressure of the bath and P(t) is the actual pressure at time t. The volume of simulation box is scaled by factor (λ) (equation 2.25) which scales the atomic coordinates by a factor λ 1/3 . 58

𝜆 = 1 -𝑘 𝛿𝑡 𝜏 𝑝 (𝑃 -𝑃 𝑏𝑎𝑡ℎ ) (2.25)
where k is the isothermal compressibility and P is the required pressure. Thus the new position (r) would be given by equation 2.26:

𝒓 = 𝜆 1 3 𝒓 𝒊 (2.26)
where ri is initial position. In extended pressure-coupling system methods, an extra degree of freedom is added which corresponds to the volume of box. 59,64 The kinetic energy associated with this degree of freedom behaves like a piston acting on the system. The kinetic energy (kp) of piston can be given by equation 2.27:

𝑘 𝑝 = 1 2 𝑄 ( 𝑑𝑉 𝑑𝑡 ) 2 (2.27)
where Q is the mass of the piston and V is the volume of the system. 59,64 The potential energy of the piston is given as PV, where P is the desired pressure and V is volume of the system.

Piston with small mass gives rapid oscillations, while a large mass shows opposite effect. 58

Molecular mechanics

Energy calculation is based on either ab initio, semi-empirical quantum chemistry calculations or empirical methods. Although the ab initio description using quantum mechanics is more accurate, the limited computational power restricts its use only to very small systems containing up to a few hundred atoms. Molecular mechanics provides a faster method to determine the energy of the system which depends solely on the derived set of parameters (force-field) for the potential energy estimation. 58 The potential energy calculated using molecular mechanics usually consists of bonding terms (bond lengths, angles and dihedral angles) and non-bonding terms (van der Waals and electrostatic interactions)

given by equation 2.28:

𝑈 = ∑ 1 2 𝑏𝑜𝑛𝑑𝑠 𝐾 𝑟 (𝑟 -𝑟 𝑒𝑞 ) 2 + ∑ 1 2 𝑎𝑛𝑔𝑙𝑒𝑠 𝐾 𝜃 (𝜃 -𝜃 𝑒𝑞 ) 2 + ∑ 𝑉 𝑛 2 𝑑𝑖ℎ𝑒𝑑𝑟𝑎𝑙𝑠 [1 + 𝑐𝑜𝑠(𝑛Ø - ϒ 𝑒𝑞 )] + ∑ 4𝜖 𝑖𝑗 [( 𝐴 𝑖𝑗 𝑟 𝑖𝑗 ) 12 -( 𝐵 𝑖𝑗 𝑟 𝑖𝑗 ) 6 ] 𝑖<𝑗 + ∑ 1 4𝜋𝜀 𝑖<𝑗 𝑞 𝑖 𝑞 𝑗 𝑟 𝑖𝑗 (2.28)
where U is the total potential energy and Kr, Kθ, and Vn are the force constants for bondstretching, angle bending and dihedral angle deformations respectively. 58 Similarly, r, θ, and φ represents the bond-length, valence and dihedral angle values respectively; req, θeq, and γeq are the equilibrium values of bond-length, angles and phase angle respectively; ϵij is the depth of the potential well, Aij and Bij are the finite distances at which the inter-particle potential is zero; ε is dielectric constant; qi and qj are charges of atoms i and j, and rij is the distance between them. Molecular mechanics force fields can be used to calculate the potential energy of large biological systems with millions of atoms. The total energy of the system is calculated as the sum of overall interaction terms. 58 In larger systems the calculation of energy is a time consuming process, therefore, interactions between atoms separated by more than a specific cut-off distance are ignored. 62 The cut-off of distance for non-bonded terms can result discontinuities in the potential energies. Therefore, to resolve this problem, the non-bonded terms may be multiplied by a switching function. 58,61 There are other methods like particle mesh Ewald (PME) to deal with long range electrostatics where cut-off potential is not set to zero. This method is particularly important for the MD simulation of nucleic acids, which have polyanionic backbones. 61 Most of the MD simulations are currently performed in explicit solvent conditions that involve the use of different water models. The examples of these models include TIP3P, TIP4P, TIP5P and SPC. [65][66][67] All the models make use of a rigid geometry however there is a difference in the number of interaction points used to represent the water molecules. For instance, TIP3P and SPC use three interaction points that represent the three atoms of the molecule. Likewise, TIP4P and TIP5P have four and five interaction points respectively, by involving one or two dummy atoms with negative charge on them. The dummy atoms represent the lone pairs of oxygen. The additional interaction sites usually improve the electrostatic distribution around the molecule but at the same time also enhance the computational cost. Therefore, three-site models are the most widely used in the MD simulation studies. In addition to these models, implicit solvation model can also be used to reduce the computational cost. In this model, the average action of water molecules is represented by a potential that gives equivalent properties. Hence, explicit solvent coordinates are not used in the model. Although this model is as not accurate as the explicit solvent model but it can be useful for the MD simulation studies of huge systems.

Periodic boundary conditions

Periodic boundary conditions are used for approximating a large system. About half of the total atoms arranged in a box are on the outer faces in the simulation box have less neighbouring atoms than atoms inside. This will have a large effect on the measured properties. Therefore, surrounding the simulation box with replicas of each cell may help to overcome this problem. 62 When the potential range is not too long, the principle of minimum image convention can be adopted which states that each atom interacts with the nearest atom or image in the periodic array. 61 When an atom leaves the basic simulation box during simulation, incoming image can be considered for calculations. To calculate the particle interactions within the cut off range real and image neighbours are included.

Analysis of water thermodynamics using Grid Inhomogeneous Solvation Theory (GIST)

Ordered water molecules on protein surfaces are frequently observed in the X-ray crystal structures. When a macromolecule binds another macromolecule or a drug-like small molecule, the displacement of surface water to bulk can have significant contribution to the overall free energy of binding. [68][69][70][71][72][73] Therefore, the study of water at molecular surfaces is crucial in molecular recognition and ligand binding. The water molecules can act as H-bond acceptors or donors and also establish favourable van der Waals contacts with polar and apolar parts of the surface. These strong interactions make the displacement of these water molecules more challenging by the drug molecules. Conversely, the hydrophobic surfaces have unfavourable effects, due to combination of entropic and enthalpy costs, and thus allow the displacement of water. The water-protein interactions in a binding pocket display a pattern which is strongly replicated by the binding of small molecules. 74 Hence, these hydration sites in the binding pocket can provide valuable information about the key features that a molecule could mimic to favour its binding to the target. Grid Inhomogeneous Solvation Theory (GIST) is a computational method to calculate thermodynamic values of the water molecules occupying the binding pocket. 75 This approach discretizes the integrals of Inhomogeneous Solvation Theory (IST) onto a three-dimensional grid that includes the binding site region involving high-and low-occupancy sites (Figure 2.1). Thermodynamic values of solvent are calculated within a defined region that involves boxes, or voxels, of a three-dimensional grid. The energy of each voxel represents the full interaction between the water molecules located in that voxels, with the solute (protein) and other water molecules in the region. GIST produces quantitative thermodynamic data for each grid box, or voxel. In the calculation of energy of voxels, the interaction between a water molecule and solute is assigned in its totality to the grid box while the water-water energies are divided by two, to avoid double counting. This approach provides a detailed analysis of water thermodynamic and structural quantities in a defined region of interest such as water occupancy within each voxel, water first order entropic penalty for each voxel etc. This information can be useful to understand the water thermodynamics and to know whether the water at a given site is thermodynamically favourable or not when compared to the bulk distribution. The tool helps to estimate a local, density-weighted free energy of solvation, ΔG(rj) for voxel (j), ∆𝐺(𝒓 𝑗 ) = ∆𝐸 𝑡𝑜𝑡𝑎𝑙 (𝒓 𝑗 ) -𝑇∆𝑆 𝑠𝑤 𝑡𝑜𝑡𝑎𝑙 (𝒓 𝑗 ) (2.29)

Thus, by assembling the corresponding energy and entropy terms, change in solvation free energy after displacing the water from a site into bulk, and the normalized (per water) solvation free energy can be computed.

Recombinant protein (BC2L-C-nt) expression and purification

The biophysical evaluation of fragments or ligands requires sufficient production of the protein, here lectin of interest. To achieve this, the protein was expressed in recombinant form in heterologous system such as the bacteria Escherichia coli. The plasmidic construct was performed by Rafael Bermeo. 76 Primers on both 5' and 3' ends were designed using short single-strand DNA sequences that complement and delineate the genetic material to be amplified. Then amplification was performed using polymerase chain reaction (PCR) which can produce billions of copies of the gene. Subsequently, ligation of the genetic material is achieved by mixing with ligase that allows insertion of genetic material into an expression vector i.e pCold TF-TEV 77 in this study (Figure 2.2). The recombinant plasmid is then transformed into E. coli competent cells by applying heat shock at 42 °C and the colonies bearing these plasmids are cultured for protein expression. Protein expression was performed in baffled culture flasks and induced with isopropyl β-d-1-thiogalactopyranoside (IPTG). The overnight incubation was required for the expression of the target protein with higher yield.

The cspA (cold-shock protein A) promoter, upregulates the expression of the target protein on induction at low temperature while the expression of other proteins is hampered.

Likewise, trigger factor (TF) chaperone is expressed to enhance the solubility and improve the yield. The antibiotic ampicillin is added to ensure selective expression of the cells with acquired resistance due to the plasmid (Amp R gene) and thus get the desired protein.

The protein expressed by the bacterial calls are released from the cytoplasm after cell disruption using high pressure. After centrifugation of the disrupted cells, the target protein released in the supernatant which is filtered using 0.45 μM filter to discard the cell particles before the purification. To facilitate purification, the construct contains tobacco etch virus (TEV) cleavage site and histidine tag (HisTag) at the N-terminal. The His-tag allows separation of the fusion protein from the other bacterial proteins using immobilized metal affinity chromatography (IMAC). The desired protein can be eluted with the help of a gradient of imidazole which disrupts the interactions of the proteins with the metal immobilized (nickel) on the column matrix. After elution, TEV protease is used to act on the TEV cleavage site to separate the HisTag from the target protein. 78 Further purification is performed by repeating IMAC to separate cleaved tag and the desired protein. Final step involves purification using size exclusion chromatography (SEC). This step separates the desired protein from the contaminants by size. In this method, smaller particles are trapped by the porous matrix in the column of SEC apparatus while the larger particles cannot enter the pores and passes quickly. Thus, smaller particles have longer retention time. This procedure is the first quality control of the recombinant lectin as is eluted as trimer in the SEC. In addition, electrophoresis performed using sodium dodecyl sulfate-polyacrylamide gel electrophoresis helps to evaluate the purity of the lectin.

Thermal shift assay (TSA)

The biophysical characterization of protein-ligand interactions plays an important role in structural biology. Several methods used to screen compound libraries are available ranging from highly specialized techniques such as NMR 79 and mass spectrometry 80 to simpler methods like thermal shift assays (TSA). 81 TSA measures a shift in thermal denaturation temperature and hence stability of a protein under different conditions such as change in drug concentration, sequence mutation, buffer pH etc. This change in melting temperature is usually measured by fluorescence methods. These fluorescence-based techniques are also known as thermofluor assays or differential scanning fluorimetry (DSF). [81][82][83] TSA methods are easy to perform, hence most laboratories can routinely use them in high-throughput screening to identify new ligands. 84 The identified hits can be further characterized for bimolecular interactions, using more sophisticated biophysical techniques. In addition, these methods can be used to determine the storing solution where the protein is the most stable and monodisperse to favour crystallization. 83,[85][86][87][88] In thermofluor assay, a compound such as SYPRO Orange dye, presents a low fluorescence signal in aqueous solution but emits high fluorescence 82 when binding non-specifically to hydrophobic surfaces exposed in denaturated proteins. When the protein unfolds upon heating, the hydrophobic core is exposed to the dye which results in stronger fluorescence signals. As the temperature increases, the protein becomes completely denatured. The stability curve and the temperature (Tm) at which half of the protein is unfolded is determined (Figure 2.3). The curves can be measured for protein in the absence or presence of ligand to calculate the difference in melting temperature (ΔTm). The significant difference in melting temperature suggests interaction (binding) of ligand and protein. Usually, the assays work with most protein samples. However sometimes a clear signal is not observed due to several factor such as high background noise because of fluorophore binding to native state of protein, insufficient hydrophobic core etc. In addition to measuring ligand binding, TSA is commonly used for determining buffer conditions, or additive nature, that would stabilize a protein and enhance the probability of crystallization. 89

Microscale thermophoresis (MST)

Thermophoresis can be and described as the directed motion of molecules through a temperature gradient. 90 Microscale thermophoresis (MST) is a biophysical method for the detection of directed movement of fluorescent molecules through microscopic temperature gradients in a very small volume (µl) of solution, that allows accurate analysis of binding events. [91][92] The basic principle involves that the spatial temperature difference dT results into a change in molecule concentration in the region of elevated temperature which can be quantified by the Soret coefficient ST. This can be represented as

𝑐 𝑇𝑖 = 𝑐 𝑖 exp (-𝑆 𝑇𝑖 𝑑𝑇) (2.33)
where ci is the initial concentration, cTi is the concentration at the position where the temperature is increased by dT and i indicates the index for different states and types of molecules. A temperature difference dT causes the depletion of solvated biomolecules in the region of elevated temperature. This phenomenon of depletion depends on the interface between solvent and molecule. In general, thermophoresis depends on the charge, size, and solvation entropy of the molecules. The thermophoresis of a protein is usually different from the thermophoresis of a protein-ligand complex. This difference is observed due the ligand binding which results a change in size, charge and solvation energy. [93][94] Even if one of these parameters is changed by ligand binding, a wide range of molecular interactions can be analysed.

MST is performed in thin capillaries (Figure 2.4) containing ligand and protein (with a fluorescent probe) as a free solution without immobilization. At the time of MST experiment, infrared laser is used to induce a microscopic temperature gradient. This results into temperature related intensity change (TRIC) of the fluorescent probe and also generates thermophoresis. The binding events influence the fluorophore's microenvironment which ultimately have effect on the TRIC. Similarly, the movement of the molecule in the temperature gradient, depends on the parameters (such as charge, size, and solvation entropy of the molecules) that typically change upon interaction. Finally, a dose-response curve is obtained by plotting overall MST signal against the ligand concentration that can be further used to deduce binding affinity. Other than proteins-ligand binding, MST can also detect binding substrates to enzymes and ligands to liposomes. 94 Unlike some other methods . 95 (such as surface plasmon resonance, SPR), 95 MST experiment is performed without surface immobilization and requires only small volume of sample. Further advancements in MST involves label-free approach that uses intrinsic fluorescence of tryptophan-containing proteins to follow their thermophoresis. 96

Saturation transfer difference (STD)-NMR

Among different screening techniques, nuclear magnetic resonance (NMR) is a powerful tool known to provide structural knowledge. In particular, saturation transfer difference NMR (STD NMR) 97 has emerged as a popular method due to its robustness and ease of implementation. 98 This method is generally used to investigate interactions of weak binding molecules with dissociation constants (Kd) in the millimolar to micromolar range.

Thus, making it ideal for the identification of early lead molecules. In addition, STD NMR has been successfully used to investigate protein-carbohydrate interactions. [99][100] The STD NMR experiment is based upon the principle of the nuclear Overhauser effect (NOE). This phenomenon involves the perturbation of signal intensity of a specific proton (for example) because of the cross-relaxation with another perturbed proton located nearby in space. 101 In case of STD NMR, protons in the proteins can be selectively saturated by irradiating with radiofrequency. Subsequently, the saturation is rapidly transferred across the whole protein, in a process known as spin diffusion (Figure 2.5). When a ligand is present in the binding site of the protein, the saturation is transferred to the protons of the ligand. The ligand protons in closest contact, receive most of the saturation. Due to fast exchange, the ligand returns to solution and its signal is acquired. The signal produced by these protons helps to map the binding epitope of the ligand.

