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Proteomics/Protein - Protein Interactions/Types of Interactions

Page Edited and Updated by: Christopher Fucile E-mail: cff8255@rit.edu

This Section:

Molecular Docking
Molecular docking is the technique that is used to study molecular binding and   how   molecules  bind. The term “docking” is mostly related to protein molecule interactions. There are several types of molecular docking for protein interactions: Of all these Protein ligand interaction techniques are the most widely used techniques.
 * If a protein interacts with a ligand : protein-ligand interaction
 * If a protein interacts with another protein: protein-protein interaction
 * If a protein binds to DNA: protein-DNA interactions

Advantages: One of the major advantages conferred by docking is that it allows researchers to quickly screen large databases of potential drugs which would otherwise require tedious and prolonged work in the lab using traditional drug discovery procedures.

The Principle Behind Docking
The foremost thing that we need to start with a docking search is the sequence of our protein of interest. We then search for ligand sequences from suitable databases. These two components are the input for the docking method. There are two basic components of molecular docking:
 * The search algorithm
 * The scoring function

The Search Algorithm
A search algorithm finds the best docking pose measured by the scoring function. Since it is impossible to do an exhaustive search most of the docking tools resort to the most flexible ligands available. There are several ways for sampling the search space.

Some examples are:
 * Use a coarse-grained molecular dynamics simulation to propose energetically reasonable poses
 * Use a "linear combination" of multiple structures determined for the same protein to emulate receptor flexibility
 * Use a genetic algorithm to "evolve" new poses that are successively more and more likely to represent favorable binding interactions.

The Scoring Function

A scoring function discriminates correct (experimentally verified) docking poses from incorrect ones. It estimates the binding affinity between ligand and receptor. To check whether the resultant pose is stable or not, we use several physics theories, such as Gibb's free energy. A low Gibb’s free energy confirms a stable conformation rather than a high energy which denotes an unstable complex. Another approach of the scoring function is to establish a conformational relationship (based on statistical analysis) from large protein databases and then evaluate the stability and fitness of the pose. One of the main problems with utilizing a database technique for the docking function is the increased rate of false positives.

Protein-Protein Docking Interactions
Protein-protein interactions occur between two proteins that are similar in size. The interface between the two molecules tend to be flatter and smoother than those in protein-ligand interactions. Protein-protein interactions are usually more rigid; the interfaces of these interactions do not have the ability to alter their conformation in order to improve binding and ease movement. Conformational changes are limited by steric constraints and thus are said to be rigid.

Protein Receptor-Ligand Docking
Also known as the molecular docking technique, protein receptor -ligand docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands. Protein receptor-ligand motifs fit together tightly, and are often referred to as a lock and key mechanism. There is both high specificity and induced fit within these interfaces with specificity increasing with rigidity. Protein receptor-ligand can either have a rigid ligand and a flexible receptor, or a flexible ligand with a rigid receptor.

Rigid Ligand with a Flexible Receptor
The native structure of the rigid ligand flexible receptor often maximizes the interface area between the molecules. They move within respect to one another in a perpendicular direction in respect to the interface. This allows for binding of a receptor with a larger than usual ligand. Normally when there is ligand overlap in the docking interface, energy penalties incur. If the van der Waals forces can be decreased,  energy loss in the system will be minimized. This can be accomplished by allowing flexibility in the receptor. Flexible receptors allow for docking of a larger ligand than would be allowed for with a rigid receptor....

Flexible Ligand with a Rigid Receptor
When the fit between the ligand and receptor does not need to be induced, the receptor can retain its rigidity while maintaining the free energy of the system. For successful docking, the parameters of the ligand need to be constant and the ligand must be slightly smaller in size than that of the receptor interface. No docking is completely rigid though; there is intrinsic movement which allows for small conformational adaptation for ligand binding. When the six degrees of freedom for protein movement are taken into consideration (three rotational, three translational), the amount of inherent flexibility allowed by the receptor is even greater. This further offsets any energy penalty between the receptor and ligand, allowing for easier, more energetically favorable binding between the two.

Docking Problems
The search space consists of all possible conformations and configurations. With present computing resources, it would be impossible to exhaustively explore the search space for all possible poses (a pose is the name given to the configuration of the conformation of a molecule in a coordinate system). Needless to say, every docking simulation is a trade-off between accuracy and speed and a good docking tool is expected to maintain a reasonably good balance between the two. See also Protein-Protein Interaction Network Visualization and  Prediction Methods for Interactions and Docking.

References(Open Access)
1. "Docking (molecular)." Wikipedia, The Free Encyclopedia.. Wikimedia Foundation, Inc. 9 Apr 2007 ..


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Proteomics/Protein - Protein Interactions/Types of Interfaces

Page Edited and Updated by: Christopher Fucile E-mail: cff8255@rit.edu

This Section:

Domain-domain interfaces
Domain-domain interfaces exist between separate domains contained in the same chain. A domain is defined as a region of a protein containing a globular structure with a distinct self-stabilizing motif that has the ability to fold independently of the protein. Domain sequences are not unique to individual proteins; thus the structural and sequential alignment of a binding domain is often used to determine the biological function and significance of the protein. The Protein Data Bank can be used in tangent with SCOP and the NIH’s Cn3D to visualize the domain binding in proteins.

Intra-domain interfaces
Intra-domain interfaces have a single structural domain and the surface interfacing occurs most often through disulfide bonding between the N-terminus of the protein to DNA binding C-domain.

Hetero-oligomers
Hetero-oligomers create an interface between permanently interacting protein chains of in different chain sequences in oligomeric enzymes. When hetero-oligomer binding occurs, the ligand on one subunit interacts both positively and cooperatively with the same type of ligand bound on adjacent subunits.

Hetero-complexes
Hetero-complexes contain interfaces between different transiently interacting protein chains. Examples of hetero-complexes include signal transduction proteins, protease complexes, enzymes, antibody-antigen, and G- coupled proteins.

A common theme contained in domain-domain, intra-domain, and hetero interfaces are  the presence of hydrophobic, hydrophilic interactions, and cysteine and salt bridges, which are not found in the homo structures discussed below.

Homo-oligomer surfaces
Homo-oligomer surfaces are permanent interfaces between intermingling identical chains. Oligomeric enzymes bind in this way; the binding of a ligand on one subunit interacts in such a way that the same ligand interacts positively and cooperatively on the same type of ligand bound on adjacent subunits.

Homo-complexes
Homo-complexes exist between transitorily interacting interfaces that contain indistinguishable protein chains. Homo-dimer interfaces mimic the appearance of the protein interior. They incorporate non-covalent interactions and These interactions at the quaternary structural level that allow for the numerous regulatory roles including ligand binding or modification. Enzymes, transport proteins and transcriptional factors all contain homo-dimeric interactions. These types of interactions are unique in that the presence of hydrophobic or hydrophilic interfaces are rare, nor are salt bridges, which will be discussed later in the chapter. Homo-complexes prefer to form bonds between amino acids which are identical.

References (Open Access)
1. "RCSB Protein Data Bank" RCSB Protein Data Bank. 30 March 2008.

2. "SCOP: Structural Classification of Proteins" SCOP. 30 March 2008.

3. "Structure Home" NCBI. 30 March 2008.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Proteomics/Protein - Protein Interactions/Binding Sites

Page Edited and Updated by: Christopher Fucile E-mail: cff8255@rit.edu

This Section:

Characterization of Binding Sites
Protein stability and flexibility are dependent on electrostatic interactions such as hydrogen bonding and van der Waals forces. The energy of the interaction is not evenly distributed between the two proteins at the interface. Rigidity in the interface ensures that the entropy loss is offset and the binding free energy is contributed to in a favorable way by the amino acid residues contained in the interface. Contributions of hot spots to the stability of the protein-protein complex within a hot region is cooperative, however the contributions of independent hot regions are additive.
 * Hydrogen Bonding/Hydrophobic Packing
 * Dehydron Bonds
 * Salt Bridges
 * van der Waals
 * Cation-π Interactions
 * Pockets
 * Hot Spots
 * Protein Chips

Hydrogen Bonding and Hydrophobic Packing
Hydration is very important for the three dimensional structure and activity of proteins. Surface water molecules are held most strongly by the positively charged basic amino acids. Hydrogen bonding adds conformational flexibility, while the level of hydration determines the degree of flexibility in the internal molecular motions. Water molecules in close proximity to peptide hydrogen bonds cause separation in the bonds and a release of tension in the structure. The presence of water molecules also prevents swift conformational fluctuations from occurring. Water interacts with protein surfaces; the breaking of hydrogen bonds between the water molecules and the amino acids has an unfavorable enthalpic result on the system, effecting the overall energy of the protein. Water molecules act as an adhesive, sealing holes in complementary surfaces that would normally lack shape. Hydrogen bonding drastically reinforces other bonding present in the interface between proteins. This type of bonding occurs when an hydrogen atom proximal to a docking facade interacts across the interface with atoms of another molecule. Hydrogen bonding can occur between side chains, backbone groups, and between the two.