This method is easy to implement for ligand screening as the sample preparation involves a cocktail of ligand which can be used to identify hits simply by recording the STD NMR signals. 102 Further, the different signals for the same compound can be used to construct a ligand binding epitope map, which may provide useful information to understand the relative position of the ligand in the binding site. 103 This method can be used to validate the results of docking studies by comparing STD NMR data with the predicted binding poses of the ligands. 104

X-ray crystallography

X-ray crystallography has become a powerful screening method in structure-based drug discovery that provides structural information on complexes to fuel the drug discovery process. From practical aspect, crystallization involves different necessary steps. In order to crystallize the protein, different crystallization conditions are screened which involve the identification of physical, chemical and biochemical conditions to initiate crystallization and then systematic alteration and optimization of these initial conditions is required to further improve the crystallization process. In the next step, X-ray diffraction is used to collect the quality data that are further processed with the help of appropriate mathematic and informatics tools to solve the crystal structure. Finally, the structure is validated and deposited in the Protein Data Bank. 105

Crystallogenesis

Crystallization of macromolecules is largely empirical since the number of variables is very high, and requires numerous trials to obtain crystals. Crystallization of any molecule including proteins, involves two important steps: nucleation and growth. 106 Nucleation represents a first-order phase transition by which molecules pass from a disordered state to an ordered and thermodynamically stable state. This process ultimately yields small, ordered assemblies which are known as critical nuclei. They provide suitable surface for crystal growth which depends on the diffusion of particles to the surface of the nuclei and their ordered assembling onto the growing crystals. [107][108] Both nucleation and growth, depend on supersaturation of the mother liquor giving rise to the crystals. When the concentration of a protein solution is increased beyond its solubility limit, the solution becomes supersaturated.

The overall phenomenon of crystal growth can be described with the help of phase diagram (Figure 2.6) which illustrates three zones. The first zone with very high supersaturation is called "precipitation zone", that leads to the formation of amorphous aggregates. 109 The next zone with intermediate supersaturation is called "labile zone" where growth and nucleation occur while the zone with lower supersaturation supports crystal growth only. [109][START_REF] Dunne | Factors that mediate colonization of the human stomach by Helicobacter pylori[END_REF] Thus creating a supersaturated protein solution is the immediate objective in growing protein crystals. This can be achieved by altering different parameters such as temperature, buffers, pH, precipitant nature: salt; polymers or organic compounds, addition of ions etc. 109 The crystals of the proteins are usually grown in a physical apparatus which allows the alteration of the properties of the mother liquor and the solubility of the protein by different approaches. One of the such approach is based on the vapor diffusion (Figure 2.7). 106 In this method, droplets containing purified protein under crystallization conditions are allowed to equilibrate in a large reservoir solution which generally contains similar precipitants and buffers in slightly higher concentrations. As the drop and reservoir equilibrate, the concentrations of precipitant and protein are increased in the drop that may result in crystal growth in the drop. 111 The drop can either hang from a cover slip (hanging drop) over the reservoir or allowed to sit at top on a small platform (sitting drop). The chamber is sealed and kept at a constant temperature to allow equilibration between the reservoir and the drop. After crystallization, single crystals are mounted in cryoloops after transfer in cryoprotectant solution if necessary and flash-freezed in liquid nitrogen. This allows diffraction experiments to be done at 100K in order to limit the impact of radiation damages.

Data processing, structure determination, and refinement

A protein crystal is composed of a highly ordered array of protein molecules that forms repeating units called unit cells (Figure 2.8). These unit cells are described by three vectors which have defined angles (α, β, γ) and lengths (a, b, c). Unit cell can build up the whole crystal by applying translational operations. Unit cells can be reduced to bare components called asymmetric units. These minimal arrangements, after applying symmetry operations can generate a whole crystal (Figure 2.8). X-rays are electromagnetic waves with wavelengths in the range between 0.1-100 Å. Since the interatomic distances also fall within this range (for example, C-C bond~1.5 Å), X-rays can reveal the atomic detail of the proteins. In X-ray diffraction experiment, an incident X-ray beam is scattered by the electrons of the protein crystal. When X-rays pass through a crystal, their interaction with the electrons induces oscillation in the crystal, causing electrons to emit partial waves. These partial waves then superimpose constructively (in phase) in certain directions and generate "reflections", which are recorded on the detector (Figure 2.9). The probability of observing diffraction in a particular direction depends on the amplitude of the resulting wave (structure factor F). The X-ray diffraction event can be explained with the help of Bragg's law. According to this, crystals consist of Bragg's planes that are separated by the interplanar distance d. When incident X-ray wave of the wavelength λ hits a lattice plane at an angle θ followed by reflection at the same angle, the constructive interference is possible, only if the path difference (d sinθ)

between the waves is an integer multiple of the wavelength λ. This can be written as:

nλ = 2dsinθ
(2.34) For all the incident X-rays which do not exhibit angles that fulfil Bragg's Law, the resulting scattered waves will show destructive interference and thus no reflection will be observed. 106 Since protein contains numerous atoms, the crystal need to be rotated during data collection to be able to measure complete data (all necessary reflections) and the number of degrees to be collected depend on crystal orientation and symmetry.

Following the data collection, the processing of the diffraction data is done using well established algorithms available in many software packages and program suites. These tools process data and calculate an electron density map. During data processing, the first step is to accurately determine crystal system and unit cell. In addition, orientation of the crystal in the beam is also determined and then indexing can be carried out. 112 In this process, an index is assigned to each reflection on the image, represented as three integers: h, k, and l. Then, reflection intensities are measured using various software tools leading to computer file containing the measured intensity and index of each reflection. 113 The intensity is determined from the amplitude of the diffracted waves incident on the detector and by the phase difference which is expressed as an angle, between them. Amplitudes can be calculated directly from the intensities but information on the phases are lost and needs to be calculated from indirect ways that involves methods like molecular replacement and isomorphous replacement. 106,114 Once the amplitudes and phases are obtained, structure factors can be calculated using the fast Fourier transform (FFT) method. 115 This will generate an electron density map and in the form of the three dimensional contours which can be used to build the protein structure. The quality of the electron density map may be further improved by refinement and model building. The final coordinates for the structure are validated against geometry rules and quality fit and then deposited to the Protein Data Bank (PDB) repository. 116

Isothermal titration calorimetry (ITC)

Isothermal titration calorimetry (ITC) is a technique to determine the thermodynamic parameters of binding between two molecules in solution. [117][118] ITC determines the heat released or absorbed as the result of interaction between molecules, depending if of exothermic or endothermic character. The equation of heat exchange can be used to determine binding affinity. This can be defined as:

𝑄 = 𝑉 𝑜 ∆𝐻 𝑏 [𝑀] 𝑡 { 𝐾 𝑎 [𝐿] 1+𝐾 𝑎 [𝐿] } (2.35)
where Q is the heat evolved or absorbed, Vo is volume of sample cell, [M]t is the total concentration of protein in sample cell, ∆Hb represents the enthalpy of binding per mole of ligand, [L] corresponds to the concentration of free ligand and Ka is the binding constant.

Furthermore, the heat exchange corresponds to the enthalpy of binding (ΔH), and with Ka being related to the free energy of binding ΔG, it is possible to determine also the entropy ΔS, giving access to the thermodynamic parameters of the interaction.

-𝑅𝑇 ln 𝐾 𝑎 = ∆𝐺 = ∆𝐻 -𝑇∆𝑆 (2.36

)
where R is the gas constant and T is the absolute temperature. The dissociation constant (Kd) can be directly obtained as the inverse of the binding constant (Ka).

The instrument consists of two cells which are enclosed in an adiabatic jacket. One of the cell is called sample cell where the compounds to be studied are placed while the other cell is used as reference cell, which is meant only for the buffer used to dissolve the sample (Figure 2.10). In ITC experiment, ligand is usually titrated into the sample cell containing macromolecule (protein). To maintain the isotherm, the apparatus provides higher (or lower) electric power to the system depending on the interactions. The heat change during the titration is calculated by integration of the power over the time (seconds). Therefore, heat released or absorbed during the complete titration corresponds to the fraction of bound ligand. As the titration proceeds, the protein gets saturated with the increased concentration of ligand and less heat is released or consumed. In order to obtain the reliable results using ITC, it is important to use the appropriate concentration of protein and ligand which relies on the parameter "c" value, which can be defined as:

𝑐 = 𝑁 × 𝐾 𝑎 × [𝑃 𝑡 ] (2.37)
where Ka is binding affinity constant (M -1 ), Pt is sample cell concentration (M) and N is stoichiometry. The c value determines the shape of the binding curve. A higher c value (~1000)

will result a very steep curve, hence make it difficult to estimate the Kd value. Similarly, if the c value is <5, then shape of the binding curve will be too shallow. This will not allow accurate estimation of thermodynamic parameters. Therefore, in order to get a good sigmoidal shape of the binding curve, that will allow estimation of Kd and thermodynamic parameters, it is recommended to keep the c value between 20 and 100.

Analysis of crystal structures and fragment screening in BC2L-C-nt

Understanding the structure of target protein and its binding sites is one of the most important steps in drug design. Therefore, accurate prediction of druggable binding sites is equally important at the initial stage of ligand design. The identified pockets can be used to design the ligands against the target protein. Moreover, these predictions can also provide insight into ligand-receptor interactions for lead optimization by suggesting effective strategies to improve receptor complementarity.

As discussed under the objectives of the project, this thesis work aims to design highaffinity glycomimetic antagonists against BC2L-C-nt using molecular modelling and biophysical methods. In this chapter, I will discuss the approaches used to identify druggable sites in the vicinity of fucose-binding site in the BC2L-C-nt. These sites were further employed to virtually screen a small fragment library. Later, the interaction of the identified fragments with a predicted site was confirmed using a set of biophysical techniques (discussed in the next chapter).

Analysis of the binding site in crystal structures

The X-ray crystal structure of the N-terminal domain of BC2L-C (BC2L-C-nt) in complex with methylseleno-α-L-fuco-pyranoside (MeSe-α-L-Fuc, PDB code 2WQ4) 1 is available at high resolution (R = 1.42 Å) in PDB. 2 The structure analysis shows that asymmetric unit contains three peptide chains and three carbohydrate ligands (MeSe-α-L-Fuc), organised around a 3fold pseudo axis of symmetry (Figure 3 oligosaccharides. 1,3 In particular, the crystal structures of trimeric BC2L-C-nt complexed with H-type 1 and Globo-H oligosaccharides are available, as recently solved by Rafael Bermeo in the framework of his PhD thesis in the PhD4GlycoDrug network. 3 The water molecule W1 is deeply buried in the binding site and sandwiched between the protein and the ligand, forming an H-bonding interaction with HO-3 atom of fucose (Figure 3.2). W2 is more exposed to the solvent and mediates an H-bonding interaction between HO-2 atom of fucose and the side chain of Tyr58. The region surrounding the fucose binding site appears interesting as it shows pockets which could be analysed for their druggability and relevance to host small molecules (fragments).

Resolution of first crystal structure of the apo form of BC2L-C-nt

All available crystal structures of the BC2L-C-nt so far were in the holo form , i.e.

complexed with fucosides or fucosylated oligosaccharides. The crystal structures of the complexes displayed some promising pockets in the vicinity of the fucoside binding site. The investigation of such pockets in the apo-protein was needed to verify their occurrence and stability in both the forms. This may also confirm that these additional pockets were not merely formed as crevice due to the ligand (fucose/oligosaccharides) binding at the interface of protomers. Therefore, attempts were made to successfully crystallize the apo-protein interactions with the methyl group of N-acetylgalactosamine (GalNAc) in the complex with Globo-H. 3 Likewise, small differences in the conformation of residues at the N-and C-terminal were noticed due to the H-bond interactions between them (Figure 3.5 B). The minor differences in the conformation of the termini also caused a small displacement (0.6 to 1.0 Å) of surface loops (Val28-Asp35, Asp95-Val100). Analysis of water molecules bridging the fucose to protein showed that W1 is conserved and deeply buried in all the crystal structures while W2 located in the more exposed region was displaced by 1.9 Å in the apo structure (Figure 3.4). The data collection and refinement details for the crystal structure are given in Table 3.1.

These studies illustrated that the apo form of the trimer does not show any significant difference when aligned to the previously solved structures in complex with fucoside or oligosaccharides. 1,3 This also indicates that the residues in the fucose binding site and surrounding pockets do not display strong flexibility and maintained the conformations of loops and side chains necessary for key interactions with the fucoside, even when bound to (apo form) confirms that the region in the vicinity of the fucose binding site could be interesting for drug (glycomimetic) design. The additional region does not display any significant difference in the binding site surfaces (Figure 3.6). Hence, the identified sites were explored for their druggability.

Prediction of druggable sites

After structural analysis of BC2LC-nt, the additional region was explored for the drugability using SiteMap 4 tool. SiteMap creates a grid of points on the surface of protein and based on the depth, size, van der Waals interaction energy, hydrophilicity and hydrophobicity it determines the druggability of the region. Based on these characteristics, a single scoring function called SiteScore is assigned to the potential druggable regions that helps to prioritize the best one for further studies. To run the SiteMap calculations, the PDB structure (PDB 2WQ4) was prepared using Maestro. 5 Crystallographic water molecules were removed from the structure to avoid their effect in binding site prediction. The protein preparation procedure involved adding hydrogen and missing atoms followed by pKa prediction for the protein residues using the PROPKA 6-8 method at pH 7.4. The protonation state was also assigned to the N(Ɛ) atom of histidine (His116) residue. Finally, the protein was subjected to restrained minimization. The SiteMap calculations performed using the PDB structure identified three regions as potentially druggable sites in the vicinity of fucoside binding site which we labelled as X, Y and Z (Figure 3.7). Region Y consisting of residues Ser82, Thr83 and Phe54 in each monomer, corresponds to the area where larger, fucosylated oligosaccharides were observed to bind, including the recently described Globo H hexasaccharide and H-type 1 tetrasaccharide. 3 In addition, two other regions (X and Z) appear promising to host the small frgments. The site X forms a deep cleft extending along the dimerization interface while the site Z is formed by the region between residues Val110 and Arg111. All the three sites are interesting to explore for druggability/ligandability. Therefore, docking protocol was setup involving all the (three) sites in order to identify suitable hits in the identified sites. Thereafter, best fragments could be connected to the fucose core to design high-affinity glycomimetic ligands.

Virtual screening

After analysis of the druggable pockets, virtual screening was performed to identify the fragments (small molecules) binding at these sites. Finally, in order to design glycomimetic ligands, the best fragments could be connected to the fucose core by using a suitable chemical linker. All the calculations for virtual screening were performed using the Schrödinger Suite through Maestro (version 2018-1) graphical interface. 5 The in silico protocol based on docking calculations is shown in Figure 3.8.

Ligand preparation

The Maybridge library of small fragments (rule of 3 diversity set with molecular weight < 300, hydrogen bond donors/acceptors ≤ 3, cLogP ≤ 3, and rotatable bonds ≤ 3) containing 2000 fragments (available at https://www.maybridge.com/) was used for in silico screening. The small molecules (fragments) in the library allow efficient sampling of the chemical space compared to the molecules with greater complexity. This approach also provides room for ligand optimization which can result the final ligand with relatively low molecular weight.

The ligands were prepared for docking using LigPrep tool which generated tautomers, stereoisomers and protonation states at pH 7±2. The calculations resulted in 2904 structures useful for virtual screening.

Models for docking study

The coordinates of the complex (PDB 2WQ4) 1 were obtained from PDB database 2 and prepared using Maestro. 5 Since the three binding sites are almost identical, only one binding site located between chains A and C was used for docking calculations. The two structural water molecules (W1 and W2) bridging fucose and protein were retained (Figure 3.2). The hydrogen atoms were added and pKa was predicted for protein residues using the PROPKA 6- 8 method at pH 7.4 and assigned protonation state to the N(Ɛ) atom of histidine (His116) residue. Finally, protein-ligand complex was subjected to restrained minimization with convergence of heavy atoms to an RMSD of 0.3 Å using the OPLS3 force field. 9 For docking studies, the residues from chain A (Tyr48, Ser82, Thr83, Arg85) and chain C (Tyr58, Thr74, Tyr75, Arg111) were selected as the center of a cubic grid box of size 32×32×32 to define the binding site region. The two important water molecules (W1 and W2) and ligand (MeSe-α-L-Fuc) were also retained as a part of protein during the grid generation.