Hydrophobic surfaces located at ligand receptor interfaces have greater ambiguity with bonding. Hydrogen bonding between the peptide atoms decreases the hydrophobicity of the backbone. Coincidentally, hydrophobic surfaces can be sealed with an ordered shell of water molecules, creating an entropically favorable state when water molecules are ejected. Water removal increases the thermodynamic benefit in hydrophobic patches. These patches are uncommon though; the patches found in proteins are more often hydrophilic. The secondary structure of the protein is a result of hydrogen bonding since the residues arrange themselves in such a way so that hydrophobicity of the backbone is reduced. α-helices and β-sheet conformations are the way in which is accomplished.

Amino acids in order of hydrophobicity
Least hydrophobic to most hydrophobic:

 * Lysine
 * Glutamic Acid, Aspartic Acid
 * Arginine, Glutamine, Serine, Aspargine
 * Proline, Glycine, Threonine
 * Histidine, Alanine
 * Tyrosine
 * Cystine, Valine, Tryptophan, Methionine
 * Leucine, Isoleucine, Phenylalanine

Dehydron Bonding
The hydrogen bonds in the backbone of a protein usually exist in the form of dehydron bonds, which form between two hydrogen atoms bonded that differ in polarity bonds. The proton acceptor in this case is a metal hydride with the donor being OH or NH. Dehydrons attract hydrophobes, their bonding increases the electrostaticity of hydrogen bonds by increases the surrounding dielectric coeffiecient. The dehydron bonds thus create a hydrophobic shell around the backbone increasing the electrostatic interaction; this desolvated environment is condusive for salt bridge formation increasing structural stability.

Salt Bridges
Salt bridges, also referred to as ionic bonds, are a form of chemical bonding that occur between amino acids of opposite charge. They contribute greatly to the stability of the protein and are usually found in the interior of the protein structure. This stability is dependent on the location of the side chains, and the electrostatic contribution. Hydrophobic environment is conducive to salt bridges. Charged residues within four angstroms to each other have the ability to form salt bridges.

Van der Waals Interactions
Van der Waal forces occur between dipoles. Covalently bonded atoms differ in electronegativity that causes a minute dipole. Since the distribution of electrons is asymmetrical, a dipole effect is created by the van der Waals attraction. These dipoles oscillate, creating a dipole field. This field allows two atoms with identical oscillation frequencies to synchronize, one negative while the other positive. Thus the charge between the two cancel each other out and the net charge is zero. van der Waals bonds are one of the weakest intermolecule forces studied. But van der Waal bonds are important, since it is with these forces that noble gases can achieve bond saturation with the dominant form of interaction between electrically neutral species. Induced dipole interactions cause van der Waal bonding to occur, and the large close surface contact allows van der Waal binding to contribute significantly. The following formula is used to determine van der Waal bonding: A/(r6) - B/(r12) where r is the distance between A and B.

Pockets
A protein pocket is a concave surface region containing the active sites of the protein. These areas are solvent accessible, and are characterized as either unfilled or complemented, the partner conformation not being taken under consideration. Compositional difference between complemented pockets and other surface pockets may be useful for predicting complemented pockets from the unbound states. The floor of complemented pockets are often filled with residues that are ionizable and/or polar. In a hydrophobic environment, these residues provide molecules for salt bridge formation and hydrogen bonding which are essential for binding stability. Pockets are crucial for binding site flexibility for large interfaces reduce binding stability, and allow for ligand movement.

Cation-π Interactions
Cation-π pairs are frequently present in the interface of proteins, probably due to their low energy conformation. They play an important role in specificity, causing a need for conformational changes to provide the correct orientation for binding. Cation-π interactions are found most frequently in homodimers. The abundance of this form of bonding is tied to the occurrence of specific amino acids. In homodimers, arginine bound to phenylalanine forms the most frequent cation-π bonds, while in the other protein complexes arginine-tyrosine and arginine-tryptophan cation-π bonding is a more likely occurrence.

Hot Spots
A hot spot is an area of high energy and binding around an amino acid residue. Surrounded by hydrophobic pockets, hot spots are found in clusters, rather than scattered throughout the interface. This clustering assists with the elimination of water molecules, and increases the bond strength and available bond energy through increases charge-charge relations. Hot spots usually do not include hydrogen bonding, electrostatic interactions, although there is a slight preference of non-polar residues. Packing around hot spots is significantly tighter that the other residues found in the interface. Since hot spots contribute significantly to binding, the association of neighboring residue size, charge, and interaction must be taken under consideration when considering binding affinity and docking. It has been established that a linear relationship between the number of hot spots and the interface size exists; the larger the interface the higher the number of hot spots present.

Protein Chips
Much like DNA microarrays, and gene chips, protein chips have revolutionized the method and speed with which scientists can discover differences in gene expression levels, protein chips are a relatively new addition to proteomics that brings high-throughput efficiency to discovering protein-protein interactions. Earlier protein chips emulated existing proteomic techniques by requiring labeling of proteins, a requirement that resulted in only revealing certain proteins or classes of proteins. Recent developments have put forth label-free techniques that couple the original protein chip idea with MALDI-TOF  mass spectrometry, and other forms of mass spectrometry, to produce a protein chip technology that does not require proteins to be labeled, consequently widening the range of protein-protein interactions detected with each chip. While protein chip technology does not directly contribute to binding site characterization, it is a useful method for gaining insight into how proteins and classes of proteins function through their interactions with many other proteins. See chapter on protein chips more more information.

References(Open Access)
1. "Hydrogen bond" Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 30 March 2008. .

2. "Dehydron" Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 30 March 2008. .

3. "Van der Waals force" Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 30 March 2008. .

4. "Proteomics/Protein Identification - Mass Spectrometry" Wikibooks. 30 March 2008. .


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Parkour

Le Parkour (also called parkour, and abbreviated PK) is a physical discipline of French origin in which participants attempt to pass obstacles in the most efficient way possible as if moving in an emergency situation, using skills such as jumping and climbing, or the more specific parkour moves. The obstacles can be anything in one's environment, but parkour is often seen practiced in urban areas because of many suitable public structures that are accessible to most people, such as buildings and rails. Parkour also has a deep mental and spiritual side; based around the overcoming of fears and mental obstacles in an altruistic way which may one day prove beneficial to the community.

Another discipline which is related on the surface is Freerunning, which is a break-away discipline formed by Sebastien Foucan, where the freerunner moves through their environment in any way they wish, which often includes acrobatic manouveres such as flips and kicks or vault and flip combinations. These movements are generally accepted as not parkour because they are not the most efficient way of moving through an environment.

Introduction
An introduction to the Parkour Wikibook, the practice, philosophy and history of Parkour, and a guide for beginning traceurs
 * What is Parkour?
 * How can I start?
 * History and Philosophy of Parkour

/Exercises/
Some basic advice on exercises to condition the body for Parkour
 * The Squat
 * The Push-Up
 * The Pull-Up
 * The Lunge
 * The Handstand Push-Up
 * The Muscle-Up
 * The Burpee

Basic movements
Basic movements of Parkour
 * Landing
 * Parkour Roll
 * Cat Leap
 * Dash Vault
 * Monkey Vault(Saut De Chat/Cat Pass/Kong/Panther/Gorilla)
 * Speed Vault
 * Kash Vault
 * Two Handed Vault
 * Turn Vault
 * Precision Jump
 * Lazy Vault
 * Reverse Vault
 * Underbar
 * Tic Tac
 * Wallrun

Advanced Movements
These movements are not recommended for beginning traceurs
 * Double Kong Vault
 * Diving Roll
 * Railflip Vault
 * Barrel Vault
 * Vert Vault
 * Rocket Vault
 * Gate Vault

/Freerunning/ (Not Parkour)
These are considered to be Freerunning or Tricking, related to Parkour but not embracing the core value of efficient movement
 * Frontflip
 * Backflip
 * Sideflip
 * 360 Catleap
 * Screwdriver
 * Handspring
 * Wallspin
 * Palmspring
 * Cheat Gainer
 * Dash Bomb
 * Kong Gainer
 * Speed 360
 * Bar Backflip
 * Wallflip

title: Parkour


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Proteomics/Protein - Protein Interactions/Allosteric and Affinities

Page Edited and Updated by: Christopher Fucile E-mail: cff8255@rit.edu

This Section:

Allosteric Effects and Binding Affinities
Allosteric binding occurs at an interface that is not within the active site of the protein, while allosteric regulation occurs through binding of an activator molecule in the interface of the allosteric site. Allosteric binding is cooperative, that is the affinity for the ligand of the molecule rises as the levels of ligand increase. Binding is said to be noncooperative if the concentrations of ligand have no effect on binding.

There are two models for allosteric binding:
 * Concerted modeling
 * Sequential modeling.

Concerted Modeling:
In concerted modeling, the conformational changes of one protein subunit extend to all of the other subunits, such that all the subunits share the identical conformation. Without a ligand bound, the equilibrium of the conformation favors either a tensed or relaxed state, T or R. The conformation is shifted between these states depending on the binding of the ligand to an allosteric site.

Sequential Modeling:
In sequential modeling the conformational changes of one protein subunit may induce similar changes in the others, but none of the subunits share identical conformations. Induced fit is an element of the sequential model. Collision with the interface elicits ligand conformational changes in such a way that there is relaxation in the subunit allowing the receptor to form fit around the ligand. But these conformational changes do not propagate to the surrounding subunits.

Allosteric inhibition occurs when the binding of a ligand in the manner described above decreases the binding affinity of substrates in the active site.

Binding Affinity
Receptor-Ligand binding can be characterized by the affinity, or attraction, the two have for each other. Binding sites with high affinity, have strong intermolecular forces between receptor-ligand and remain bound for longer periods of time than those of low affinity. High affinity sites have high molecule energies, and can transmit some of their energy to provide for conformational changes in the receptor. The concentration at which half amount of ligand is bound to the receptor is used to quantify binding affinities. High affinity sites have more ligand bound, whereas low affinity sites have less than half bound. Binding affinities are used to determine the number and location of binding sites present in a protein, and this information is gathered through computation of data determined through isothermal titration calorimetry  and enzyme inhibition.


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I.0 Introduction Section I.A: I.1 Wie heißt du? (1. Teil) •  I.2 Wie heißt du? (2. Teil) •  I.3 Bitte buchstabieren Sie •  Review Section I.A Section I.B: I.4 Freizeit •  I.5 Geburtstag •  I.6 Essen •  Review Section I.B Section I.C: I.7 Kleidung • I.8 Familie und Nationalität • I.9 Schule • Review Section I.C Section I.D: I.10 Das Fest • I.11 Privileg und Verantwortung • I.12 Wetter • Review Section I.D Section I.E: I.13 Zu Hause essen • I.14 Filme • I.15 Das Haus • Review Section I.E
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Proteomics/Protein - Protein Interactions/Experimental

Page Edited and Updated by: Dan Surdyk E-mail: dfs6389@rit.edu

This Section:

FRET
Fluorescence Resonance Energy Transfer uses fluorescent energy transfer to visualize protein interactions. The procedure is simple. Fluorophores are fused to the proteins of interest and then bombarded with light at the excitation wavelength. The first fluorophore transfers some of the energy it absorbed from the light source to the second fluorohore, which in turn emits some of its energy into the environment where, with the use of a fluorescent microscope, it is visible.

BRET
Bioluminescence Resonance Energy Transfer is a biodetection system that utilizes energy transfer between a donor and receptor to visualize binding. When the two molecules are in close proximity, a green fluorescent protein on the receptor absorbs the energy from luciferase attached to the donor. As a result, the GFP emits light that appears green to the naked eye.

Yeast Two-Hybrid
The yeast two-hybrid system is an in vivo system that utilizes the binding domain (BD) and activation domain (AD) of genes (used in transcription) to determine protein interactions. Proteins are fused to both the BD and the AD of a reporter gene, one to each. When the protein attached to BD interacts with the protein fused to the AD, transcription is initiated, and the reporter gene is transcribed. The effect is quantifiable by measuring the levels of reporter gene expressed.

Protein arrays
Thanks to the success of DNA microarray technology, other areas of science are developing arrays or “chips” to test for multiple things at once. One of these areas is protein interaction studies, where array technology is currently expensive and complicated, but looks like it will soon be a realistic option. Protein interaction arrays have the advantage of allowing a scientist to test for numerous interactions quickly and conveniently. Read more about arrays in Chapter 8: Protein Chips.

Tandem Affinity Purification (TAP)
To perform TAP, a “TAP tag” must be attached to the protein of interest. This is done by genetic recombination of the protein with the tag, usually using plasmids. The expressed fusion protein then contains the protein of interest covalently bound to the tag. The tag consists of two IgG binding domains from Protein A from Staphylococcus aureus and a calmodulin binding protein (CBP), with a TEV protease cleavage site between those two sections. The CBP is closest to the protein, and the IgG binding domains are farthest away. When the protein is extracted from the cells, any interacting proteins will come with it, giving you complexes of protein attached to the tag rather than just your one protein. The sample is applied to a column of IgG beads, so that the complexes of interest bind to them due to protein A, and any other proteins pass through. The bound proteins are released by cleavage with the TEV protease, and the resulting proteins passed through a column with calmodulin beads. The CBP causes them to bind these beads, and the protease and any other contaminants pass through. This results in a very pure sample of the protein of interest and its interaction partners. The resulting proteins can be identified by SDS-PAGE or mass spectrometry.

Solid-phase detection
Unlike many interaction detection methods, solid-phase detection does not involve labeling of proteins. In these techniques, one protein is attached to the surface of a transducer. The other protein is contained in a solution that can be applied to the surface. This allows binding kinetics of a reaction to be studied in real time. Some of the common approaches use surface plasmon resonance or reflectance interference to detect and image the interactions. Just like the protein array trend, this area is moving more toward parallel analysis of several interactions. This can be done with a microfludic system, where proteins in solution are passed through a tiny channel that runs over a series of surfaces that each have a different type of protein attached to them.

Single molecule detection
Some of the newest and most exciting developments in studying protein interactions involve single molecules. These allow experiments to be carried out with low concentrations, and with well-defined samples so there won’t be other interactions confounding the results.

One method used is force spectroscopy. One member of a pair of interacting proteins is attached to the tip of an atomic force microscope, which can measure very small forces. The other protein is attached to a solid surface. The tip is brought down to the surface so that the proteins interact, and then pulled away. The amount of force needed to pull the tip away from the surface is the force of the interaction. This method was used effectively by Nevo et al. to determine whether the binding of an effector protein to a receptor caused a conformational change.

In addition, fluorescence can be used to detect single interacting pairs of proteins. Two proteins can be marked with different fluorescent labels, and confocal microscopy used to scan a very thin section of a small volume of sample and detect the fluorescence. When it detects both colors, it knows that it must have found an interacting pair, because they have to be very close to be in the same thin section.

Further Information

 * FRET: http://biowww.net/browse-44.html
 * BRET (the BMG Lab site): http://www.bmglabtech.com/
 * Yeast two-hybrid: http://www.uib.no/aasland/two-hybrid.html

References (Open Accss)
[1] Nevo, R., Brumfeld, V., Elbaum, M., Hinterdorfer, P., Reich Z. 2004. Direct discrimination between models of protein activation by single-molecule force measurements. Biophys J, 87:2630-2634.

[2] Piehler, J. 2005. New methodologies for measuring protein interactions in vivo and in vitro. Current Opinions in Structural Biology, 15:4-14.

[3] Puig, O., Caspary, F., Rigaut, G., Rutz, B., Bouveret, E., Bragado-Nilsson, E., Wilm, M., Se´raphin, B. 2001. The Tandem Affinity Purification (TAP) Method: A General Procedure of Protein Complex Purification. Methods, 24:218-229.

[4] http://physics.nist.gov/Divisions/Div844/facilities/smspec/smspec.html


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Proteomics/Protein - Protein Interactions/Prediction

Page Edited and Updated by: Dan Surdyk E-mail: dfs6389@rit.edu

This Section:

Monte Carlo
The Monte Carlo approach to protein/protein interaction is modeled after the well-known random sampling algorithm used in computer science. The theory is summarized as follows: Given a sufficiently large number of initial configurations, one will either emerge as the best configuration, or eventually lead to it. The process begins with the docking of one protein to another. A score is then computed based on things like energy, amount of exposed hydrophobic surface, number of interacting amino acids, among other parameters. Random changes are made to the interaction (on the level of whole proteins, side chains, and even individual atoms!), and the step is either approved or rejected based on whether it caused the score to improve.

Accessible Surface Area (ASA)
Each atom in a protein has a van der Waal’s radius, and this is used to create a van der Waal’s surface of the protein. The ASA describes all the places that solvent could potentially make contact with this surface. It is calculated by computationally “rolling” a sphere around on the van der Waal’s surface, and calculating what parts of the surface it makes contact with. This surface is incorporated in many docking and surface patch programs.