Another docking grid, was also generated using the same residues but retaining only one water (W1) molecule and the fucose. This docking model could identify some fragments, suitable to replace the water molecule and occupy the site. The docking studies were performed by employing both the grids (models) and XP (extra precision), SP (standard precision) and HTVS (high throughput virtual screening) scoring functions. All the calculations were accomplished using the OPLS3 force field 9 and the flexible docking approach in the Glide (Grid-based ligand docking with energetics) version 7.8. 10

Docking analysis

Docking calculation were performed using both the docking models: using either one or two water molecules in the docking grid while retaining the fucose as part of protein. The results showed that the fragments were docked mainly in the region X and Y in both the models. The region Y is a shallow and exposed binding site which mostly hosted the hydrophobic fragments on the surface. Some of the ligands binding in this region show interactions with the key residues involved in the binding of fucosylated oligosaccharides. 3 Likewise, region Z accommodated a few hydrophilic fragments in the narrow channel formed by the surrounding residues. The region X comparatively forms a deeper binding site where fragments appear to be nestling and generating some specific pattern of interactions.

Therefore, our initial efforts were mainly focused on this region. Analysis of top 200 fragments was performed for the docking results obtained from 6 docking runs involving two types of docking models and three (XP, SP and HTVS) scoring functions. 10 HTVS and SP use the same scoring function, however HTVS protocol is based on the criteria that reduce the sampling, number of intermediate conformations, and torsional refinement. On the other side, XP protocol is based on greater requirements for ligand-receptor shape complementarity and thus uses a more complex scoring function. This leaves out false positives that SP or HTVS scoring functions may let through. The docking results with the two waters model indicated that SP and HTVS methods identify almost the same number of hits at site X while the number was reduced to half using XP approach. In the docking calculations using one water model, the number of hits at site X were increased by a factor of two, due to the occupancy of the hydration site (W2) by fragments. From each docking model, the molecules ranked within top 200 (obtained by XP, SP and HTVS) were analyzed to identify the key residues involved in ligand binding and subsequently to prioritize the best fragments with consensus scoring. The interaction pattern of the top ranked fragments (obtained using three scoring functions) at site X showed that the fragments with benzylamine moiety have better binding affinity. This also identified Tyr58, Asp70 (from one chain) and Asp118 (from neighboring chain) as key residues (Figure 3.9) involved in fragment binding. In most of the top ranked fragments, Tyr58 forms π stacking while Asp70 shows salt bridge interactions with benzylamino group of the fragments. Based on consensus scoring criteria involving XP and SP/HTVS or all the three scoring functions, a total of 94 and 89 fragments were identified as top ranked fragments (at site X) using one and two water models, respectively.

Selection of best fragments from the docking results

The identified top ranked fragments were visually analyzed based on different parameters such as structural diversity, possibility to connect them to the fucose core by considering the binding pose, size of the fragments and distance from the fucose core. The fragments which docked at regions significantly far (>6 Å) from the fucose core and bind on the shallow surface outside the site X were discarded. The remaining 32 fragments were redocked at the site X in order to save the multiple (10) binding poses and know the stability of ligand interactions. Consideration of additional factors like synthetic feasibility, commercial availability and purchasing cost allowed to finalize the best 12 fragments (Figure 3.9, Table 3.2) for experimental studies. Among the final molecules, fragments KL1-KL8 are the top scorer in the two waters model, while the fragments KL9-KL12 are the top scoring fragments in the one water model.

Hit expansion

The identified fragments from docking studies were further employed for hit expansion by using 2D fingerprint based similarity (95%) search based on Tanimoto coefficient 12 in PubChem database. [13][14] Total 28 molecules from both the models (12 from two water and 16 from one water model) were used as input for similarity search which identified 6385 fragments (3339 molecules from two waters model and 3046 molecules from one water model). The fragments were prepared for docking by generating tautomers, stereoisomers and protonation states using LigPrep 15 tool. The calculations generated 15374 structures: 6937 for one water model and 8437 for two waters model. The structures were docked using one or two waters docking models. The schematic of the protocol is given in Figure 3.10. Only some of the identified molecules (from similarity search) showed docking score higher than the query molecules. These molecules were redocked to analyse their multiple binding poses so that the molecules binding outside the binding pocket (site X) or significantly far (>6 Å) from the fucose ring can be discarded. Finally, 19 fragments ( 7fragments from two waters model and 12 fragments from one water model) were obtained. [a] Solubility at higher concentration is not known

[b] dissolved in 10 percent DMSO 123
These new molecules from hit expansion consist of additional flexible moieties in the structure (Table 3.3). The introduction of additional groups in the molecules increased the stereocenters and the structural complexity that ultimately increased the purchasing cost.

Moreover, most of the molecules were not available with the commercial vendors. Therefore, we decided to proceed further with the best molecules selected from the query molecules without using the fragments from the hit expansion. However, the fragments obtained from the hit expansion can be synthesized in future or they can also be used further for glycomimetic ligand design in later studies.

Experimental validation of fragment binding

All the selected fragments were tested using a series of biophysical methods to investigate their interactions with the lectin. 1 The results obtained from each method are discussed in this chapter.

Protein production and purification

As already discussed under the methods section in chapter 2, the primer design, gene amplification and ligation were performed by Rafael Bermeo. 2 For ligation, the genetic sequence coding for the 132 first amino acids of BC2L-C-nt amplified via PCR was inserted into pCold-TEV (Figure 4.1). By employing the same vector, production and purification of the BC2L-C-nt was performed using the protocol described by Rafael Bermeo. 2 The vector (pCold-TEV) includes a promotor (cspA), a translation enhancement element (TEE), trigger factor, and 6-Histidine tag at the N-terminus resulting in fusion protein of about 52.4 kDa. This fusion could be cleaved using the tobacco etch virus (TEV) protease using TEV-cleavage site while the 6-Histidine tag could ease the purification. pCold-TEV-BC2L-CN-132 was transformed by heat shock in the BL21 (DE3) Star strain of E. coli for recombinant production. Then a successful expression was obtained after overnight protein expression at 16 °C induced by addition of 0.01 mM isopropyl β-D-thiogalactoside (IPTG) in Luria Broth (LB) medium. The fusion was cut using TEV protease overnight and the desired protein (BC2L-Cnt) was purified using immobilized nickel affinity chromatography thanks to imidazole

Thermal shift assay (TSA)

A solution of each of the 12 fragments (refer Table 3.2, Chapter 3) of interest (2.5 mM) was used to test the interaction with BC2L-C-nt using thermal shift assay (TSA, ThermoFluor). 3 To validate the protocol, methyl α-L-fucoside (Me-α-L-Fuc) was used as a reference for positive control. The results displayed an expected positive shift (~2 °C) in the melting temperature (Tm) upon Me-α-L-Fuc binding (Figure 4.6), thus validating the protocol. The possible reason is that the fragments KL10-KL12 do not contain ammonium moiety to establish H-bonding interactions with the residues (for example Asp70) in the binding site which probably makes them weak binders. Since negative shift corresponds to destabilization of the protein, this possibly suggests that the fragments bind at the interface of the monomers or to a non-native/partially unfolded state of the protein. 4 The experiment was repeated in the absence of Me-α-L-Fuc to ensure that fucoside does not have any influence in the fragment binding. The results showed similar pattern of smaller negative shift in the melting temperature (

Microscale Thermophoresis (MST)

The screening was repeated using the microscale thermophoresis (MST) [5][6] method.

All the fragments were tested using microscale thermophoresis to confirm their binding/ interactions with BC2L-C-nt. The lysine residues of the protein were labelled with the fluorescein isothiocyanate (FITC) dye and a ratio of 1.6 :1 (protein to dye ratio) was obtained as determined by UV spectroscopy. The protein at 326 nM was used to test the fragments at 2.5 mM. Me-α-L-Fuc was first assayed as a control experiment, but no significant response was obtained, using different dilutions (50 mM to .0015 mM) (Figure 4 The experimental results of TSA and MST indicate that the fragments are binding to the lectin, but they do not afford structural information concerning the interaction, thus cannot be conclusive about the location of binding site of the fragments. Therefore, another screening was performed using STD-NMR 7 and X-ray crystallography 8 for the protein and the fragments.

STD-NMR analysis of fragment binding

Saturation transfer difference (STD) NMR has become a popular technique to characterize weak fragment-macromolecule interactions in solution. [9][10][11][12][13] This technique is sensitive to weak binding events (dissociation constant in μM to mM range) [11][12][13] , thus widely used to characterize protein-carbohydrate interactions. [9][10][11][12][13] In the STD-NMR experiments, macromolecule (protein) is selectively irradiated at the resonance frequency of side chains of specific amino acids which causes transfer of the magnetization from macromolecule to the ligand. Spin diffusion is responsible for the spread of the magnetization from the irradiated residues to the rest of the protein. STD can confirm the binding event and provide information on the regions of the fragment that are in contact with the protein. The protons of the ligand that receive a higher magnetization produce more intense signals on a 1D 1 H-STD spectrum.

In general, methyl group of amino acids such as valine, leucine, or isoleucine, that are often present in the binding site of proteins, are irradiated (between 1 and -1 ppm). 11 The binding fragments usually have preferred interaction with aliphatic or aromatic amino acids of the protein, therefore experiment are performed at different irradiation frequency. 14 The experiments discussed here were performed using irradiation at -0.05 ppm and 10 ppm which correspond to the aliphatic and aromatic protons, respectively.

A first STD-NMR experiment was performed using Me-α-L-Fuc alone with irradiation at -0.05 ppm. This resulted in transfer to resonance for the peaks at 1.1 ppm that corresponds to the methyl group carries by carbon C5 of the fucose ring. This is in agreement with the strong involvement of the methyl group (Figure 4.11) in its binding to BC2L-C-nt, as seen in the crystal structure (PDB 2WQ4).

To investigate the interactions of fragments with BC2L-C-nt, STD-NMR experiments (irradiating at -0.05 ppm) were performed using fragments KL3, KL8 (top scoring fragments in two waters model) and KL9 (top scorer from one water model) in the presence of the protein and of 2mM Me-α-L-Fuc. Two protocols for sample preparation were followed, either by adding the fragment to a pre-incubated solution of protein and Me-α-L-Fuc, or by adding the fucoside to a pre-incubated solution of protein and fragment. The resulting STD spectra were very similar independent of the set up. The samples were prepared at 1:1 ratio between the In addition, STD spectra were also acquired for the fragment KL3, KL8 and KL9 at 10 ppm irradiation frequency. In this case, the aromatic protons of the fragments involved in interactions were observed while no signal was detected for Me-α-L-Fuc (Figures 4.12 C and

Crystal structure of the complex KL3-BC2L-C-nt

To further investigate the binding of fragments, attempts were made to obtain the crystal structure of fragment-protein complexes. All fragments (KL1-KL12) were soluble in water at high concentration allowing their use for soaking experiment. First, the crystals of BC2L-C-nt complexed with Globo H hexasaccharide were obtained by following the method described by Rafael Bermeo. 2 Crystals were obtained within 48 hours in the form of clusters of plates (Figure 4.17) which were used for the soaking in fragment solution.

Thereafter, these crystals were tested for the X-ray diffraction.

The crystals containing fragments KL10, KL11 and KL12 did not diffract at sufficient resolution to allow data collection. However, crystals soaked with the other fragments (KL1-KL9) diffracted at a resolution near 2 Å, and thus data were collected. All nine structures were solved using molecular replacement using 6TIG as search model and the electron density was analysed in the binding sites. In all cases, Globo H was present, but no electron density could be located for the fragments at the predicted site (X), except for KL3. This indicates that the fragments are not able to bind to the target protein in the experimental conditions used.

Nonetheless, crystal soaked with KL3 (3-(2-Methyl-1H-imidazol-1-yl) benzylamine) diffracted at 1.9 Å resolution and electron density was visible for the fragment at the interface between monomers which clearly indicated the binding at the expected site. Data collection and refinement details of the complex are given in Table 4.1 b Rmerge = ∑hkl ∑i |Ii(hkl)  I(hkl)|/ ∑hkl ∑iIi(hkl). c CC½ is the correlation coefficient between symmetry-related intensities taken from random halves of the dataset. d The data set was split into "working" and "free" sets consisting of 95 and 5% of the data, respectively. The free set was not used for refinement. The analysis of three binding sites located at the interface of trimer showed that the fragment reproduces the same binding interactions and thus binds with the identical pose in the three binding sites (Table 4.2).

Table 4.2 Analysis of interactions of BC2L-C-nt with KL3 in three binding sites in the trimer.

[a] Only present in two binding sites

[b] The distances were calculated from the nearest atoms in the ligand and the protein for hydrophobic contacts and π-π interactions. Mean distance and standard deviation were calculated from the distance of ligand and protein atoms in each binding site. The crystal structure complex of BC2L-C-nt and the fragment KL3 showing binding at the site X is fully consistent with the predictions of binding pose obtained from STD-NMR experiment (Figure 4.12). The results indicated proximity of the benzylic methyleneamino group of the fragment (KL3) to aliphatic residues (Asp70 and Ser119 in the X-ray structure) of BC2L-C-nt.

Ligand atom

Similarly, the crystal complex showed that the methyl group of KL3 is located in the vicinity of Tyr58 side chain, hence responds to irradiation at 10 ppm in the STD experiment. In addition, the water mediated H-bond interactions with Globo H were identical to the previously studied complex. 2 The results of X-ray crystallographic screening finally validated the results of virtual screening (docking) and the ability of the site X (ligandability) to host the fragments.

Affinity analysis using ITC

The biophysical methods including X-ray crystallography confirmed the binding of KL3 at the expected site. Therefore, isothermal titration calorimetry (ITC) method was used to measure the binding affinity of KL3 for BC2L-C-nt. 15 Before running the titration using protein and ligand, control measurements were recorded for the titration of fragment with the buffer (Figure 4.20 A). Subsequently, titration of the lectin by the fragment (KL3) was performed (Figure 4.20 B). The measurements showed small exothermic peaks after the correction for buffer mismatch (Figure 4.20 C). The integrated curve was fitted using one-site model with stoichiometry of one. The final fit determined the binding affinity (Kd) of 877 µM. However, the thermodynamic contributions could not be estimated due the low c-value of the experiment. The fragment titration was repeated using the same experimental setup that gave the similar results indicating that the fragment (KL3) binds at the expected site with a sub-millimolar binding affinity.

Summary

The biophysical methods used to investigate the binding of the fragments showed interaction/binding with the BC2L-C-nt. Remarkably, the structural complex of BC2L-C-nt with the fragment KL3 confirmed the druggability (ligandability) of the predicted site (site X) in the vicinity of the fucoside binding site. Thus, the experimental studies validated the results obtained from computational screening. The information from the screening results can be used further for structure-based design of high-affinity glycomimetic ligands by connecting the best fragments to the fucose core. This can be achieved using suitable linkers.

Importantly, a robust synthetic route to glycomimetics will help in designing the high-affinity ligands against the target. obtained from the solution while complexes led to clusters of plates which were broken to single plates.

The fragments were tested for their aqueous solubility at high concentration and stock solutions were prepared. The globo-H complexes and apo form crystals were soaked overnight in solution containing 0.5 μl volume of fragment solution (from stock) and 4.5μl of 2.5 M sodium malonate used for cryoprotection. This resulted in final concentration of 2 mM, for the fragments KL1, KL7 and KL11, 2.5 mM for KL12, 5 mM for KL2, KL5, KL6, KL8 and KL10 and 10 mM for KL3, KL4, KL9. For KL2 and KL12. 10 percent DMSO was added to achieve the above concentration. The crystals were flash-cooled in liquid nitrogen prior to data collection. The data was collected on the beamline Proxima 1, synchrotron SOLEIL, Saint Aubin, France, using an Eiger 16 m detector (Dectris, Baden, Switzerland). The data was processed using XDS and XDSME. [16][17] The CCP4 suite was used for all further processing. 18 The coordinates of the monomer A of PDB code 2WQ4 were used as search model to solve the structures of the apo form and the complexes with BC2L-C-nt by molecular replacement using PHASER. 19 Refinement was performed using restrained maximum likelihood refinement and REFMAC 5.8 20 interspaced with using manual rebuilding in Coot. 21 for cross validation, 5% of the data were set aside. Hydrogen atoms were added in their riding positions during refinement. Library for the fragment was made using ligand builder in Coot. All carbohydrates were validated using Privateer in CCP4i2 prior validation using the PDB validation server and deposition to the Protein Data Bank under code 7BFY for the apo form and 6ZZW for the complex.

ITC measurements

The ITC experiments were performed at 25 °C with an ITC200 isothermal titration calorimeter (Microcal-Malvern Panalytical, Orsay, France). The protein (BC2L-C-nt) and ligand

Design of bifunctional glycomimetic ligands

Strategies to connect fragments to the sugar core

The studies on fragment screening was performed with fucose present in the binding site with the aim of designing glycomimetic ligands by connecting the best fragments to the fucose core. The general idea for ligand design was to obtain the bifunctional molecules which can occupy the fucoside binding site as well as the site X simultaneously. To achieve this, selection of suitable linkers was made by considering the synthetic feasibility of the glycomimetic ligands and the possibility to maintain the binding pose at the site X. Since the best fragments were docked at 3-6 Å distance from the anomeric carbon of the fucose, the carbohydrate functionalization through the anomeric position was preferred to design glycomimetic ligands. In the bifunctional ligands, the fragments can make interactions with the hydrophobic residues in the site X that might contribute to counterbalance the inherent hydrophilicity of the sugar. Such molecules can further improve the selectivity and the binding-affinity. In order to maintain the expected binding pose of the glycomimetic ligands, different factors like rigidity, orientation and length of the linkers were considered to link the fragments to the fucose core. Finally, considering the synthetic feasibility, chemical linkers such as alkyne, amide, triazole and alkene moieties were considered suitable for the present studies (Figure 5.1).

A new model for the docking of fucoside-linker conjugates

In order to investigate the orientation of the chemical linkages, docking was performed for the several fucoside-linker conjugates in the sugar binding region. The crystal structure of BC2L-C-nt (PDB 2WQ4) was prepared using Maestro for docking as described previously under the fragment docking section (Chapter 3, section 3.4.2). Docking grid was prepared without fucose but including two water molecules (W1 and W2). The centroid of fucoside was located in the active site between chain A and chain C in order to define a cubic grid box with dimensions 32×32×32 Å. Selenium atom in the fucoside of the crystal structure (MeSe-α-L-Fuc) was replaced by oxygen and then redocked at the sugar binding. The protocol reproduced the co-crystallized pose (RMSD 0.1 Å), hence validating the docking protocol using Glide (version 7.8). 1 The selected chemical linkers were then attached to the C-1 of fucose in β-configuration (equatorial) to be directed towards the hydrophobic pocket. The docking procedure allowed to determine their predicted preferred orientation and conformation.

The docking results show that the alkyne function has the desired orientation in βfucosylacetylene with an acceptable length (4.2 Å) required to connect the best fragments from the virtual screening in the site X. Similarly, amide linker (Figure 5.1) also appears interesting offering polar interactions with the structurally conserved water molecule W2. In addition, triazole function and alkene bond could be additional options for ligand design. After selection of the linkers, strategies to chemically connect the fragments to the fucose core were designed. The grid defined above was further used for the docking studies of glycomimetic ligands by employing extra precision (XP) and standard precision (SP) scoring functions in Glide (version 7.8). 1

Selection and building of bifunctional glycomimetic ligands

The fragments of interest determined earlier (see Table 3.2, Chapter 3) were functionalized with additional groups (such as -COOH) and used as query structure for Tanimoto coefficient based similarity (95%) search to obtain the functionalized fragments from the PubChem database. 2 Some of the functionalized fragments were commercially available that would help to facilitate the synthesis of glycomimetic ligands in collaboration with Rafael Bermeo. For example, the fragments KL1 and KL2 provide the possibility to be linked to the fucose anomeric position with the amide linkage. Hence, the fragments similar to KL1 and KL2 with carboxyl and amide moieties have been identified (Table 5.1). These fragments were later used to design the glycomimetic ligands. In Table 5.1, the fragment number 15 is likely to have low solubility due to fused ring system, therefore similar fragments with carboxyl functionalization have been identified using similarity search based on Tanimoto coefficient in the SciFinder (https://scifinder-n.cas.org/).

The resulted fragments (Annexe, Table 1) have heterocyclic rings which can increase the polarity of the fragments and improve the solubility. The substitution at different positions in the fused ring system may also influence the binding affinity of the designed (glycomimetic) molecules.

After selection of the linkers and functionalized fragments, the first generation of antagonists was designed as β-C-and β-N-fucosides using Maestro 3 to target the fucoside binding site and the site X simultaneously in BC2L-C-nt.

Docking studies of bifunctional glycomimetic ligands

All the designed bifunctional ligands were then studied for binding using docking studies. The ligands were prepared for docking using the LigPrep 4 tool. The protonation states were generated at pH 7±2. The new docking model (discussed in previous section) was employed as the receptor grid for the target (BC2L-C-nt). The glycomimetic ligands (Table 5.2 and 5.3) designed using the best fragments from virtual screening were studied using XP and SP approaches in Glide. 1 Likewise, the ligands (Table 5.4) derived from the functionalized fragments resulting from similarity search (using KL1 and KL2 from Table 3.2) were also studied using two waters docking model and the XP and SP scoring functions. In addition, the glycomimetic molecules with heterocyclic rings (Annexe, Table 2) designed using the functionalized fragments were also docked using the two water model and the same (XP and SP) docking approaches.

The docking results for the molecules with alkyne and amide linkers indicate that the sugar and the non-sugar part of glycomimetic ligands establish interactions with the key residues already identified by the docking studies of the fragments. The main interactions between the ligands and the protein involve π-π stacking with Tyr58 and the salt bridge or Hbond interactions between ammonium group (NH3 + ) and Asp70 (O) in the site X. Moreover, the amide linker forms additional H-bonding interactions with the structurally conserved water molecule W2. Thus, the expected binding pose with all the key interactions were maintained in the docking studies which was again confirmed by the analysis of multiple (10) binding poses of the ligands. The ligands with fused ring system involving different heterocyclic rings, do not display any significant difference in their docking scores (Annexe, Table 2) due to minor structural differences. These ligands have been classified into different subclasses based on the heterocyclic system of the non-sugar part (Annexe, Table 2). After docking studies of the designed glycomimetic ligands, fragment 12 (-COOH functionalized fragment) in Table 5.1 was purchased from a commercial vendor. Other fragments from this extended set as well as fragments with heterocyclic rings (Annexe, Table 2) were not considered for purchasing due to their high cost or non-availability with the vendors. However, they can be considered for design and in-house synthesis of glycomimetic molecules in future.

The ligands with alkyne and amide chemical linkers appear more interesting as they are more rigid to direct the ligands towards site X and, in particular, the amide linker establishes additional interactions with water molecule (W2). Hence, some of the prioritized ligands with alkyne and amide linkers were further studied using MD simulations (discussed later) to evaluate the conformational behaviour of the ligand-protein complexes, that could be compared with the results of molecular docking studies and with experimental results. The glycomimetic molecules (Table 5.2) designed based on the best fragments from the initial fragment screening were selected for synthesis in collaboration with Rafael Bermeo. After synthesis, the glycomimetics were further tested for their binding using a series of biophysical methods such as isothermal titration calorimetry (ITC), surface plasmon resonance (SPR) and X-ray crystallography (discussed in next chapter). The values of binding affinity (Kd) for some of the synthesized ligands are given in Table 5.2.

Most of the affinities were measured equivalent to the binding affinity of the monosaccharide (Me-α-L-Fuc) in the millimolar range. Nevertheless, the two alkyne-bound ligands Lfuc-Aky-KL07 and Lfuc-Aky-KL08 showed better binding affinity then the monosaccharide with nearly 2-fold and 9-fold improvement, respectively. The results indicate that the use of alkyne linker could be an efficient approach to design the bifunctional ligands with a non-sugar part binding at the site X in BC2L-C-nt.

MD simulations of BC2L-C-nt complexes with glycomimetics

After docking studies, molecular dynamics (MD) simulation can be employed to further investigate the key residues or interactions involved in ligand binding. The static view of ligand-receptor interactions provided by docking studies can be verified by performing MD simulation studies of ligand-receptor complexes in solution with ions. Thus, the studies can be performed using a setup much closer to real physiological condition. The results can provide insights into docking studies and help rationalizing experimental binding data. The combination of two in silico techniques (docking and MD) could improve the reliability of the results.

Force field parameterization of glycomimetic ligands

Based on the docking studies and the feasibility of synthesis using suitable chemical linkers (alkyne and amide), three bifunctional glycomimetic ligands, Lfuc-Aky-KL07, Lfuc-Aky-KL08 and Lfuc-Amd-KL13, were prioritized for MD simulations in complex with BC2L-C-nt. The results can be compared with the docking studies by analyzing the major contacts during the MD. This can further rationalize the protein-ligand interactions.

To accomplish the calculations using MD simulations, partial atomic charges and force field parameters for the non-sugar part of the ligands were required. Therefore, PyRED server 5 was used to derive the partial atomic charges for the aglycone moiety of the glycomimetic ligands. PyRED derives charges by restrained electrostatic potential (RESP) fit at the HF/6-31G(d) level using Gaussian16. 6 The program optimizes the input molecular structures using the Gaussian program and carries out single point energy calculations. The minimized structure is used to compute the corresponding molecular electrostatic potential (MEP). Subsequently, the RESP program is executed to fit the atom-centred charges to the MEP. Finally, mol2 files with derived partial charges are generated (Annexe, Table 3 A, B, C).

The program can generate charges compatible with the AMBER force fields. The parameters for the bond, angles and torsion of the aglycone linkage were obtained from the AMBER force field (GAFF2). Finally, the charges derived by PyRED server were employed for generating the input files for MD simulations using AMBER (version 18) biomolecular simulation package. 8

MD simulation setup

The X-ray crystal structure of the N-terminal domain of the lectin in complex with methylseleno-α-L-fucopyranoside (PDB code 2WQ4) was prepared for MD simulations.

Initially, the complex with the fucoside in all three binding sites of the trimer was prepared for MD simulation. In another setup, the system involved BC2L-C-nt trimer in complex with the glycomimetic molecule (generated using docking) in the binding site between chain A and C while maintaining the co-crystallized fucosides (methylseleno-α-L-fucopyranoside) in the two other binding sites. Selenium in the fucosides was changed to oxygen linked to anomeric carbon to create Me-α-L-Fuc. The AMBER force field ff14SB 9 and GLYCAM06-j 10 parameters were used for protein and fucoside respectively. The charges derived using PyRED server and the force field parameters from AMBER force field (GAFF2) were used for the non-sugar part of the molecules to generate input files for the MD simulations. Missing side chains and hydrogen atoms were added using tleap program of AMBER18. 8 The first conformation of the residues was retained for the alternate side chain conformations. The ligand-protein complex was solvated by adding TIP3P 11 water molecules to fill a truncated octahedral box extending 12 Å from the solute and with a van der Waals closeness parameter set to 0.7. Sodium and chloride ions were added to neutralize the system at the physiological concentration of 150 mM. Hydrogen mass repartitioning was performed on the topology file using parmed. 12 This allowed 4 fs time step for MD simulation run. The system was minimized for 3000 cycles using the steepest descent (1000 cycles) and conjugate gradient (2000 cycles) algorithms and then subjected to 100 ps MD in NVT (canonical) ensembles to gradually raise the temperature to 300 K. The system was equilibrated for 500 ps stepwise in isothermal-isobaric (NPT) ensemble with decreasing constraints on the backbone of 50, 25, 10, 5, 2.5, and 1 kcal mol -1 Å -2 . MD was run in periodic boundary conditions using the PMEMD module of AMBER18. Nonbonded interactions were treated with cutoff radius of 9.0 Å. 600 ns and 1µs production ensued for the complexes with Me-α-L-Fuc and glycomimetics, respectively at 300 K and 1 atm. The temperature and pressure was controlled using the Langevin thermostat 13 and Berendsen barostat 14 , respectively. Frames were saved for both the complexes: every 200 ps for Me-α-L-Fuc complex and every 40 ps for glycomimetic complex. Finally, a part of the total saved frames was analysed.

Molecular dynamics simulations of methyl α-L-fucoside (Me-α-L-Fuc) in complex with

BC2L-C-nt

The 600 ns second simulation demonstrated that the protein backbone of BC2L-C-nt trimer was stabilized (RMSD 1 Å) during the MD simulation (Annexe, Figure 1). The ligand formed key interactions with the residues in the binding site and similar to the co-crystallized conformation, the anomeric position of the ligand was oriented towards the site X. The analysis of ligand also showed that hydrogen bond interactions with key residues in the binding sites were maintained in the sampled structures. The frames in the trajectory were (chain C) also appear to play important role in ligand binding. The multiple H-bonding interactions ultimately resulted a very low RMSD (0.1 Å) for the fucoside in all the binding sites in the MD simulation performed for 600 ns. Moreover, the key water molecules (W1 and W2) also showed bridging H-bonds between the ligand and the protein. Thus, the water molecules at these two sites can be important for the ligand binding. agreement with the docking studies. Like Lfuc-Aky-KL07, the ammonium group (NH3+) in the ligand establishes additional interactions with Ser119 (Figure 5.6). However, due to the presence of two methyl groups occupying the site X, the non-sugar part is pushed towards Asp118 and forms H-bonding interactions. Consequently, a higher RMSD (2.0 Å) was observed for the non-sugar part of the ligand (Annexe, Figure 3). Similar to Lfuc-Aky-KL07, the sugar part of Lfuc-Aky-KL08 displayed a lower RMSD (0.5 Å). The results from computational studies thus indicated that the ligands designed using alkyne linker might be interesting for the synthesis and ligand binding studies.

MD simulation of glycomimetic ligand Lfuc-Amd-KL13 in complex with BC2L-C-nt

In addition to the MD simulation studies of the ligands with alkyne linkers, MD simulation were performed for the docking complex of BC2L-C-nt with Lfuc-Amd-KL13. The ligand consists of fragment (KL13) connected through an amide linker. The sugar part of the Therefore, in addition to the ligands designed using alkyne linkers, it was also prioritized for synthesis and ligand binding studies with the help of collaborators.

Experimental validation of glycomimetic binding using ITC and X-ray crystallography

Synthesis and evaluation of the designed molecules (glycomimetics) against BC2L-Cnt was performed by Rafael Bermeo. The ligands were first tested for their binding using STD NMR and then later further characterized for the interactions and binding affinity using surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC). The results indicated that the ligands bind with different binding affinities (Kd) ranging from millimolar to micromolar (Table 5.5). Table 5.5 Comparison of the binding affinities determined using ITC for monosaccharide (Me-α-L-Fuc) and the designed glycomimetics (Lfuc-Aky-KL07 and Lfuc-Aky-KL08). The binding affinity of Lfuc-Amd-KL13 was estimated using SPR, therefore required further evaluation using ITC.

The alkyne-bound molecules appear strongest binders, with molecule Lfuc-Aky-KL08 showing almost 9-fold increase in affinity when compared to the monosaccharide (e.g Me-α-L-Fuc).

Moreover, crystallographic complexes (PDB not released) with two glycomimetic ligands: Lfuc-Aky-KL08 and Lfuc-Amd-KL13 were also solved by Rafael Bermeo. Lfuc-Aky-KL08 consists of fragment KL08 connected via alkyne linker while Lfuc-Amd-KL13 is composed of fragment KL13 and an amide linker. The complexes of both the ligands (Figure 5.8 and 5.9) show that the co-crystallized binding poses of the ligands with all the key interactions were matching the binding poses predicted by docking and MD simulations studies. The top ranked binding of the Lfuc-Aky-KL08 and Lfuc-Aky-KL13, respectively. Thus, these experimental studies further confirmed the druggability of the binding site and also validated the in silico ligand design for the BC2L-C-nt. The two ligands in the co-crystallized complexes and compound Lfuc-Aky-KL07 from ITC studies can be used as 'hit' compounds for further optimization studies towards a 'lead' structure aiming functional inhibition of BC2L-C-nt. Thus, these molecules were further used to design new molecules with additional substitution in the existing structures that might improve their binding affinity.