Critical Assessment of PRediction of Interactions (CAPRI)
The CAPRI challenge the European Bioinformatics Institute’s (EBI) creative way of evaluating protein docking programs. Groups receive protein data from EBI and attempt to dock the proteins. Their results are then tested and compared, not only giving recognition to deserving researchers, but also providing scientists with a comparison of the advantages of each program or protocol.

Geometric Hashing
Geometric Hashing is a molecular modeling technique that matches a target molecule to a protein of interest.

TESS is an algorithm that uses X-ray and NMR data to determine 3D templates of proteins from the structures deposited in PDB. New structures can be scanned against these 3D structures to identify functional sites. Thus TESS is used to identify function of new protein structures and design proteins with specific function with the development of a 3D template database.
 * TESS

Weighted Delaunay Triangulation
Delaunay triangulation is one way of turning the surface of a protein into triangles. The method involves taking a set of points in three dimensions, and forming triangles so that if a circle were drawn that contains all three points (a circumcircle), no other points in the set would fall within that circle. In the case of protein surfaces, the points would be locations of atoms. This creates a surface without little “sliver” triangles that take up more time and memory in the computer without really being meaningful to the calculations.

FADE and PADRE
The Fast Atomic Density Evaluator (FADE) and Pair-wise Atomic Density Reverse Engineering (PADRE) programs are used to determine molecular modeling of proteins. FADE and PADRE identify most prominent features in a protein such as regions which are involved in interaction with the other molecules by elucidating features of interest like crevices, grooves and protrusions. FADE has a special feature of evaluating shape complementarity for docked protein protein complexes. FADE and PADRE can be downloaded at http://www.mitchell-lab.org/mitchell-lab/FADE.php

CAST
Computed Atlas of Surface Topography measures the shape of protein pockets with weighted Delauney triangulation. Proteins and certain aminoacids have specific structures known as cavities which creates the physiochemical properties needed for the function of the protein. CASTp is an online tool which estimates the function of the protein by locating and measuring the pockets and voids on 3D protein structures. The new version of CASTp includes the annotated versions from PDB,Swiss-prot and SNPs. The annotated residues from these sources are mapped to surface pockets, interior voids or other regions of the PDB structures. CASTp is used to study surface features, functional regions and specific roles of key residues of proteins. CASTp can be downloaded at http://cast.engr.uic.edu

=Computational Methods for Predicting Protein and Domain Interaction Partners=

Gene neighbor and gene cluster method
The hypothesis of the gene neighbor method is genes with similar functions are transcribed together as a single unit known as operon. If the genes that encode two proteins are neighbors on the chromosomes of several genomes then those proteins will have similar function. These genes which transcribe together inspite of the genetic distance between them are known as co-regulated genes. Conservation in the co-regulated genes is observed even in the distantly related organisms. The operons reveal the functional linkage between the constituent genes. Gene neighbor method is the most widely used method compared to other genomic inference methods. This method is used to detect the previously unknown interactions. Examples of gene neighbor prediction are the prediction of archeal exosome by comparing with the eukaryotic genome.

Phylogenetic profile methods
Phylogenetic profile describes the presence or absence of a protein in a particular organism whose genome has been sequenced. Phylogenetic profiles consists of n entries where n indicates the number of genomes sequenced. Presence of a homolog to a given protein in the nth genome is denoted by one and absence of the protein is denoted by zero. The proteins are then clustered based on the similarity of their phylogenetic profiles. Functionally related proteins are found in the same cluster. Both proteins and protein domains can be detected by using this method. The main drawbacks of this method are high cost, dependence on high information profiles and false detection of homology between distant organisms. The most striking example of the false correlation is to detect correlation between ubiquitous unlinked proteins present in all genomes.

Rosetta Stone method
Rosetta Stone method is also known as gene fusion method. Some pairs of interacting proteins have homologs in other organisms which are fused to a single protein chain. This fused protein is called a Rosetta Stone protein. This method detects protein interactions in different genomes. For example Gyr A and Gyr B are separate protein subunits in Escherichia coli where as the homolog of these proteins found in Saccharomyces  cerevisiae is a single protein Topoisomerase-2. Rosetta Stone method is used for predicting about 6.4% of all experimental interactions. In Escherichia coli this method has found about 6,809 pairs of interacting non homologous proteins.

Sequence-based co-evolution methods
This method is based on measuring the evolutionary distance between two different organisms. Co-evolution is the similarity in phylogenic trees of non-homologous proteins. The extent of similarity is quantified by calculating the correlation coefficient between the distance matrices indicating co-evolution of two different protein families. To calculate the correlation coefficient correspondence between the two elements is required which is not always available. Hence several algorithms are developed to identify specific interaction patterns. In co-evolution the changes in one protein which leads to the loss of its function is compensated by the correlated changes in the other gene. There is always some similarity between trees of any proteins due to the speciation process. This is known as "background similarity". It is constructed from the 16S rRNA sequences and the final distance matrix is calculated by subtracting the rRNA based distances from the evolutionary distance obtained from original phylogenetic trees. Widely studied example of co-evolution studies is to predict interaction partners in DNA colicin and their immunity proteins. Co-evolution method is also used for predicting domain interactions.

Classification methods
Classification methods are used for the prediction of both protein and domain interactions. These methods distinguish true interacting protein pairs from the false non-interacting protein pairs by training a classifier with various data sources. There are many classifiers used in the classification methods the most popular classifier is Random Forest Decision followed by Support Vector Machines. Most popular classification method used is Kernel method which provides vectorial representation of the data in the feature space with a set of pair wise comparisons. The feature vectors represent the particular information of a protein such as protein interactions, domain compositions of a particular protein.

Association methods
Association methods are generally assigned to predict domain interactions. Association methods are used to distinguish interacting proteins from non-interacting. Different classifiers such as correlated sequence signatures are used to distinguish this. Correlated sequence signatures considers each pair of interacting domains separately, ignoring other domains present in the protein. These methods distinguish interacting from non-interacting proteins by calculating the log odds score. The log odds score (log2(Pij/PiPj)) is calculated by taking the ratio of observed frequency domains in one protein pair to the background frequencies in the data. The domain interactions are predicted based on the positive log odds score. Based on the occurrence of domains in protein interacting pairs protein prediction can also be done.

Bayesian network models and maximum likelihood methods
This method is used to predict the domain interactions. This is advantageous when compared to association methods as bayesian network models considers missing and incorrect interaction data. Bayesian parameters are estimated by using Maximum Likelihood Estimation method. Maximum Likelihood Estimation method calculates experimental errors in the scoring scheme by maximizing the probability of interactions of the putative domain pairs. The likelihood function(θ) is a function of many parameters such as λij (probability that domains i and j interact), fp (false postive rate), fn (false negative rate), hence it is very difficult to maximize the likelihood function directly. Therefore Expectation Maximization algorithm is used to find the maximum likelihood estimate of unknown parameters by calculating the complete data in two iterative sets 1) observed data: consisting of protein-protein interactions 2) unobserved data: consisting of non protein-protein interactions.

Domain pair exclusion analysis
This method is advanced to maximum likelihood estimation method which predicts non specific interactions. Domain pair exclusion analysis detects specific and rare interactions between the proteins which have a lower θ value. These interactions are estimated by calculating the Eij score which is the logarithmic ratio of the probability that two proteins interact given the domains i and j interact to the probability that the two proteins interact given i and j do not interact. The probability in the numerator is calculated by using Expectation Maximization procedure and the probability in the denominator is calculated by repeating the procedure where the probability of domain pair interactions is zero. Higher E-scores indicate the high possibility of domain interaction while the lower values indicate that the competing domains are responsible for interactions. Thus domain pair exclusion analysis detects the interactions with low θ and high Eij values. Though it does not detect the false positives and false negatives it detects true positives to a considerable extent.

p-Value method
The null hypothesis of this method tests whether the presence of a domain in a protein pair effects the protein interactions. The statistic in p-Value method is calculated by considering two factors. They are the fraction of false positives known as experimental error and the fraction of false negatives known as incompleteness of the dataset. An ideal reference distribution for this method is obtained by shuffling the domains in proteins such that the protein interactions remain stable. The p-statistics then obtained considering the reference distribution indicate the reliability of domain interactions given that the two proteins interact. There is a inverse relationship between the value of p-statistics and the interaction of the proteins. Given that two proteins interact the domain pair with the lowest p-value is most likely to interact. The p-value method works well if there are nine or more domains in a given protein pair.

Protein Interactions Calculator
Protein Interactions Calculator (PMID:17584791)is a server which recognizes various kinds of interactions; such as disulphide bonds, hydrophobic interactions, ionic interactions, hydrogen bonds, aromatic- aromatic interactions, aromatic-sulphur interactions and cation - π interactions within a protein or between proteins in a complex. It also determines the accessible surface area as well as the distance of a residue from the surface of the protein. The input should be in the Protein data bank(.pdb) format. Interactions are calculated based on empirical or semi-empirical set of rules. All the interactions and bonds can be seen in a single site and also the identified interactions/bonds between residues can be visualized using a RasMol and Jmol interface. URL: http://crick.mbu.iisc.ernet.in/~PIC/

DOCK
DOCK software is compatible with UNIX based platforms. DOCK generates many possible orientations of a putative ligand in a user selected region of a receptor structure. These orientations are scored using several scoring schemes which are designed to measure steric and chemical complementarity of the receptor-ligand complex. DOCK has many applications such as detecting the binding orientation of protein-protein complexes, protein-DNA complexes, evaluating likely orientations of a single ligand, ranking molecules from a database. It also searches different databases for the compounds which acts as enzyme inhibitors, DNA binding compounds, compounds binding to receptor etc., DOCK uses a geometric matching algorithm to assess rigid receptor docking by superimposing the negative image of the ligand onto the binding pocket. The official website of DOCK is http://dock.compbio.ucsf.edu/

autoDOCK
AUTODOCK is composed of many automatic docking tools. It predicts how substrates and drug candidates bind to a receptor or protein of known 3D structure. It has two main programs. The first program performs the docking of the ligand to a set of grids describing the target protein. Second program is the AutoGrid which precalculates these grids. AUTODOCK is also compatible with the UNIX based platform. AUTODOCK has applications in many fields such as X-ray crystallography, structure-based drug design, protein-protein docking, lead optimization, combinatorial library design etc.,

ICM (Internal Coordinates Mechanism)
ICM uses bond length, torsion angles, bond angles to determine structural predictions. It does fast and accurate docking simulations. It has a unique set of tools for accurate ligand-protein docking, peptide-protein docking, and protein-protein docking including interactive graphic tools. Visit the Abagyan lab at: http://abagyan.scripps.edu/lab/web/man/frames.htm or www.molsoft.com to learn more.

FleXX
FleXX is a high speed computer program used to determine binding mode. It detects the protein-ligand complex within seconds if the three dimensional structure of the protein is known. FLEXX estimates  the binding affinity of protein-ligand complex by predicting the geometry of protein-ligand complex from the known 3D structure of the protein. FLEXX has 2 main applications in protein predictions. They are 1) Complex prediction: It is used when a protein and a small molecule is present without the knowledge of the structure of the protein ligand complex. It creates and ranks a series of possible protein-ligand complexes. 2) Virtual Screening: It is used for prioritizing the compounds for experimental testing from the set of compounds and a given protein. Placement algorithm in FLEXX is based on interactions between two molecules and Boehm function is applied for scoring. Two databases are widely used in FLEXX to predict protein-ligand interactions. They are 1) MIMUMBA torsion angle database: It is used for the creation of the conformers 2) Interaction geometry database: It is used to describe exactly intermolecular interaction patterns. The advantages of this software are it is best enrichment tool in structure based drug design,generating poses of lead structures in a protein binding site and docking gigantic libraries by using high speed docking. Download the program at: http://www.biosolveit.de/download/

GRAMM (Global RAnge Molecular Matching)
GRAMM is an empirical approach for smoothing intermolecular energy function by changing the range of the atom-atom potentials. It is a free program for protein docking used for high resolution and inaccurate studies. It predicts structure of the complex based on the atomic coordinates of two molecules and by doing exhaustive 6D search through relative translations and rotations of the molecules. The complex may consist of two proteins, a protein and small molecule, two transmembrane helices etc., GRAMM is compiled on SGI series, SUN SPARC, IBM RS6000, DEC Alpha and PC. To view the GRAMM website: http://reco3.musc.edu/gramm/

GRAMM X
GRAMMX is the extended version of GRAMM. It is done by updating smoothed potentials, refinement stage and knowledge based scoring. Docking problems are processed by a 320 processor Linux cluster. GRAMMX can be implemented in Python and C++. Perform a GRAMMX simulation at: http://vakser.bioinformatics.ku.edu/resources/gramm/grammx/

FTDOCK
FTDock is a free program which performs rigid-body docking on two biomolecules in order to predict their correct binding geometry. FTDock outputs multiple predictions that can be screened using biochemical information. This software runs on RedHat Linux and pentium based platforms. FTDock used Fourier Transforms to determine docking. Download the program at: http://www.bmm.icnet.uk/docking/download.html

ZDOCK
It is a computational technique where the structure of a complex between two proteins is predicted based on the independently crystallized structures of the components. ZDOCK uses a fourier transform to search all possible binding modes for the proteins, evaluating based on shape complementarity, desolvation energy and electro statistics. Visit the Boston University site at :  http://zdock.bu.edu/software.php

Further Information

 * Monte Carlo method: http://www.desy.de/~heramc/mclist.html
 * Accessible Surface Area:
 * http://wolf.bms.umist.ac.uk/naccess/naccess.html
 * http://roselab.jhu.edu/utils/unfolded.html
 * CAPRI home page: http://capri.ebi.ac.uk/
 * TESS at: http://www.biochem.ucl.ac.uk/bsm/PROCAT/manual/man1.html
 * FADE and PADRE: the San Diego Supercomputer Center http://www.sdsc.edu/CCMS/FP/
 * CAST: http://sts.bioengr.uic.edu/castp/

References (Open Access)

 * 1)   Wikipedia page on Protein-protein docking (Monte Carlo section): http://en.wikipedia.org/wiki/Protein-protein_docking#Monte_Carlo_methods

 * 1)  NACCESS S.Hubbard and J.Thornton. 1992. http://wolf.bms.umist.ac.uk/naccess/naccess.html

 * 1)  CAPRI  community wide experiment on the comparative evaluation of protein-protein docking for structure prediction http://capri.ebi.ac.uk/

 * 1)  Wallace AC, Borkakoti N, Thornton JM. 1997. TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases.

 * 1)  Wikipedia page on Delaunay triangulation: http://en.wikipedia.org/wiki/Delaunay_triangulation

 * 1)  Mitchell, J.C., Kerr, R. and Ten Eyck, L.F. 2001. Rapid atomic density measures for molecular shape characterization. J.Mol. Graph. Model. 19(3): 324-329,2001.

 * 1)  Joe Dundas, Zheng Ouyang, Jeffery Tseng, Andrew Binkowski, Yaron Turpaz, and Jie Liang. 2006. CASTp: computed atas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucl. Acids Res., 34:W116-W118.

 * 1)  Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y and Liang J. 2006. CASTp:computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues.

 * 1)  Marcotte, E.M., Pellegrini, M., Ho-Leung, N., Rice, D.W., Yeates, T.O., Eisenberg, D.  1999.  Detecting protein function and protein-protein interactions from genome sequences. Science 285:751–753.

 * 1)  Shoemaker, B.A., Panchenko, A.R. 2007 Computational Methods to Predict Protein and Domain Interaction Partners. PLoS Comput Biol 3(4): e43.

 * 1)  Timothy Palzkill, Proteomics, Kluwer Academic Publishers. http://books.google.com/books?id=JoEgI1a3yrAC&pg=PA78&lpg=PA78&dq=Gene+neighbor+method+in+proteomics&source=bl&ots=k7ByV84JZf&sig=wSdaO0l91A9BX-cFt9xwjQil3o4&hl=en&ei=034BSvLoBomeM4PclOcH&sa=X&oi=book_result&ct=result&resnum=1#PPA76,M1

 * 1)  K. G. Tina, R. Bhadra and N. Srinivasan. 2007. PIC: Protein Interactions Calculator, Nucleic Acids Research,  Vol. 35, Web Server issue W473–W476.