Strategies to improve binding affinity of the glycomimetics

The binding of a macromolecule to another macromolecule or a drug-like small molecule can cause displacement of surface water to bulk that can contribute to the overall free energy of binding. [15][16][17][18][19][20] The carbohydrate binding site in lectins usually consist of water molecules which established bridging H-bonding interactions with protein and the ligand.

Therefore, the study of the water at molecular surfaces can be crucial in molecular recognition and ligand binding. The water molecules at the hydrophobic surfaces have unfavourable effects, due to combination of entropic and enthalpy costs, and that allows their displacement to bulk. The water-protein interactions in a binding pocket display a pattern which is strongly replicated by the binding of small molecules. 21 Hence, these hydration sites in the binding pocket can provide valuable information about the key features that a molecule could mimic to favour its binding to the target.

In addition to the analysis of water molecules, the analysis of the region in the vicinity of the binding site can guide towards the structure optimization of the existing ligands to improve their binding affinity. The substitution in the ligand by a suitable chemical group could help to establish interactions with the additional residues in the binding site, thus can improve the ligand efficiency and binding affinity.

Analysis of water thermodynamics at binding site using Grid Inhomogeneous Solvation Theory (GIST)

Water molecules play an important role in the free energy of molecular recognition. 22- 24 High-resolution crystal structures of ligand-protein complexes have demonstrated that water molecules frequently mediate protein-ligand interactions. 25 The studies have shown that the hydrogen bonds formed between the ordered water molecules and ligands can have significant impact on the ligand specificity and binding affinity. [26][27][28] The displacement of highenergy water molecules from the binding site surface to the bulk solvent can improve the binding of the ligands by several folds. [29][30] Therefore, introduction of substituents in the ligands to displace these water molecules can show remarkable improvements in the binding affinity. [31][32] Hence, analysis of solvation thermodynamics in protein-ligand binding studies could be an efficient approach in structure-based drug design that can contribute towards ligand optimization to enhance the binding affinity. [29][30] Grid inhomogeneous solvation theory (GIST) 33 method calculates thermodynamic values of solvent located within a defined region using the grid discretized inhomogeneous solvation theory formulation and produces quantitative thermodynamic data for each grid box or voxel. This calculation provides a detailed map of water thermodynamic and structural quantities in a defined region of interest including: interaction energy of water found in a voxel with all other water molecules or solute molecules and water occupancy within each voxel. The information can be used to decide whether the water at a given location is favourable or not compared to the bulk distribution.

For the calculations of thermodynamic properties of the solvent near glycomimetic binding site, the docked complex of BC2L-C-nt with glycomimetic molecule (Lfuc-Aky-KL07) was solvated (using TIP3P water model) in a cubic box and topology and coordinate files were generated (using tleap) as input for MD simulation. Protein was restrained (200 kcal/Å 2 ) to avoid translation or rotation and only water was minimized for 20000 cycles using steepest descent (1500 cycles) and conjugate gradient algorithms. Next, water and the protein hydrogen atoms were minimized to another 20000 cycles. System was heated to 50 K while restraining the protein heavy atoms in NVT (canonical) ensembles, followed by increment from 50 K to 300 K for 200 ps. The system was relaxed restraining the protein heavy atoms in NPT ensemble for 10 ns followed by restraining all atoms in the protein at 300 K for 5 ns in NVT ensemble. MD simulation was run using PMEMD module of AMBER18 in NVT ensemble for 30 ns sampling every 1 ps frame.

Analysis of trajectory using GIST post processing (GISTPP) 33 tool identified three regions (A, B, C) with higher occupancy (Figure 5.10) of water molecules within 3.5 Å from the ligand. The grid points in these regions were grouped and thermodynamic properties of the sites were calculated (Table 5.6).

Figure 5.10 Three hydration sites A, B, C have been identified with higher solvent occupancy.

The site A was predicted with lowest free energy while the other two sites (B and C) indicated the region of slightly higher energy but both the water molecules are directly connected to the fucose ring and appears important for ligand binding in all the crystallographic complexes of BC2L-C-nt with glycomimetics and monosaccharide. Therefore, site C can be explored further for the possibility of displacement of water by suitable moiety to further improve the ligand affinity. Ligands with additional polar group such as ammonium can be designed to mimic the interactions of water molecule at site C.

Docking studies of additional ligands with guanidine moiety

In order to improve the binding affinity of the glycomimetic ligands (Lfuc-Aky-KL07, and Lfuc-Aky-KL08), the benzylamine moiety have been further modified with a substitution of guanidine moiety (Table 5.7). This can probably improve ligand efficiency by establishing additional H-bonding interactions with the binding site residues (e.g. Asp70). The newly designed ligands were docked using the existing model (with two water molecules) for the docking studies of glycomimetics. The results show that the key interactions were maintained by the newly designed ligands. In addition, bidentate H-bonding interactions with two oxygen atoms of the side chain of Asp70 were established. Likewise, -CH2-group in the ligand shows hydrophobic contacts with -CH2-group in the side chain of Ser119 (Figure 5.11 and 5.12). These additional interactions ultimately enhanced the binding affinity (glideScore) of the newly designed ligands by 1 kcal/mol (approx.). These two ligands have been already

synthesized by another collaborator at the University of Milan and further studies related to their binding to the target are still to be performed at the Université Grenoble Alpes (UGA), France.

Conclusions and perspectives

The prevalence of drug resistant infections has challenged the existing treatment regimen using antibiotics. There is a need to discover and employ alternative and complementary therapies to counteract these life threating infections. In the fast few decades, the use of anti-adhesion molecules targeting virulence factors such as lectins has been proven an attractive approach to counteract the infections by disarming the pathogens.

This has been achieved by the development of glycomimetic inhibitors of various lectin targets.

Within the scope of the PhD4GlycoDrug consortium, this thesis work aimed to design glycomimetic antagonists of the N-terminal domain of the BC2L-C lectin (BC2L-C-nt) from the drug resistant pathogen known as B. cenocepacia. To achieve the objectives, this project employed a fragment-based approach to design glycomimetic antagonists of the target protein (BC2L-C-nt). This approach under the structure-based drug design involved the application of the computational and experimental methods to achieve the objectives of the research.

The initial studies were focused towards the binding site prediction and target evaluation by computational tools which identified additional druggable regions near the fucoside binding site in the lectin (BC2L-C-nt). These additional regions have been explored

further to evaluate the druggability by employing virtual screening of a small fragment library in the vicinity of the fucose-binding site. This identified an interesting region (region 'X') that could host the drug like fragment by establishing some key interactions in the site. The interactions of the fragments with the lectin have been confirmed using a group of biophysical techniques, including X-ray crystallography. Remarkably, the binding mode of one of the fragment (KL3) has been validated by X-ray crystallography at high resolution confirming the ability of site X to host drug-like fragments.

The computational studies also identified suitable linkers that could be used to chemically connect the fragments to the fucose core to obtain high-affinity bifunctional glycomimetic ligands. In addition, the MD simulation studies of some of the glycomimetics indicated that they establish stable interactions in the region X, thus reproducing the results in agreement with the docking studies. Further studies involving glycomimetic synthesis and evaluation of their binding to the target were performed in collaboration with another colleague (Rafael Bermeo) in the Phd4GlycoDrug network. Interestingly, the crystal complexes of BC2L-C-nt with three bifunctional glycomimetic ligands (designed connecting fragments and fucose core) again confirmed the druggability of the identified site, thus also validated the computational predictions. Hence, the first generation of glycomimetic ligands with binding affinities in micromolar range have been successfully designed.

The planned objectives of the project under the framework of the PhD4GlycoDrug-Marie Skłodowska-Curie Innovative Training Network (MSCA ITN) have successfully completed. The first generation glycomimetic ligands provide further opportunities to design high-affinity glycomimetic ligands.

Future studies to design high affinity ligands can be benefitted from the structural information obtained from the solved crystal structures of BC2L-C-nt complexes with a fragment, glycomimetic ligands and oligosaccharides. This can further help in designing new ligands with additional moieties that can mimic the interactions of the oligosaccharides. In addition, the druggable region near the fucose binding site that was identified as site Y and site Z can be targeted to further enhance the binding affinity of the ligands. The best fragments binding at these two additional regions can be prioritized and strategies to chemically connect them to the fucose ring can be briefly explored. The ligands designed based on the targeted sites can be used to generate libraries of glycomimetics with potential as lectin antagonists. In silico studies on the ligands can be further extended to optimize the binding affinity using MD simulations and binding free energy calculations. Likewise, the multimeric structure of BC2L-C-nt can provide opportunity for multivalent approach to improve the binding affinity of the ligands that can antagonize the target. Hence, future studies based on structure-based approaches and robust synthetic routes to synthesize glycomimetics can lead towards the high-affinity ligands as anti-adhesive agents against B.

cenocepacia. Further, it will be interesting to test the synthesized molecules in functional assays such as biofilm formation, cell-adhesion and hemagglutination.

Lys23 (in three sites) and Leu87 (in one site) illustrate significant difference in side chain orientation which slightly changes the shape and the size of the predicted site. Nonetheless, these sites were top ranked by the SiteMap tool for their propensities to bind a ligand.

Conclusions

The results indicate the presence of druggable sites in a bacterial lectin BambL, which could be used to design allosteric inhibitors. The computational and experimental methods demonstrated the binding of drug-like molecules in the predicted binding site in BambL.

Further studies using PrOF NMR demonstrated that fragment binding to the secondary site induced conformational changes in the carbohydrate-binding site in BambL. This indicates a communication between two spatially distant binding sites in the lectin. Site-directed The key residues identified in the binding site are shown in the binding pose of 16A02. mutagenesis within the predicted site and the carbohydrate binding pocket also demonstrated conformational changes in distal regions from the mutation sites. The results suggest the presence of allosteric site in BambL. Additional computational analysis and experimental studies on RSL and AFL lectins showed structural similarities and demonstrated hit rates comparable to BambL. The results suggest the presence of allosteric sites in other βpropeller lectins. These observations can support future studies that aim to develop drug-like allosteric inhibitors against bacterial and fungal lectins.

1

 1

Figure 1 . 1

 11 Figure 1.1 (A) As per the estimates, deaths due to antimicrobial resistance could grow to 10 million by 2050. (B) The economic loss due to the impact of AMR on the world's economy is predicted in trillions of US dollars between 2014 and 2050. Adapted from O'Neill (2014).1

Figure 1 . 2

 12 Figure 1.2 Representation of different stages in a biofilm formation. (1) As a first step, planktonic cells attach reversibly and then adhere to the surface. (2) Subsequently, bacteria form a monolayer with irreversible adhesion and also produce an extracellular matrix. (3) Next, multilayers appear to form a microcolony. (4) In later stages, the mature biofilm forms "mushroom like" structure containing polysaccharides. (5) Finally, the cells are dispersed from the biofilm to start a new cycle. Adapted from

Figure 1 . 3

 13 Figure 1.3 Different strategies for anti-adhesion therapy to counteract bacterial infection. Bacterial adhesion can be hampered by interfering with the biosynthesis of adhesin (A), assembly of adhesin (B), or the assembly of host-receptor (C). Binding inhibition can be achieved by competitive replacement of the adhesion from the receptor (D) or by competitive replacement of the host from

Figure 1 . 4

 14 Figure 1.4 Examples of common lectin folds complexed with the ligands. Complex of (A) FimH from E. coli with mannoside (PDB 1TR7) and (B) LecA tetramer from P. aeruginosa with D-galactose showing β-sandwich fold (PDB 1OKO). (C) The complex of DC-SIGN (PDB 2XR5) from human with a mannoside showing a C-type lectin carbohydrate recognition domain with a mixed fold. The Ca 2+ ion is shown in dark grey sphere.

Figure 1 . 5

 15 illustrates some infections mediated by adhesins, lectins, toxins etc. at host-pathogen interface. C-type Lectin Receptors (CLRs), a class of proteins expressed on the membrane of antigen-presenting cells (APCs) such as macrophages and dendritic cells (DCs) are often used as entry receptors by viral pathogens.[66][67] CLRs recognised conserved carbohydrate epitopes on diverse pathogens and initiate specific adjustments of the immune response. DC-SIGN (Dendritic Cell-Specific

Figure 1 . 5

 15 Figure 1.5 Strategies used by pathogens for host recognition and adhesion. Adapted from Imberty and co-workers (2008).62

Figure 1 . 6

 16 Figure 1.6 The cellular glycocalyx acts as biological interface that mediates the exchange of information between cells and their surroundings. The glycans in glycocalyx are molecular target for opportunistic pathogens. Adapted from Purcell and co-workers (2019).35

Figure 1 . 7

 17 Figure 1.7 Structures of oligosaccharide epitopes of histo-blood groups with schematic representation. Fucose, sialic acid and GlcNAc are shown according to the SNFG nomenclature.90

Figure 1 . 8

 18 Figure 1.8 (A) Type 1 fimbrial adhesin FimH of E. coli binds to oligomannose present on the host uroepithelial cell during urinary tract infections. (B) Adhesins (BabA, SabA and LabA) of Helicobacter pylori. Adapted from Poole and co-workers (2018).100

Figure 1 . 9 .

 19 Figure 1.9. Examples of glycomimetic inhibitors that have successfully reached the market.

Figure 1 . 10

 110 Figure 1.10 Structures of potent FimH antagonists designed using different optimizations strategies.

Figure 1 . 11

 111 Representative structures of antagonists of LecA.

Figure 1 .

 1 Figure 1.12 (A) Schematic representation of LecB-like proteins with number of amino acid (aa) in B. cenocepacia. The arrows with shaded part indicate the predicted lectin domains. Adapted from Lameignere and co-workers (2008). 207 (B) Schematic representation of the BC2L-C hexamer with Nterminal domains (blue) and C-terminal domains (green) in two models. Both the domains are connected through a peptide linker. Adapted from Šulák and co-workers (2011).[START_REF] Sulák | Burkholderia cenocepacia BC2L-C is a super lectin with dual specificity and proinflammatory activity[END_REF]

Figure 1 . 13

 113 Figure 1.13 Structural similarities between LecB, BC2L-A and C-terminal domain of BC2L-C. (A) LecB from P. aeruginosa (PDB code 1GZT); (B) BC2L-A (PDB code 2VNV) lectin from B. cenocepacia and (C) the apo form of the C-terminal domain of BC2L-C (PDB code 2XR4) from B. cenocepacia. LecB forms a tetramer while BC2L-A and the C terminal domain of BC2L-C are arranged as dimers.

  BC2L-C is therefore a superlectin that binds independently to mannose/heptose glycoconjugates and fucosylated human histo-blood group epitopes. In the same study, the first crystal structure of BC2L-C-nt (PDB ID: 2WQ4) complexed with methylseleno-α-Lfucopyranoside (MeSe-α-L-Fuc) has been reported at 1.42 Å which confirmed the trimeric arrangement of the lectin (Figure1.14). The structure of BC2L-C-nt domain revealed a compact jellyroll architecture composed of 11 β strands and a short helix. The β strands show

Figure 1 . 14

 114 Figure 1.14 Trimeric arrangement and fucoside binding site of the N-terminal domain of BC2L-C (PDB code 2WQ4). (A) Side and (B) top view of the crystal structure (homotrimer) of the N-terminal domain of BC2L-C. (C) The H-bond interactions with the ligand (methylseleno-α-L-fucopyranoside) and water molecules (depicted as red spheres) are shown as black lines. Adapted from Šulák and co-workers (2010).[START_REF] Sulák | A TNF-like trimeric lectin domain from Burkholderia cenocepacia with specificity for fucosylated human histo-blood group antigens[END_REF]

BFigure 1 .

 1 Figure 1.15 (A) Model of the BC2L-C hexamer obtained by best manual fit of the different domains in the ab-initio SAXS envelop. 199 The N-and C-terminal domains are shown in blue and green, respectively. (B) Schematic representation of BC2L-C hexamer showing its role in crosslinking bacteria surface and host epithelial cells.