 * 1)  BioSolveIT http://www.biosolveit.de/FlexX/

 * 1)  University of Illinois at Urbana. http://www.ks.uiuc.edu/Development/biosoftdb/biosoft.cgi?&category=7

 * 1)  Structure based drug design and molecular modelling. http://www.imb-jena.de/~rake/Bioinformatics_WEB/dd_tools.html

 * 1)  Boston University Bioinformatics. http://zlab.bu.edu/zdock/

 * 1)  Delaunay triangulation. http://en.wikipedia.org/wiki/Delaunay_triangulation

 * 1)  The Scripps Research Institute. http://www.scripps.edu/mb/olson/doc/autodock/


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User:Dilaudid

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title: User:Dilaudid


text: Wikibooks:Book of the month/May 2006

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Template:German/Grammar/Footer

(discussion) Grammar

Introduction and overview • Basic terminology • Personal pronouns, formal and informal you, introduction to gender • Intransitive verbs, verb conjugation, present tense • Adverbs, V2 word order, Negation of verbs, Sentence adverbs • Stem-changing verbs, Weak vs. Strong verbs • Polar questions, V1 word order, Pre- and postambles • Noun gender • Noun plurals • Noun phrases, Articles • Transitive verbs, Accusative case, word order • Pronomial possessives, Possessive determiners, Possessive pronouns, Negation with kein • Irregular verbs, Past-like present verbs • Uninflected adjectives, Predicate phrases, Copulative verbs • Interrogatives, der words • Future tense, The sentence bracket • Ditransitive verbs, Dative case • Coordinating conjunctions, Ellipses, Adverbial conjunctions, Multipart conjunctions • Prepositions with accusative and dative, Prepositional verbs • Prefixed verbs, Separable verbs, Separable and inseparable prefixes • Imperatives, The imperative mood, The impersonal imperative • Impersonal verbs, Impersonal pronouns, the Point-of-view dative • Dative prepositions • Possessives and the genitive case • Modal auxiliary verbs, Compound verbs, Modal adverbs • Demonstratives • The simple past tense • Reflexive pronouns and reflexive verbs • Subordinating conjunctions • Declining adjectives

Adjectives and Adverbs • Alphabet • Cases • Nouns • Prepositions and Postpositions • Pronouns • Sentences • Verbs
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German/Grammar/Pronouns

Note: The possessive is not a case of the personal pronoun; it's a possessive determiner, called possessive adjective. This table shows the possessive adjective's stem, which is declined as an ein- word (that is, like the indefinite article "ein").

The genitive case indicates possession or association, and is equivalent to, and replaces, the English word "of". "Des" and "der" (do not confuse with masculine singular nominative) mean "of the"; "eines" and "einer" mean "of a" / "of an"; and "der Sohn guten Weins" means "the son of good wine" (no article, M, Gen strong adjective). Strict replacement of the genitive case with the word "of" maintains the word-order of the German nominal phrase: possessed - possessor (in genitive). The genitive case also replaces "'s" in English, though reversing the word order (possessed then possessor, vs. English: possessor then possessed). German itself also uses an "s" (though without the apostrophe) to indicate possession, in the same word order as English. It is used mainly with proper nouns, such as "Goethes Heimat", as well as for compounding words.

Standard genitive constructions are used with nouns and modifiers of nouns such as articles and adjectives, and the inflection they receive implies possession. The first noun may be in any case and may occur in any part of the sentence; the second noun, which possesses the first noun, immediately follows the first noun, and is in the genitive case. The noun in the genitive case need not have any modifiers - e.g., Heimat Goethes, Heimat Katerina, which mean the homeland of Goethe and Katerina, respectively - though such constructions can be cumbersome and ambiguous.

Proper treatment of the genitive case, including all of the declensions, is found in another part of this book.

German pronouns have genitive forms, but they are used only rarely nowadays, mostly in archaic or formal German. In many cases, a preposition can be added to allow a different case to be used. Ich erinnere mich ihrer. (I remember her) Also possible: Ich erinnere mich an sie.

Wir gedachten seiner. (We thought of him) Also possible: Wir dachten an ihn.

Herr, erbarme dich unser! (Lord, have mercy upon us) Also possible: Herr, erbarme dich über uns.

The possessive pronouns (mein-, dein-, unser-, etc.) are almost identical in form to the genitive pronouns but they directly modify their attribute and could be conceived of as adjectives, though they decline differently. Alternatively, one could think of possessive pronouns, for example, "mein-", as replacing the phrase, "of me". Directly translated, "mein-" means "mine" in English.

Examples:

I want the teacher's book. Let's rewrite this as: I want the book of the teacher. -Ich will das Buch des Lehrers (der Lehrerin). --The genitive case here is masculine (feminine) singular, inflecting the definite article (der/die inflected to des/der) as well as the noun (Lehrer [+s], but not Lehrerin, which doesn't change because it is feminine).

Without his friend's car, we cannot go home. -Ohne den Wagen seines Freundes können wir nicht nach Hause fahren. --Here, two possessive relationships are mentioned. The car belongs to the friend, and the friend belongs to "him". For illustrative purposes, one could conceivably rewrite the prepositional phrase as "without the car (accusative case) of the friend of him". German's rendering is far less awkward.

The wall of the building is old and brown. -Die Wand des Gebäudes ist alt und braun. --As in the first example, the genitive case here is in the neuter singular, and inflects the definite article and the noun (M,N add +s/+es in the genitive case).

= Comparison of Pronouns to other Parts of Speech =

Despite the difficulty many people have in learning German declensions, case endings in German correspond to each other to a considerable degree. Specifically, the pronouns bear an obvious resemblance to their parent direct articles. Learning the corresponding third-person declensions side by side allows some people to comprehend the declension pattern more easily.

As discussed above, possessive pronouns replace the genitive case for pronouns. In this table, they will be placed where the genitive case is, so that their similarities to other parts of speech that actually are in the genitive case can become clear.

German is very rigorous in its use of gender, and will use the pronoun corresponding to the gender of the referential noun, regardless of whether the noun being referenced is a person (unlike English, which uses "it" for everything not a person or other entities (animals, ships) in certain contexts). Der Liberalismus will be referred to as "er", or "he", whereas "das Mädchen" would be "es", or "it". Many English speakers have trouble with this, especially in spoken language. Mastery is nonetheless possible with a proper understanding of German declension, use of a few rules of thumb (for example, nouns ending in "-chen" are usually neuter), and a considerable amount of practice.

N.B.(1) The use of "wessen" is considered old-fashioned, though most Germans would find it endearing to hear a non-native speaker use the word. One is encouraged to use the "gehören + dativ (wem?)" construction, which means "to belong to someone (whom?)".

N.B.(2) The dative plural. Except for words whose plural form adds an "-s" (mainly loan words), and words whose plural form already ends in "-n"/"-en", all nouns add an "-n/-en" in the dative plural. Like the s's added to masculine and neuter nouns in the genitive, this is a remnant from when German inflected all of its nouns. Other languages based on declension, such as Russian and Latin, retain that characteristic. Sometimes one will notice an "-e" after masculine and neuter nouns in the dative case, such as the dedication on the Reichstag building - "Dem deutschen Volke", "for the German People".

This nominal declension is reflected in the dative plural pronoun (to/for them), "ihnen", instead of "ihn" (masculine, accusative). For example,

Helga: Können Sie bitte meinen Brüdern helfen? Olga: Natürlich, aber ich kann ihnen leider nur nach zwei Tagen helfen. Helga: Unsere Leben gehen trotzdem weiter.

Make a point of studying and getting used to the dative plural.

Alemão/Gramática/Pronomes

title: German/Grammar/Pronouns


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German/Grammar/Prepositions and Postpositions

German has dative, accusative, genitive and two-way prepositions and postpositions. Each preposition causes the adverbial expression on which it acts to take the case of the preposition. Two-way prepositions cause the adverbial expression to take the accusative case if the verb indicates an action or movement, and the dative case if the verb refers to something that is not changing location.

Notes:

Gegenüber is one of the rare postpositions which typically follows the object it modifies.

Er stand mir gegenüber. Mir gegenüber steht Außenminister Fischer.

However:

Gegenüber von Ihnen befindet sich das Stadtmuseum.

Nach is also sometimes used as a postposition, when its meaning is "according to". The two phrases are equivalent:

Nach dem Pfarrer sei Gott gut. Dem Pfarrer nach sei Gott gut.

In spoken language, the genitive with prepositions is nowadays often replaced by the dative. But it is important to notice that this replacement is still just colloquial language, e.g.:

Written: Während des Essens wollen wir nicht gestört werden. Spoken: Während dem Essen  wollen wir nicht gestört werden.

Two way Prepositions (Wechselpräpositionen)
The case with two way prepositions should be determined with questions like wo? (where?), wann? (when?), wohin? (where does it go to?) and wie? (how?).

Contractions
Some combinations of articles and prepositions are usually combined:

In all other cases the preposition and articles have to be written separately.

Verbs with prepositions
Several verbs are used with a specific preposition and case:


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German/Grammar/Verbs

Verbs
German verbs can be classified as weak or as strong. Weak verbs are very regular in their forms, whereas strong verbs change the stem vowel.

Weak: kaufen, kaufte, gekauft

Strong: singen, sang, gesungen

With its Anglo-Saxon origin, this notion is also present in English.

flip, flipped, flipped sing, sang, sung

Some German verbs have weak and strong forms. This may depend on meaning:

Der Botschafter wurde nach Berlin gesandt. Der Süddeutsche Rundfunk sendete ein Konzert aus dem Gasteig.

Or on transitive vs. intransitive use:

Das Hemd hing auf der Wäscheleine. Sie hängte das Hemd auf die Wäscheleine.

Strong Verbs

 * Liste der starken und unregelmässigen Verben

Separable Verbs
Sometimes you will run into verbs such as anrufen, aufräumen, mitkommen. These verbs are examples of Separable Prefix Verbs. When you see these kinds of verbs, it will have a preposition prefix followed by a verb. These verbs separate when they are the main verb of a sentence.