PhD4GlycoDrug-

  Marie Skłodowska-Curie Innovative Training Network (MSCA ITN); a European project, funded by the European Union's Horizon 2020 research and innovation programme. 224 The PhD4GlycoDrug consortium (Figure 1.16) involves academic and nonacademic partners including a research institute to ensure quality training in glyco-drug discovery and development. The project aims to pursue the research on the development of carbohydrate-based therapeutic molecules (glycodrugs) for the different targets. Hence, using the methods under structural biology, molecular modelling, organic synthesis and medicinal chemistry, pathogenic lectins have been characterized and targeted for antagonist design. In addition, the research work under the consortium was performed in collaboration

Figure 1 . 226 1. 8 References[ 1 ][ 2 ]

 1226812 Figure 1.16 The PhD4GlycoDrug consortium.



  Prediction of druggable sites using SiteMap  Virtual screening: Glide docking  MD simulations  Grid Inhomogeneous Solvation Theory (GIST) to study water thermodynamics Experimental (biophysical) techniques to validate the fragment/ligand-protein interactions: Production of BC2L-C-nt  Thermal shift assay (TSA) Microscale thermophoresis (MST)  Saturation Transfer Difference (STD) -NMR  X-ray crystallography  Isothermal titration calorimetry (ITC)

2 𝒂 2 [ 58 2. 4 . 6

 225846 ).58 𝒓(𝑡 + 𝑑𝑡) = 𝒓(𝑡) + 𝒗(𝑡)𝑑𝑡 + 1 𝒂(𝑡) + 𝒂(𝑡 + 𝑑𝑡]𝑑𝑡 (2.17) Similarly, more sophisticated algorithms include additional terms. Few examples of integration algorithm include leap-frog algorithm, Beeman's algorithm. Constant temperature dynamics Molecular dynamics is naturally performed in the constant microcanonical (NVE) ensemble because Newtonian mechanics conserve the energy (E). To perform molecular dynamics (MD) in the canonical ensemble (NVT) or Isobaric-Isothermal Ensemble (NPT), a

Figure 2 . 1

 21 Figure 2.1 GIST calculates different thermodynamic properties of water using a grid-based approach (right panel) near the binding site region of protein or solute (green). The calculations are based on the molecular dynamics simulation results (left panel). Adapted from Ramsey and co-workers (2016).75

Figure 2 . 2

 22 Figure 2.2 pCold TF-TEV Plasmid which is used for cloning and expression of BC2L-C-nt.

Figure 2 . 3

 23 Figure 2.3 Principle of thermal shift assays (thermofluor). Fluorescent dye (SYPRO Orange) can be used to monitor the thermal denaturation of protein. The melting temperature (Tm) provides important information about the protein's thermal stability. Retrieved from https://www.biotrend.com/en/other-products-186/glomelt-thermal-shift-protein-stability-514058784.html

Figure 2 . 4

 24 Figure 2.4 Principle of the MST and Experimental setup. (A) MST is performed using a capillary with a small volume (4 μL) of sample. An IR-Laser is used to heat the sample volume which is detected as fluorescence. (B) MST signal for a given capillary Initially shows the homogeneous distribution of molecules which can be measured as constant "initial fluorescence". Once IR-Laser is turned on, atemperature-jump is observed, followed by thermophoresis of molecules. Thereafter, a decrease in fluorescence is detected for about 30 seconds. Next, when the IR-Laser is turned off, a reversed temperature-jump is observed. Finally, "backdiffusion" of molecules is detected, which is driven by mass diffusion and gives information on the molecule size. Adapted from Liu and co-workers (2015).95

Figure 2 . 5

 25 Figure 2.5 STD-NMR experiment. When the protein is selectively irradiated, the bound ligand receives the magnetization from protein: only the protons in intimate contact with receptor site show an STD signal in the NMR experiment. The differences between STD and reference (proton) spectra allow to map the interaction.

Figure 2 . 6

 26 Figure 2.6 Protein crystallization phase diagram. Supersaturated solution is required for the formation of crystals. Nucleation zone supports the nucleation while the crystal growth occurs in the metastable zone. When a protein is undersaturated, none of the crystallization steps occur. Adapted from Bijelic and co-workers (2018).106

Figure 2 . 7

 27 Figure 2.7 Vapor diffusion based crystallization techniques. Setup for hanging drop (A) and sitting drop (B) methods. The drop is placed over the slide which consists of the precipitant (light blue) and the protein solution (red). Finally, the wells are sealed to allow equilibration via vapor diffusion (dark blue arrows). Adapted from Bijelic and co-workers (2018).106

Figure 2 . 8

 28 Figure 2.8 Assembly of a crystal structure (shown in 2D space). The asymmetric unit consists of one protein (green) molecule used to generated the unit cell by producing a symmetry mate (red molecule). A whole crystal can be built by translationally stacking up unit cells (shown in 2D space for clarity but exist as 3D in the real crystal). Adapted from Bijalic and co-workers (2018).106

AFigure 2 . 9

 29 Figure 2.9 (A) Basic setup of an X-ray diffraction experiment. An incident X-ray beam after passing the crystal produces a diffraction pattern in the forms of dots and recorded on a detector. (B) A constructive interference is only observed when the path difference (d sinθ) between the waves (red dotted waves) is an integer multiple of the wavelength λ (green solid wave). Thus, the amplitudes of the diffracted waves add up to generate a signal on the detector. The two Bragg planes are shown as grey bars with an interplanar distance d and lattice points (blue dots), which represent atoms of the protein. Adapted from Bijalic and co-workers (2018).106

Figure 2 . 10

 210 Figure 2.10 Schematic representation of the isothermal titration calorimeter (left) and annotated thermogram (upper right) with its evaluation (lower right). Adapted from Song and co-workers (2015).118

. 1 )

 1 . The three sugar binding sites are located at the interface between neighbouring protomers (A, B, C), and separated by a distance of ~20 Å (Figure 3.1). Further analysis shows that the binding mode for the fucose ring is identical in the three binding sites: the key residues Tyr48, Ser82, Thr83, Arg85 from one chain (e.g. chain A) and Tyr58, Thr74, Tyr75, Arg111 from the neighbouring chain (e.g. chain C) play an important role in ligand binding (Figure 3.2). In addition, two water molecules, W1 and W2, bridge the sugar and the protein. Both water molecules are conserved in the available X-ray structures of BC2L-C N-terminal domain in complex with fucoside and fucosylated

Figure 3 . 1

 31 Figure 3.1 Crystal structure of BC2L-C N-terminal domain (PDB 2WQ4) showing three identical fucoside binding sites (top view) at the interface of monomers.

Figure 3 . 2

 32 Figure 3.2 Binding site in BC2L-C-nt showing key residues and water molecules (W1 and W2) involved in fucose binding. Hydrogen bonds are represented as dashed lines.

Figure 3 . 3 (Figure 3 . 3 ). 4 .

 33334 Figure 3.3 Crystals of apo form of BC2L-C-nt were obtained as stacked cluster.

Figure 3 . 4

 34 Figure 3.4 Superimposition of crystal structures of the apo (orange, PDB 7BFY) and holo forms (cyan PDB 2WQ4, azure PDB 6TIG) of BC2L-C N-terminal domain. The binding site residues do not display any significant difference.

Figure 3 . 5

 35 Figure 3.5 (A) Overview of the surface loops in the C-terminus of BC2L-C-nt. (B) Superimposition of different structures in holo (cyan, 2WQ4 and azure, 6TIG) and apo form (orange, 7BFY) shows different orientation of the residues in the C-terminus. (C) Only minor differences were observed in a small loop (Asn52-Phe54) located near the fucose binding site.

Figure 3 . 6

 36 Figure 3.6 Comparison of binding site surfaces in holo and apo form of BC2L-C-nt. The additional pockets exist in both forms and do not exhibit any significant difference.

Figure 3 . 7

 37 Figure 3.7 Identification of additional regions (site points) near fucoside binding site suitable for small fragment binding.

Figure 3 . 8

 38 Figure 3.8 Schematic representation of the virtual screening protocol.

Figure 3 . 9

 39 Figure 3.9 Predicted binding pose (at the site X) for the top ranked fragments (KL1-KL12) using docking studies. The key residues involved in the fragment binding are shown in the docking pose of KL1.

Figure 3 . 10

 310 Figure 3.10 Schematic of the protocol used for hit expansion.

Figure. 4 . 1

 41 Figure. 4.1 Schematic of the expression construct of BC2L-C-nt in pCold-TEV.

Figure 4 . 2

 42 Figure 4.2 Purification of BC2L-C-nt. First, all proteins with no affinity to IMAC column were discarded.Then, imidazole gradient allowed elution of his-tag protein by disrupting the nickel/protein interactions.

Figure 4 . 3

 43 Figure 4.3 Purification of BC2L-C-nt after TEV cleavage on IMAC column.

Figure 4 . 5 Figure 4 . 4

 4544 Figure 4.5 (A) 10 % SDS-PAGE showing partial cutting of histidine tag in wells 3, 4 and 5. Other wells (1 and 2) correspond to uncut protein fraction. (B) A pure fraction of BC2L-C-nt was collected (wells 1, 2 and 3) after SEC.

Figure 4 . 7

 47 Figure 4.7 First derivatives of fluorescence curves of BC2L-C-nt (5 µM) in the presence of the fragments (KL1-12, 2.5 mM). The fragments induce a negative shift in the melting temperature (Tm) of BC2L-C-nt which indicates ligand interaction.

Figure 4 . 6

 46 Figure 4.6 (A) The melting curves were obtained for BC2L-C-nt at 5 µM in the presence (red) and absence (black) of 20 mM Me-α-L-Fuc. (B) First derivatives of fluorescence (melting) curves.

Figure 4 . 8 )

 48 upon fragment binding/interactions. The results from both the experiments indicate that fragments interact with the target protein. However other biophysical methods are necessary to obtain more insights on the interactions.

Figure 4 . 8

 48 Figure 4.8 First derivatives of fluorescence curves of BC2L-C-nt (5 µM) in the presence of the fragments (KL1-12, 2.5 mM) and the absence of Me-α-L-Fuc. The fragments induce a negative shift in the melting temperature (Tm) of the protein which indicates ligand interaction.

. 9 )

 9 . It is likely that the modification of surface lysines by the fluorescein isothiocyanate (FITC) dye interferes with the fucose binding as one of the lysine residues (Lys78) was in the proximity (~11.20 Å) to the fucose binding site. The MST experiment at 2.5 mM concentration was repeated for all the fragments in the presence (Figure 4.10 A, B) and the absence (Figure 4.10 C, D) of Meα-L-Fuc to identify the fragments with binding affinity for the BC2L-C-nt. The resulting graphs (Figure 4.10) from the MST experiments show a change in fluorescence pattern when different fragments were tested for their binding to BC2L-C-nt. Thus, both the screening experiments indicate interaction of the fragments to the protein domain. However, it is also likely that the fluorescein isothiocyanate (FITC) dye might have an influence on the

Figure 4 . 9 .

 49 Figure 4.9. Fluorescence signal of BC2L-C-nt-FITC for different dilutions (50 mM to 0.0015 mM) of Me-α-L-Fuc.

Figure 4 .

 4 Figure 4.10 Fluorescence (binding) signals of BC2L-C-nt (A) for the fragments KL1-7 (2.5 mM) and (B) the fragments KL8-12 (2.5 mM) in the presence of Me-α-L-Fuc (326 nM). The experiment was repeated for (A) the fragments KL1-7 and (D) KL8-12 in the absence of Me-α-L-Fuc.

Figure 4 .Figure 4 .

 44 Figure 4.11 (A) 1 H-NMR and (B) STD spectrum of Me-α-L-Fuc in the presence of BC2L-C-nt (1000:1). The red circles represent the signals of the fucose ring (at 3.7 ppm ) and of the methyl group carries by carbon C5 (at 1.1 ppm). The spectrum was recorded with a Bruker Avance 600 MHz spectrometer at 298K with irradiating frequency -0.05 ppm.

Figure 4 .

 4 Figure 4.14 A) 1 H-NMR and B) STD spectrum of fragment (KL9) and Me-α-L-Fuc in the presence of BC2L-C-nt (1000:1) at -0.05 ppm irradiation frequency. The signals produced by the fragment are highlighted with green circles for -CH2-Ph (at 2.9 ppm) and aromatic protons (at 7.3 ppm). The red circles highlight the signals of fucose ring and its methyl group at 3.7 ppm and 1.1 ppm, respectively.The spectrum was recorded at 298 K with a Bruker Avance 600 MHz spectrometer.

Figure 4 .

 4 Figure 4.15 A, B). These observations suggest that the fragments bind in the proximity of

Figure 4 .

 4 Figure 4.15 The STD spectra of fragment KL8 (A) and KL9 (B) were recorded in the presence of BC2L-C-nt and Me-α-L-Fuc (at 10 ppm irradiating frequency). The signals generated only for the aromatic protons of the fragments are highlighted in green circles. The spectra were recorded at 298 K with a Bruker Avance 600 MHz spectrometer.

Figure 4 . 16

 416 Figure 4.16 Docking pose of KL3 shows that methyl group binds near an aromatic residue (Tyr58) located in a protein binding pocket. The STD spectrum of KL3 (at 10 ppm irradiation frequency) also shows a clear signal for the methyl group of the fragment.

Figure 4 . 17

 417 Figure 4.17 Crystals of BC2L-C-nt were obtained as clusters of plates.

  e The R-factors Rwork and Rfree are calculated as follows: R = (| Fobs -Fcalc |)/| Fobs |, where Fobs and Fcalc are the observed and calculated structure factor amplitudes, respectively f refers to ligands bound in the active site and potential surface binding sites The analysis shows that the residue Tyr58 forms π-π stacking (T-shaped) interactions with the benzene ring of the ligand while Asp70 establishes salt bridge interactions with the amino group in the ligand. In addition, the free nitrogen of the imidazole ring of the fragment forms water mediated H-bond with the side chain of Arg85 and the OH-4 of the GlcNAc moiety of Globo H (Figure 4.18). The orientation of the fragment and the key interactions with the protein correspond very well with the predicted binding pose using docking studies (Figure 4.18 and Figure 4.19)

Figure 4 . 18

 418 Figure 4.18 Crystal structure of BC2L-C-nt with Globo H and the fragment (KL3). (A) Enlarged viewd of the binding site of BC2L-C-nt with 2Fo-DFc electron density represented at 1σ (B) Network of key interactions observed in the binding site (site X). Analysis shows that the key interactions and residues predicted from docking studies were involved in the fragment (KL3) binding. The complex shows π-π stacking interactions with Tyr58 and salt bridge interactions between Asp70 (side chain) and benzylamino group of the fragment. In addition, water molecules (other than W1 and W2) form bridging H-bond interactions with the fragment and the protein. Hydrophobic and H-bonding interactions are displayed in green and black dashed lines respectively. π-π stacking interactions are highlighted with blue dashed lines.

Figure 4 . 19

 419 Figure 4.19 Comparison of binding pose of docked (grey) and crystallized complex (green) with KL3 (RMSD 0.4 Å).

Figure 4 . 20

 420 Figure 4.20 Isothermal microcalorimetry for the affinity analysis of KL3. (A) Titration of buffer by fragment (KL3, 15 mM) (B) Titration of BC2L-C-nt (225 µM) by fragment (KL3, 15 mM) at 25 °C. (C) Finalcurve obtained after integration of peaks and point-by-point differences between fragment-in-buffer and fragment-in-protein for KL3. The fitting of curve was done using the "one binding site" model.

(

  KL3) were dissolved in the same buffer composed of 100 mM Tris HCl pH 7.0 and 100 mM NaCl. A total of 38 injections of 1 µL of ligand solution (15 mM) were added at intervals of 200 s while stirring at 850 rpm was maintained to ensure proper mixing in the 200 µL sample cell containing the protein, at 225 µM. A control experiment was performed by injecting same concentration of KL3 in buffer. The subtraction of control for integrated peaks was performed using the Microcal PEAQ-ITC analysis software. The binding thermodynamics was further processed with a "one set of sites" fitting model. The experiment determined experiment affinity (Kd), binding enthalpy (∆H) while the stoichiometry was fixed to 1. Free energy change (ΔG) and entropy contributions (TΔS) were derived from the equation ΔG = ΔH -TΔS. The experiments were performed in duplicates and the standard deviation was in 20% range for Kd.

Figure 5 . 1

 51 Figure 5.1 Strategies to link the fucose core to the selected fragments.

  7

Figure 5 . 2 illustrates

 52 the schematic representation of PyRED workflow.

Figure 5 . 2

 52 Figure 5.2 Schematic representation of PyRED workflow.

FiFigure 5 . 3 A

 53 used to calculate the occupancy (average occupancy) of H-bonds between the individual residue pairs of the ligand and the receptor. The hydrogen bonds involving the key residues displayed an overall occupancy of 85 to 100 percent in all the three sites (Figure 5.3 and 5.4). Hydrogen bonding interactions between fucoside and side chains of Arg85 (chain A) and Arg111 (chain C) were stabilizing the fucoside at the interface between monomers. Similarly, H-bond interactions between backbone atom (O) of Thr83 (chain A) and side chain of Thr74 representative snapshot from MD simulation trajectory of BC2L-C-nt and fucoside complex showing key H-bond interactions. Hydrogen bonds are represented as dashed lines.

Figure 5 . 4

 54 Figure 5.4 (A) Hydrogen bond interaction pattern (showing percentage occupancy) obtained from the MD simulation of BC2L-C-nt and fucoside complex. AB, BC and AB are the binding site interfaces of the monomers in BC2L-C-nt trimer. The interactions with arginine shown (twice) in the plot involve atoms of -NH1 and -NH2 groups of the side chain of the residue. (B) The RMSD calculation for the ligand (fucoside) binding at the interface of chain A and C. The results show a small value of RMSD which demonstrates that the ligand maintains key interactions in the binding site.

Figure 5 . 6 A

 56 Figure 5.6 A representative snapshot from the MD simulations of Lfuc-Aky-KL08 in complex with BC2L-C-nt showing key residues involved in ligand binding. The H bond interactions are shown in dashed lines. (B) Hydrogen bond interaction pattern (showing percentage occupancy) obtained from the MD simulation of Lfuc-Aky-KL08 in complex with BC2L-C-nt.

Figure 5 . 7 A

 57 Figure 5.7 A representative snapshot from the MD simulations of Lfuc-Amd-KL13 in complex with BC2L-C-nt showing key residues involved in ligand binding. Most of the binding poses in trajectory maintain Asp70(OD)-KL13(NH2) interactions. Other H-bond interactions were observed in less than 10 percent of sampled structures.

Figure 5 .

 5 Figure 5.11 (A) Docking pose of Glycomimetic ligand (Lfuc-Aky-KL07g) with guanidine moiety. As compared to existing ligand with benzylamine moiety, this ligand shows (B) bidentate H-bonding interactions with two oxygen atoms of the side chain of Asp70.

[ 1 ][ 3 ][ 4 ]Figure 5 .

 1345 Figure 5.12 (A) Docking pose of glycomimetic ligand (Lfuc-Aky-KL08g) with guanidine moiety. (B) The ligand establishes bidentate H-bonding interactions through guanidine moiety.

Figure 3

 3 Figure 3 RMSD analysis of (A) backbone atoms of BC2L-C-nt trimer in complex with Lfuc-Aky-KL08. RMSD analysis was performed for (B) the glycomimetic ligand and its (C) sugar and (D) non-sugar part to investigate the stability of the ligand in the binding site. The results indicate a low value of RMSD (0.5 Å) for the sugar part of the ligand while the non-sugar part displayed a higher value of RMSD probably due to the presence of additional methyl groups in the ligand. The additional groups enforce the non-sugar part toward Asp118 and thus this part establishes H-bonding interactions between -NH3+ of the ligand and the backbone oxygen atom in the residue (Asp118).

Fi Figure 9 . 2 Figure 9 . 1 Figure 9 . 3

 929193 Figure 9.1 (A) Crystal structure BambL (PDB 3ZW0) with druggable regions (site points) predicted using SiteMap. (B) Enlarged view of one of the binding sites.

Figure 9 . 5

 95 Figure 9.5 Binding pose of the ligands (A) 16A02, (B) 15B05 and (C) 14H04 predicted by docking (XP) studies.

Figure 9 . 6

 96 Figure 9.6 Key residues involved in the interaction with the molecules (A) 16A02, (B) 15B05 and (C) 14H04 predicted by XP docking.

Table 1 .

 1 1 A brief history of antibiotics and resistance. Adapted from the U.S. Centers for Disease Control and Prevention.4

	Antibiotic	Year released	Resistant strain	Year
				identified
	Penicillin	1941	Penicillin-resistant Staphylococcus aureus
			Penicillin-resistant Streptococcus
			pneumoniae
			Penicillinase-producing Neisseria
			gonorrhoeae
	Vancomycin	1958	Plasmid-mediated vancomycin-resistant
			Enterococcus faecium
			Vancomycin-resistant Staphylococcus
			aureus
	Amphotericin B	1959	Amphotericin B-resistant Candida auris
	Methicillin	1960	Methicillin-resistant Staphylococcus aureus
	Extended-spectrum	1980	Extended-spectrum beta-lactamase-
	cephalosporins	(Cefotaxime)	producing Escherichia coli
	Azithromycin	1980	Azithromycin-resistant Neisseria
			gonorrhoeae
	Imipenem	1985	Klebsiella pneumoniae carbapenemase
			(KPC)-producing Klebsiella pneumoniae
	Ciprofloxacin	1987	Ciprofloxacin-resistant Neisseria
			gonorrhoeae
	Fluconazole	1990 (FDA	Fluconazole-resistant Candida
		approved)
	Caspofungin	2001	Caspofungin-resistant Candida
	Daptomycin	2003	Daptomycin-resistant methicillin-resistant
			Staphylococcus aureus
	Ceftazidime-	2015	Ceftazidime-avibactam-resistant KPC-
	avibactam		producing Klebsiella pneumoniae

  80

). In another study, a molecule with nanomolar binding affinity, designed using glycoclusters functionalized with

	galactosides demonstrated almost complete protection against P. aeruginosa in a lung
	infection model in mice, thus providing a promising drug candidate. 184 Compared to LecB,
	LecA has been proven a more challenging target for the development of high-affinity ligands
	and was reported to have large koff for LecA-ligand interactions. Therefore, a covalent
	inhibitor (3, Figure 1.11) targeting a nearby cysteine (Cys62) was designed to circumvent
	the inherently weak affinity caused by a short lifetime of lectin-ligand complexes. 149

Table 1 .2 Binding

 1

	Ligand	Terminal epitope	Affinity	Reference
			(Kd) in µM
		BC2L-C-ct		Šulák and co-
	D-Man αMeMan	Man Man	37.4 27.6	workers (2011)
	Trimannose	Manα1-3(Manα1-6)Man	28.8
	αMeHept	L, D-manHep	236
	Diheptose	L,D-manHepα1-3L,D-manHep.	88.1
		BC2L-C-nt
	αMe-L-Fuc	Fuc	2700

affinity of monosaccharides and oligosaccharides for the two domains of BC2L-C, measured by ITC. Adapted from Šulák and co-workers (2010 and 2011).

[START_REF] Sulák | Burkholderia cenocepacia BC2L-C is a super lectin with dual specificity and proinflammatory activity[END_REF][START_REF] Sulák | A TNF-like trimeric lectin domain from Burkholderia cenocepacia with specificity for fucosylated human histo-blood group antigens[END_REF]

  For instance the Verlet algorithm uses position and acceleration at time t and positions from time t-dt to calculate new positions at time t+dt

	𝒓(𝑡 -𝑑𝑡) = 𝒓(𝑡) -𝒗(𝑡)𝑑𝑡 +	1 2	𝒂(𝑡)𝑑𝑡 2	(2.14)
	combining equations 2.13 and 2.14, the following equation can be obtained:
	𝒓(𝑡 + 𝑑𝑡) = 2𝒓(𝑡) -𝒓(𝑡 -𝑑𝑡) + 𝒂(𝑡)𝑑𝑡 2	(2.15)
	(equation 2.13, 2.14). 58
	𝒓(𝑡 + 𝑑𝑡) = 𝒓(𝑡) + 𝒗(𝑡)𝑑𝑡 +	1 2 𝒂(𝑡)𝑑𝑡 2	(2.13)

  ∆𝐸 𝑡𝑜𝑡𝑎𝑙 (𝒓 𝑗 ) = ∆𝐸 𝑠𝑤 (𝒓 𝑗 ) + ∆𝐸 𝑊𝑊 (𝒓 𝑗 )

		(2.30)
	∆𝑆 𝑠𝑤 𝑡𝑜𝑡𝑎𝑙 (𝒓 𝑗 ) = ∆𝑆 𝑠𝑤 𝑡𝑟𝑎𝑛𝑠 (𝒓 𝑗 ) + ∆𝑆 𝑠𝑤 𝑜𝑟𝑖𝑒𝑛𝑡 (𝒓 𝑗 )	(2.31)
	where r may be defined as the location of a water oxygen relative to the solute, T is absolute
	temperature. The total solute-water entropy (∆𝑆 𝑠𝑤 𝑡𝑜𝑡𝑎𝑙 ) is derived using translational (∆𝑆 𝑠𝑤 𝑡𝑟𝑎𝑛𝑠 )
	and orientational (∆𝑆 𝑠𝑤 𝑜𝑟𝑖𝑒𝑛𝑡 ) solute-water entropy terms. Likewise, for solvation energy
	(∆Etotal), ∆Esw and ∆Eww are the solute-water and water-water terms, respectively. Finally, the
	normalized (per water) free energy (∆G R,norm ) of region R can be derived as
	∆𝐺 𝑅,𝑛𝑜𝑟𝑚 = ∆𝐸 𝑠𝑤 𝑅,𝑛𝑜𝑟𝑚 + ∆𝐸 𝑤𝑤 𝑅,𝑛𝑜𝑟𝑚 -𝑇∆𝑆 𝑠𝑤 𝑅,𝑡𝑟𝑎𝑛𝑠,𝑛𝑜𝑟𝑚 -𝑇∆𝑆 𝑠𝑤 𝑅,𝑜𝑟𝑖𝑒𝑛𝑡,𝑛𝑜𝑟𝑚

Table 3 .

 3 1 X-ray data collection and refinement for the apo form of BC2L-C-nt. CC½ is the correlation coefficient between symmetry-related intensities taken from random halves of the dataset. d The data set was split into "working" and "free" sets consisting of 95 and 5% of the data, respectively. The free set was not used for refinement.

	Data set	BC2L-C-nt apo form
	Data Collection
	PDB code	7BFY
	Beamline	PROXIMA1 (SOLEIL)
	Wavelength (Å)	0.9801
	Space Group	P63
	a, b, c (Å)	42.99, 42.99, 94.68
	α, β, γ (°)	90.0, 90.0, 120.0
	Resolution (Å) a	19.97-1.50 (1.53-1.50)
	Total observations	261006
	Unique reflections	15915
	Multiplicity a	16.4 (15.5)
	Mean I/(I) a	21.7 (5.9)
	Completeness (%) a	99.9 (100)
	Rmerge a,b	0.081 (0.445)
	Rpim	0.030 (0.274)
	CC½ a,c	0.999 (0.935)
	Refinement
	Reflections: working/free d	15120 / 762
	Rwork/ Rfree e	0.149 / 0.178
	Ramachandran plot: allowed/favoured/outliers (%)	100 / 97 / 0
	R.m.s. bond deviations (Å)	0.014
	R.m.s. angle deviations ()	1.855
	R.m.s. chiral deviations	0.085
	No. atoms / Mean B-factors (Å 2 ) Protein carbohydrate ligand ligand f water	Chain A 952 / 15.6 --150 / 28.2

a Values for the outer resolution shell are given in parentheses. b Rmerge = ∑hkl ∑i |Ii(hkl)  I(hkl)|/ ∑hkl ∑iIi(hkl). c e The R-factors Rwork and Rfree are calculated as follows: R = (| Fobs -Fcalc |)/| Fobs |, where Fobs and Fcalc are the observed and calculated structure factor amplitudes, respectively f refers to ligands bound in the active site and potential surface binding sites.

  11

Table 3 . 2

 32 Top ranked fragments identified for site X.

	Fragment name	Structure	Molecular weight	Aqueous Solubility(mM)
	KL1		205.3	20
	KL2		187.2	50 [b]
	KL3		187.2	100
	KL4		193.2	100
	KL5		177.2	1 [a]
	KL6		187.2	1 [a]
	KL7		155.6	20
	KL8		149.2	50

Table 3 . 3

 33 Molecules with improved binding affinity (docking score) obtained from similarity search. Docking scores (from XP docking, kcal/mol) are mentioned below the molecule. Fragments KL1-KL8 were docked using two waters model while the remaining fragments were docked using one water model. Only those query fragments which identified new fragments with improved binding affinity are shown in the table.

Table 4 .

 4 1 X-ray data collection and refinement for the BC2L-C-nt complex with Globo H and fragment KL3. Values for the outer resolution shell are given in parentheses.

	Data set	BC2L-C-nt complex with KL3 and Globo H
	PDB code		6ZZW
	Data collection
	Beamline	PROXIMA1 (SOLEIL)
	Wavelength (Å)		0.9786
	Space Group		C2
	a, b, c (Å)	74.46, 42.91, 103.34
	α, β, γ (°)	90.0, 96.10, 90.0
	Resolution (Å) a	37.13-1.90 (1.94-1.90)
	Total observations		175918
	Unique reflections		25522
	Multiplicity a		6.9 (7.1)
	Mean I/(I) a		10.2(4.0)
	Completeness (%) a		98.8 (98.1)
	Rmerge a,b		0.13 (0.53)
	Rpim
	CC½ a,c		0.99 (0.89)
	Refinement
	Reflections: working/free d		24288 / 1224
	Rwork/ Rfree e		0.181 / 0.238
	Ramachandran plot: allowed/favoured/outliers (%)		100 / 97 / 0
	R.m.s. bond deviations (Å)		0.014
	R.m.s. angle deviations ()		1.840
	R.m.s. chiral deviations		0.093
	No. atoms / Mean B-factors (Å 2 ) Protein carbohydrate ligand ligand f water	Chain A 962 / 22.1 58 / 36.1 14 /32.0 69 / 26.9	Chain B 971 / 22.2 58 / 33.3 14 / 38.7 65 / 26.6	Chain C 951 / 21.3 47 / 30.6 14 / 36.0 72 / 24.9

a

Protein or water atom Distance (Å)

	N3	Asp70 (OD2) W3 (HOH161) [a]	3.20 2.75 ± 0.15
	N1	Arg85 (NH2) W4 (HOH108)	3.30 ± 0.07 2.46 ± 0.05
		Ser119 (CB)	3.60 ± 0.04
	C [b]	Tyr58 (CE1)	3.50 ± 0.07

Table 5 . 1

 51 List of the fragments with carboxyl and amide moieties identified from PubChem similarity (95 percent) search using COOH functionalized fragments KL1 and KL2 (see

Table 3 .2, Chapter 3) as query

 3 molecules. Fragments 1-15 are similar to the fragment KL2 and the fragments 16-20 are similar to the fragment KL1.

	S.No.	PubChem ID	Structure
		Query molecule
	1	117402114

Table 5 . 2

 52 Docking studies of glycomimetic ligands designed using best fragments from two water model. These ligands were prioritized for synthesis in collaboration with Rafael Bermeo. The last column shows the results from experimental studies performed by Rafael Bermeo to calculate the

	binding affinity.
	S.NO. Molecule name Structure	GlideScore (kcal/mol)	Binding
					affinity
			XP	SP	(Kd)
	1	Lfuc-Aky-KL07	-9.6	-7.3	7.85 mM
					(SPR)
					1.24 mM
					( ITC)
	2	Lfuc-Amd-KL07	-9.7	-7.1	1.57 mM
					(SPR)
					3.66 mM
					(ITC)
	3	Lfuc-Trz-KL07	-10.0	-6.9	2.45mM
					(SPR)
					6.25mM
					(ITC)

Table 5 .