EXAMPLES:

I am calling the butcher. Ich rufe den Metzger an.

I am trying on the boots. Ich probiere die Stiefel an.

Reflexive Verbs
Reflexive Verbs are verbs involving the reflexive pronoun "sich" and its conjugations that reflect, or refer back, to the performer of the action. There are only accusative and dative reflexive pronouns.

Accusative reflexive pronouns are used when there is no direct object. Dative reflexive pronouns are used when a direct object is present. However, when using a direct object, the possessive is not used.

Examples:
 * Accusative: Ich verletze mich. I injure myself.
 * Dative: Ich verletze mir die Hand. I injure my hand.

 * Accusative: Er hat sich verbrannt. He burned himself.
 * Dative: Er hat sich den Daumen verbrannt. He burned his thumb.

Notice that all reflexives are the same as the Akkusativ and Dativ Pronoun Declensions — except for 3rd Person and 2nd sg./pl. Person formal (man/sie/Sie), in which case all reflexives are sich.

Dürfen
Dürfen means to be allowed/permitted, may.

Examples: Darf ich einen Freund zum Fest bringen? May I bring a friend to the party? Man darf hier nicht rauchen. One is not allowed to smoke here. Niemand durfte die Stadt verlassen. No one was allowed to leave the city.

Können
können means 'to be able, capable'. It is cognate with the English word 'can'/'could'.

Examples: Ich kann das nicht tun. I can't do it. Wir konnten sie nicht erreichen. We could not reach them.

Mögen
mögen expresses a pleasure, or desire. In the present tense, it is used transitively with people or food. e.g. 'Ich mag dich' 'I like you' or 'Ich mag Erdbeeren' 'I like strawberries'. The subjunctive (of the past) expresses preference to perform the action of a subordinate clause 'Ich möchte nach Frankreich reisen' 'I would like to travel to France'. 'mögen' is cognate with the English verb 'may'/'might'.

Example: Ich möchte nach Deutschland reisen. I would like to travel to Germany.

(There is also a present subjunctive möge, which is very formal: Der König sagte: "Er möge eintreten." - The king said: "He may enter.")

Müssen
müssen expresses something forced on you. It is etymologically related to 'must'.

Examples: Ich muss nicht arbeiten. ~ Ich brauche nicht zu arbeiten. I don't have to work. Ich darf nicht arbeiten. I must not work.

Note that the negative nicht müssen is not the English must not, but rather need not or don't have/need to. must not translates to nicht dürfen.

There are however some northern German uses like: Du musst das nicht tun meaning Du solltest das nicht tun.

Sollen
sollen expresses an obligation or duty. It is etymologically related to 'shall'.

Wollen
wollen means to want.

Use in Perfect (and Pluperfect) Tense
Although all these modals have a normal perfect: gedurft gekonnt gemocht gemusst gesollt

in connection with other verbs, the infinitive form is used: Ich habe das tun dürfen - können - mögen - müssen - sollen.

Wrong: Ich habe das tun gedurft - gekonnt - gemocht - gemusst - gesollt.

It holds also for the verbs sehen and hören: Ich habe ihn kommen sehen - hören.

Use of modal verbs as full verbs
Modal verbs can be used as full verbs indicating motion. In these cases, the infinitive verb is only implied. Er muss nach Berlin. (implied: gehen or fahren) He must go to Berlin.

Use
The Present Tense is used for..

 * The Present Tense (="das Präsens") is used to describe situations that are happening and aren't the past.

 * For Ongoing Action, like I'm swimming in the pool now

 * Everyday Truths, like The moon and stars will come at night.

 * Future meaning, if explicitly stated, like I will run tomorrow morning

 * Actions started in the past and still going on in the present I've been cleaning the house all day

Progressive Forms
There is a present progressive tense in colloquial spoken German. Its use is optional.

Here is one example:

Ich bin am Fahren. (I am at the driving) I'm driving.

The person to say this would be driving during the time they say this and they would continue to drive after stating this for some time.

You nominalize the verb ("fahren" (driving) becomes "das Fahren") and add a "am". You can also do this with forms of the past.

Als er kam war ich gerade am Abwaschen. (When he arrived i was at "the dishwashing") I was washing the dishes when he arrived.

So the verb "sein" (to be) includes the information what tense he was doing what he did in.

Here the progressive meaning is also emphasized with the word "gerade" meaning something like: I was JUST ABOUT to wash the dishes(not the same though because it means he is already doing it and not about to start).

Perfect Tense
The Perfect Tense or das Perfekt of verbs is used to talk about things in the past which have already happened. It is sometimes referred to as "Present Perfect Tense". This can cause confusion. While the formation is similar, the meaning and usage differs.

Formation
As in English, the perfect tense consists of two parts. An auxiliary (Hilfsverb) and a past participle (Partizip Perfekt). Compare the examples given below with their English translations.

Past participle for regular verbs
The general rule is simple:

There are some groups of regular verbs that slightly differ from that pattern.

Some verbs drop the prefix ge-. Like the other regular verbs they end in -t. These are:

1. Verbs with unseparable prefixes (be-, ent-, er-, empf-, ge-, ver-, miss-, zer-) Examples:

2. Verbs ending in -ieren Examples:

3. Another group is formed by verbs with separable prefixes With separable verbs, the prefix ge is placed between the prefix and the rest of the verb. Examples:

Separable and inseparable verbs are distinguished by the stressed syllable:

Er hat das Buch ins Chinesische übersetzt. Der Fährmann hat den Passagier übergesetzt (über den Fluss gesetzt).

Past Participle for Irregular Verbs
Irregular verbs always end in -en. The vowel can be different from the one in present tense. Look at some examples:

You have to learn these forms by heart. How you can obtain the necessary information and how you should learn them is described in section tips for learning below.

Note that irregular verbs can be combined with the same prefixes as described above. The same rules regarding the prefix ge- apply. Therefore the forms for schreiben, verschreiben and aufschreiben are geschrieben, verschrieben and aufgeschrieben respectively.

Which verbs are irregular
A lot of verbs that are irregular in English are irregular in German, too. Unfortunately, this is not always true. It is most likely when the German and the English verb are related (i.e. look similar).

Examples: see:   irregular   sehen:    irregular buy:   irregular   kaufen:   regular get:   irregular   bekommen: irregular ;-)

Regular verbs are much more frequent than irregular ones, but a lot of the irregular verbs are used very frequently, for instance haben, sein, gehen, kommen etc.

When in doubt whether a verb is irregular or not, it is best to look it up in a dictionary (See below).

Haben or sein as auxiliaries
Whether a verb is irregular or not does not influence the choice of auxiliary.

Most verbs take haben as auxiliary.

A) Verbs which take an accusative object (transitive verbs) B) Reflexive verbs always take haben as auxiliary.

Examples A: trinken: Er hat ein Bier getrunken. lesen:  Sie hat ein Buch gelesen kochen  Sie haben gestern Spaghetti gekocht.

Examples B: sich freuen  Ich habe mich gefreut sich kämmen  Er hat sich gekämmt sich ärgern  Wir haben uns schon lange nicht mehr so geärgert.

The auxiliary sein is taken by verbs that describe
 * C) the relocation from one place to another or
 * D) the change of a state
 * and with
 * E) sein (be) and bleiben (stay)

Note: none of the verbs from groups C-E is combined with an accusative object.

Examples C: relocation verbs verb              aux. irregular sentence with perfect tense kommen (come)     sein  yes        Ich bin gekommen. reisen (travel)   sein  no         Wir sind schon dreimal nach China gereist. fahren (drive)    sein  yes        Ich bin mit dem Auto nach Kalifornien gefahren. begegnen (meet)   sein  no         Er ist ihm gestern begegnet. gehen (go)        sein  yes        Du bist gegangen. starten (take off) sein yes        Das Flugzeug ist gestartet.

In southern German (mostly Bavarian) use, also stehen, sitzen und schwimmen are treated like a (non-)movement: Ich bin gestanden - gesessen - geschwommen. High German is: Ich habe gestanden - gesessen - geschwommen. Aber: Ich habe den See durchschwommen.

Examples D: change of state verbs verb                       aux. irr. sentence with perfect tense aufstehen (get up)         sein   yes     Ich bin heute früh aufgestanden. einschlafen (fall asleep)  sein   yes     Die Kinder sind endlich eingeschlafen. verblühen (whither)        sein   no      Die Blumen sind schon verblüht

Examples E: sein and bleiben Er ist nicht lange geblieben. He didn't stay long. Er ist immer nett gewesen. He has always been nice.