 5

		3 Docking studies of glycomimetic ligands using one water model (KL9-KL12). These molecules
	can be synthesized in future.
	S.No. Molecule name	Structure	GlideScore (kcal/mol)
				XP	SP
	1	Lfuc-Aky-KL09-		-8.9	-6.4
		RR
	2	Lfuc-Aky-KL09-		-9.5	-7.1
		SR
	3	Lfuc-Aky-KL10-		-9.2	-5.9
		RR
	4	Lfuc-Aky-KL10-		-9.8	-7.3
		SR
	5	Lfuc-Aky-KL11-		-9.1	-6.0
		RR
	6	Lfuc-Aky-KL11-		-9.0	-7.7
		SR

Table 5 . 4

 54 Docking studies of the ligands derived from -COOH functionalized fragments (see Table5.1).

	The fragments were obtained from similarity search (using KL1 and KL2) in PubChem database and
	connected to the fucoside using an amide linker.
	S.No.	PubChem ID	Structure	GlideScore(kcal/mol)
		of the fragment		XP	SP
	1	117402114		-10.18	-7.10
	2	91662832		-10.31	-7.54
	3	83395866		-10.60	-7.15
	4	2771732		-9.86	-7.15
	5	83410863		-10.96	-7.51
	6	91667363		-9.94	-7.15

Table 5 . 6 .

 56 Thermodynamic properties (in kcal/mol) of the three hydration sites near ligand binding site.

	Site	∆Gsolv	∆Hsolv	T∆Ssolv	Occupancy (%)
	A	-4.24	-10.86	-6.62	99.22
	B	-1.42	-3.07	-1.65	56.10
	C	-1.41	-4.81	-3.40	87.70

Table 5 . 7

 57 Docking studies of the glycomimetic ligands with guanidine moiety.

	Molecule name	Structure	GlideScore (kcal/mol)
			XP	SP
	Lfuc-Aky-KL07g		-11.1	-6.8
	Lfuc-Aky-KL08g		-11.6	-6.5

Table 2

 2 Docking studies and classification of the glycomimetic ligands designed using the fragments with heterocyclic rings (see Table1) and amide linker.

	Subclass	Fragment	Structure	GlideScore
		No.		XP	SP
		25		-10.22	-7.20
	Fragment 25	(query
	and analog	molecule
	(Benzothio-	from
	phene)	table 1)
		23		-10.44	-7.26
		15		-10.08	-7.29

Table 3

 3 PyRED derived charges for atoms in the non-sugar part of the glycomimetic ligands (A)Lfuc-Aky-KL07 (B) Lfuc-Aky-KL07 and (C) Lfuc-Amd-KL13.

	C
	Atom	Atom	Charge
	name	type
	C1	CW	0.2140
	C2	CD	-0.3058
	H6	HA	0.1751
	C3	CD	-0.0385
	H7	HA	0.1525
	C4	CA	-0.0233
	O1	OS	-0.1749
	C5	CW	-0.1052
	C6	CA	-0.2371
	H1	HA	0.1666
	C7	CA	0.3150
	N1	N2	-0.8711
	H9	H	0.3680
	H10	H	0.3680
	C8	CA	-0.0859
	H8	HA	0.1023
	C9	CA	-0.2157
	H2	HA	0.1702
	C10	CA	-0.2044
	H3	HA	0.1576
	N2	N	-0.1570
	H5	H	0.2367
	C12	C	0.5436
	O2	O	-0.5507

The spectra for fragment KL3 at -0.05 ppm shows the enhanced signal for aromatic protons and -CH2-group of the fragment. Likewise, fragment KL8 displayed the signals for -CH2-NH2 and aromatic protons while the fragment KL9 exhibited the STD signals for -CH2-Ph and aromatic protons at -0.05 ppm irradiation. For all the fragments, interaction with BC2L-C-nt was observed in the presence of Me-α-L-Fuc which confirmed the binding of the top scoring fragments from both the models. This further confirmed that interaction of the fragments

Protein expression and purification

The construct of BC2L-C-nt was designed as 132 amino acids-long with a cleavable histidine tag. The sequence coding for the 132 first amino acids of BC2L-C-nt amplified via PCR was inserted into pCold-TEV by Rafael Bermeo. The protein expression and purification was performed using the aforementioned vector. The vector was transformed by heat shock into Escherichia coli BL21 Star (DE3) cells. The bacterial cells with the plasmid were then cultured in Luria Broth (LB) medium containing 100 μg/mL ampicillin under constant shaking (170 rpm) at 37 °C. The temperature was decreased to 16 °C when the value of optical density of the culture (OD600nm) reached 0.4. When OD600nm reached 0.7, 0.1 mM isopropyl β-d-1thiogalactopyranoside (IPTG) was added to induce the overnight protein expression. The cells were then centrifuged at room temperature for 5 minutes at 5000 x g and the pellets were obtained. These pallets can be used further or stored at -20 °C.

In the next step, wet cell pellets were resuspended in 5 mL of Buffer 1 (Tris-HCl 50 mM, NaCl 100 mM, pH 8.5) followed by treatment with DENARASE® endonuclease (c-LEcta GMBH, Leipzig, Germany) for a short duration (10 minutes) at room temperature while placed on a rotating wheel. The suspended cells were lysed by applying a pressure at 1.9 MPa in a one-shot table-top cell disruptor (Constant Systems Ltd., UK). The resulted lysate was centrifuged for 30 minutes at 24,000× g and 4 °C temperature. The supernatant was filtered through a 0.45 μm polyethersulfone (PES) syringe filter. The HisTrap™ fast flow (FF) 5ml column (GE Heathcare Life Sciences, Marlborough, MA, USA) was equilibrated with buffer 1 for affinity chromatography using NGC system (Bio-Rad, Marnes-la-Coquette, France) and then the filtered supernatant was loaded into the column. The unbound proteins are washed with buffer 1 and BC2L-C-nt was eluted using a 20 column volumes (CV) gradient of 0-500 mM imidazole. The eluted fractions were examined for the protein on 15% SDS-PAGE gel and the imidazole was removed from the pooled fractions using a PD10 desalting column (GE Healthcare Life Sciences, Marlborough, MA, USA). The protein was concentrated to at least 0.7 mg/mL by centrifugation (Vivaspin 3kDa, Sartorius, Goettingen, Germany) and treated overnight at 19 °C with TEV protease (1:50 w/w, enzyme:protein ratio), 1 mM ethylenediaminetetraacetic acid (EDTA) and 0.5 mM tris (2-carboxyethyl) phosphine (TCEP) for tag cleavage. The sample was again loaded into the affinity chromatography column and purification was repeated using the same conditions. This allows the separation of the desired protein (14 kDa) and the cleaved fusion (52 kDa) which could be assessed by SDS-PAGE 15 %.

The protein was again concentrated by centrifugation and the concentration was determined by UV absorbance at 280 nm using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Illkirch-Graffenstaden, France). Finally, SEC was performed by employing an ENrichTM SEC 70 10 × 300 column (Bio-Rad, Marnes-la-Coquette, France) in a NGC™ systems (Bio-Rad Ltd.).

The analytical column was pre-equilibrated with a buffer (20 mM Tris-HCl pH 7.0 and 100 mM NaCl) which was optimized for protein stability using TSA. The sample was injected using 240 μL volume with a flow rate of 1.0 mL/min. A column calibration curve based on gel-filtration standards (GE Healthcare, Life Sciences) was performed which helped in the calculation of protein molecular weight.

Thermal shift assay (TSA)

The fragments KL1, KL2, KL3, KL5, KL6, KL7, KL9, KL10, KL11 (Table 3.2, Chapter 3) were purchased from the Maybridge Company (Fisher Scientific International) and the other fragments KL4, KL8 and KL12 were purchased from abcr GmbH. The purity of the fragments was tested using liquid chromatography-mass spectrometry (LC-MS). For the dye-based TSA, BC2L-C-nt (5 µM) in assay buffer (20 mM Tris HCl, 100 mM NaCl, pH 8.0) was incubated with 50x SYPRO orange and 2.5 mM KL1-12 in the presence or absence of 20 mM Me-α-L-Fuc. A Qiagen Rotor-Gene Q instrument was used to apply a heat ramp of 1 °C/min from 25-95 °C and SYPRO orange fluorescence was monitored at 620 nm using the appropriate optical channel.

Microscale Thermophoresis (MST)

All the fragments (KL1-KL12) were tested using microscale thermophoresis. Prior to labelling, the protein was transferred to 0.1 M carbonate-bicarbonate, pH 9.5 buffer using buffer exchange column. The protein (lysine residue) labelling by the fluorescein isothiocyanate (FITC) dye was obtained at a ratio of 1.6 :1 (protein to dye ratio) as determined by UV spectroscopy. Tween-20 (0.2%) was also added to avoid protein sticking to the surface of tubes at low concentration. The protein at 326 nM concentration was used to test the fragments (2.5 mM).

STD-NMR interaction studies

STD-NMR studies were performed by Prof. Francesca Vasile at the University of Milan, Italy. More details related to the experiment can be found in the publication (Appendix 1). 1

X-ray crystallography, data collection, and structure determination

Crystals of BC2L-C-nt in complex with Globo H oligosaccharide were obtained following the procedure described previously. 2 Globo H hexasaccharide at 10 mM concentration in water was added to BC2L-C-nt (5 mg.mL-1) for a ligand concentration of 1 mM. The mixture of protein and ligand (Globo H) was incubated for 30 minutes at room temperature (22 °C) and finally 2-μL hanging drops containing 50:50 (v/v) mix of protein and reservoir solution (1.2-1.4 M tri sodium citrate pH 7.0) was used for crystallization using vapor diffusion method. Crystals were obtained in a few days. Cubic shape crystals of apo form were Lfuc-Ake-KL07 -9.4 -6.9

MD simulations of glycomimetic ligands Lfuc-Aky-KL07 and Lfuc Aky-KL08 in complex with BC2L-C-nt

The analysis of the trajectory (1 µs) for the BC2LC-nt complex with Lfuc-Aky-KL07

shows that the designed ligand is maintained in both sites: fucose binding and the site X.

Values of RMSD of 0.5 Å for sugar and 1.5 Å for the linked non-sugar (KL7) part (Annexe, This interaction with Ser119 was not observed in the docking studies. Thus, the results from MD simulation of the complex with the ligand provides important information for the design of new ligands that can establish additional interactions with Ser119. The analysis of Lfuc-Aky-KL08 also provides similar results confirming that the ligand maintains key interactions in Annexure: Chapter 5

S.No Structure

Query molecule and non-sugar (0.5 Å) part of the ligand. In most of the sampled structures, H-bonding interactions between the ligand and Asp70 were maintained but it also displayed additional interactions with Ser119 in few (< 10%) sampled structures. The possible reason is that the nonsugar part of the ligand is shorter (lacks methyl groups or benzylamine moiety) compared to other two ligands thus accommodated well in the site X.

Scientific communication: Short secondment at Glycopedia

Under the framework of PhD4GlycoDrug, a short secondment for 15 days was organised in coordination with Glycopedia for the purpose of scientific communication.

During this time, a chapter was drafted in collaboration with Rafael Bermeo and Dr. Serge Perez. The chapter will be added to the glycopedia platform although it was published as an open-access review article in the Beilstein Journal of Organic Chemistry. The review article entitled 'Computational tools for drawing, building and displaying carbohydrates: a visual guide' is attached in the next section.

Identification of druggable allosteric pockets in β-propeller lectins

In addition to the research work related to my PhD project, I have been working on another side project with the collaborators of Dr. Imberty at the Max Planck Institute of Colloids and Interfaces, Department of Biomolecular Systems, Germany. The work has been submitted for publication as a research article in a scientific journal.

In this research, we identified a druggable allosteric binding site in a lectin from the opportunistic human pathogen Burkholderia ambifaria (BambL) 1 using the SiteMap 2 analysis and docking predictions. The computational analysis combined with experimental studies revealed a promising compound (dissociation constant of 0.3±0.1 mM) binding at the allosteric site. Interestingly, the fragment binding at the allosteric site affected the carbohydrate-binding site as determined by protein-observed fluorine NMR (PrOF). 3 These finding were further supported by the studies involving site-directed mutagenesis in the orthosteric and secondary pocket which showed the effect on fragment binding, indicating additional insights into the communication path between the sites. In addition, computational and experimental studies performed on the structurally similar β-propeller lectins from Ralstonia solanacearum (RSL) 4 and Aspergillus fumigatus (AFL) [5][6] also suggest the presence of druggable secondary pockets. These observations can be useful for drugdiscovery campaigns to develop allosteric inhibitors for bacterial and fungal lectins as a new therapeutic approach against antibiotic-resistant pathogens.

The computational approaches used to perform the studies are briefly discussed in this chapter.

Methods

Binding site prediction

The crystal structures of BambL (PDB code 3ZZV and 3ZW0 ) were used for prediction of the possible secondary binding sites using the SiteMap tool. SiteMap creates a grid of points on the protein surface based on depth, size, van der Waals interaction energy, hydrophilicity and hydrophobicity and assign a single scoring function (SiteScore) to the potential druggable regions. The score helps to assess a site's propensity for ligand binding and prioritize the pharmaceutically relevant regions in the target protein. For BambL, the calculations identified three regions at the interface in the trimer as potential druggable sites. These sites were explored further, thus the docking model was set up involving the key residues in the identified region. The same approach was applied to predict the druggable sites in the apo (PDB code 3ZI8) and holo (5AJB) forms of another bacterial lectin (RSL) from Ralstonia solanacearum. In addition, the calculations were performed on a fungal lectin known as AFL (PDB code 4AGI) from Aspergillus fumigatus.

Preparation of protein model

All the calculations were performed using the Schrödinger Suite through Maestro (version 2018-1) graphical interface. 7 Atomic coordinates from the high resolution crystal structures of BambL (PDB code 3ZZV and 3ZW0 ) were taken from the Protein Data Bank. 8 The asymmetric unit contains three peptide chains and carbohydrate ligands, around a 3-fold pseudo axis of symmetry. The water molecules were removed and hydrogen atoms were added. pKa was predicted for protein residues using the PROPKA 9-11 method at pH 7.4.

Protonation state (δ-nitrogen protonated) was assigned to the histidine (His58) residue.

Finally, the complex was subjected to restrained minimization with convergence of heavy atoms to an RMSD of 0.3 Å using the OPLS3 force field. 12

Preparation of ligand models

All (three) ligands were prepared for docking using the LigPrep 13 tool and generated tautomers, stereoisomers and protonation states at pH 7.4. The calculations yield 13 structures.

Models for docking study

For docking grid generation (using PDB 3ZW0), the centroids of residues from chain B (Gly67, Thr69, Gly86, Leu87) and chain C (Thr18, Asn20, Lys23, Thr25) were selected to define a cubic grid box with dimensions 32×32×32 Å. The grid was used for docking studies using extra precision (XP) and standard precision (SP) scoring functions. All the calculations were accomplished by Glide (version 7.8) 14 using the flexible docking approach.

Results

Binding site analysis

The crystal structures of BambL show that it forms a trimer involving three identical chains. The complex with oligosaccharides revealed 6 binding sites in both the PDB structures (PDB code 3ZZV and 3ZW0). In addition, the SiteMap tool identified three secondary sites (Figure 9.1) which can potentially host the ligands. The predicted sites located at the interface of the monomers near C-terminal form a narrow channel near the termini involving residues Thr18, Asn20, Lys23, Thr25, Gly67, Thr69, Gly86 and Leu87. The deep cavity can maximize the occupancy of suitable ligands. The bindings site surrounded by hydrophilic residues makes it suitable to accommodate ligands with polar groups. Comparison of the key residues in both PDB structures shows differences in orientation of side chains, probably due to their location at the surface and flexible terminal region of the protein (Figure 9.2).

However, shape and size of the predicted sites were slightly different due to differences in residues in the binding sites. In fungal lectin AFL, SiteMap identified a completely different druggable region which is structurally more distant from both bacterial lectins (Figure 9.4).

Docking analysis

Docking calculations were done using XP and SP approaches to examine any difference in the ligand binding pose. The results from both the docking approches show that the ligands bind at the predicted site and the residues Thr18, Lys23, Thr25, Gly67, Tyr84 and Leu87 play key role in the binding (Figure 9.5 and 9.6). In both the methods, Ligand 16A02 binds with an almost identical pose with a small difference due to a rotation of morpholine ring. Likewise, 15B05 and 14H04 generated similar binding poses with a slight difference in the orientation of fluorobenzene and benzoic acid, respectively. Results from docking studies indicate that the ligands possibly bind to the identified binding sites.