Exceptions to the rules Some of the verbs from group A can be used with an object in accusative case. In this case, they take haben as auxiliary.

Compare: Ich bin nach Kalifornien gefahren. I drove to California. Ich bin mit dem Auto nach Kalifornien gefahren. I drove to California by car (literally: with the car) Ich habe das Auto (Akk.) nach Kalifornien gefahren. I drove the car to California.

The same applies to fliegen (fly), starten and reiten (ride a horse).

Usage
Unlike in English the difference in meaning between Perfekt and Präteritum is rather small. The main difference between those two forms lies in usage. Perfekt is mostly used in spoken language, while Präteritum is mostly reserved for written texts. However, the modals, the verbs haben and sein and the expression es gibt are almost exclusively used in Präteritum - even when speaking. One reason might be the frequency of those verbs, the other reason is most likely the very complex perfect forms for modals.

(This is in southern German use; in northern German, you'll hear the preterite also in spoken language.)

On the other hand, the perfect tense is used in writing too. The more oral the text is, the more perfect tense you will find (for example in personal letters etc.). If an action has happened very recently, it tends to be in perfect tense too.

Look at the following conversation and concentrate on the distribution of Präteritum and Perfekt.

(1) Anna: Hallo Peter. Wo warst du denn? Ich habe dich schon lange nicht mehr gesehen. (2) Peter: Hallo Anna. Ich war die letzen zwei Wochen im Urlaub. (3) Anna: So? Wo warst du denn genau? (4) Peter: Auf der Insel Elba, in einem fantastischen Hotel. Es gab jeden Abend ein Büffet und man konnte essen, so viel man wollte! (5) Anna (lacht): Ich glaube dir sofort, dass dir das gefallen hat. Du hast aber nicht nur gegessen, oder? Was hast du denn den ganzen Tag gemacht? (6) Peter (lacht auch): Nein, natürlich nicht. Ich bin viel geschwommen, ich habe mir die Insel angeguckt und am Abend bin ich immer zum Tanzen in eine Disco gegangen. (7) Anna: Aha... Und? Hast du jemanden kennen gelernt? (8) Peter (grinst): Kein Kommentar.

Vocablary to help you understand the text: der Urlaub, -e  vacation genau           exactly, precisely die Insel, -n   island das Büffet, -s  buffet gefallen        like angucken        to look at (colloquial) kennen lernen   get to know grinsen         grin

Used forms to talk about past events Präteritum        Perfekt du warst (1/3)    habe gesehen (1) ich war (2)       es hat gefallen (5) es gab (4)        du hast gegessen (5) konnte (4)        du hast gemacht (5) wollte (4)        ich bin geschwommen (6) ich habe angeguckt (6) ich bin gegangen (6) du hast kennen gelernt

How to find the forms in a dictionary
Unless you have a special dictionary for learners, not all the forms will be spelled out. Regular forms are often omitted. The same goes for the auxiliary haben. If no forms are indicated, you may assume that the verb is regular and has the verb haben as an auxiliary. However, if you find the abbreviation itr or i. (for intransitive) behind the verb, the auxiliary is often sein. Intransitve verbs don't have an accusative object and these are often used with sein, while transitive verbs (tr. or t.) are always conjugated with haben.

Sometimes not even the forms of irregular verbs are given in the lexicon entry. Irregular verbs are often indicated by irr. for irregular or a similar abbreviation. In that case, look for a list of irregular verb forms in the index of your dictionary.

To find the past participle of separable verbs you often have to cut the prefix and look for the base form of the verb. If you look for aufstehen (get up), you probably find your answer in the entry of stehen. Remember: The prefix ge goes in between the prefix of the separable verb and the verb itself: auf + ge + standen.

When working online, you might consider using Canoo. Enter an arbitrary form of the word you are interested in into the mask. Hit enter. On the results page, choose the link Flexion behind the appropriate entry (or inflection in the English version). You will get a table of all possible verb forms.

Tips for learning
Irregular forms are just that - irregular. Therefore you have to learn them by heart. By learning four forms, you can construct every verb form for a given verb.

The forms you should know are: Infinitiv   Präsens          Präteritum    Hilfsverb  + Partizip Perfekt infinitiv   3rd person       preterite     auxiliary  + past participle gehen       geht             ging          ist        + gegangen nehmen      nimmt            nahm          hat        + genommen fahren      fährt            fuhr          ist        + gefahren lesen       liest            las           hat        + gelesen essen       isst             aß            hat        + gegessen kommen      kommt            kam           ist        + gekommen bleiben     bleibt           blieb         ist        + geblieben sein        ist              war           ist        + gewesen anfangen    fängt ... an     fing ... an  hat        + angefangen ...

All forms - besides the infinitive of course - should be in 3rd-person singular.

A good way to learn those forms is to put them on small cards. On one side you write the infinitive and probably a sentence to illustrate the usage of the verb. On the backside you put the rest of the forms and - if needed - a translation of the verb. When learning, you look at the infinitve and try to remember the forms and the meaning. You can easily verify your hypothesis by flipping the card.

If you encounter a verb you want to learn, look it up in a dictionary. If it is irregular, learn the verb together with its defining forms. Like that, you spare yourself a lot of trouble later on.

Sentence Structure
The perfect tense consists of two verb forms: an auxiliary and a past participle. Together they form the so called predicate. The predicate consists of all verb parts in one clause.

The sentence structure in perfect behaves as with every two parts predicate (modals plus infinitive, separable verbs etc.)

Main Clauses
In a main clause (Hauptsatz), the conjugated verb (the auxiliary in this case) is in the second position and the past participle stands at the end of the clause.

First Position (I)      (II) 1) Sein Vater              hat  gestern ein fantastisches Essen gekocht. 2) Gestern                  hat  sein Vater ein fantastisches Essen gekocht. Both: Yesterday, his father cooked a fantastic meal. 3) Ein fantastisches Essen hat  sein Vater gestern gekocht.*    It was a fantastic meal that his father cooked yesterday. * The third example is correct, although not very frequent. You might use it if you want to stress what exactly his father has prepared or if you have to repeat the sentence because your partner has not understood this particular part of it.

Second position does not equal second word, as you can see above. However, there is only one group of words allowed before the conjugated verb (the auxiliary in this case). Such groups of words are called "phrases". While you can put very long phrases in front of the conjugated verb, you must not use two. Therefore the sentence "Gestern sein Vater hat ein fantastisches Essen gekocht" is wrong.

Subordinated Clauses
Subordinated clauses begin with a subordinating conjunction. Well known conjunctions of this kind are weil dass  wenn.

* In spoken language weil is often used like und or aber, which means that it is followed by a main clause. However, after weil, speakers often pause for a little while. There is no pause after either und or aber. Weil + main clause is not allowed in written language. Therefore you may say: Ich gehe, weil - (little pause) - ich bin müde. But you wouldn't use it in a letter. At least not yet. The correct conjunction for a main clause is denn, which is rarely used in spoken language.

In subordinated clauses the conjugated verb, i.e. the auxiliary, stands at the very end of the sentence. The past participle stands directly in front of it. For example: conj. aux. participle aux. Ich weiß,        dass  du      das             gemacht     hast. Ich glaube dir,  weil  du      bisher noch nie gelogen     hast. Ich glaube dir,  denn  du hast bisher noch nie gelogen. Ich gehe,        wenn  du                      gegangen    bist.

Regular verbs
Regular (or better, weak) verbs take the ending -te. The person endings are added afterwards. Note that the forms for 1st- and 3rd-person singular are the same.

If the stem of a verb (infinitive minus -en) ends in -t (arbeit-en), -d (end-en) or consonant plus m or n (öffn-en, rechn-en) you add an -e before the preterite endings.

Without -te
The strong verbs belong to this group. The endings are easy to memorize. It is harder to know which vowel to use. The rule mentioned above for t/d, double-consonant + n/m applies also for irregular verbs.

gehen, ging, gegangen stehen, stand, gestanden

With -te
Few irregular verbs take the -te ending. Examples are: nennen, rennen, kennen, bringen, denken and the irregular modals (können, dürfen and müssen).

Talking about future with the present tense
German uses the Present Tense to talk about the future whenever it is clear to both speaker and listener that the future is meant. In the dialogue example:

Wenn du zu Hause bleibst, kommen wir dich besuchen. If you stay at home, we shall come and visit you.

The whole conversation is about the future, so there is no need to indicate it again in the tense of the verb.

Some more examples:

Ich schreibe den Brief heute Abend. I will write the letter this evening. Wir gehen nächstes Jahr nach Spanien. We will go to Spain next year.

Futur I
Where the meaning would not otherwise be clear, and in more formal language, e.g. to express an intention, German talks about the future tense by using werden plus the infinitive at the end of the clause. The forms of werden are: ich werde du wirst er/sie/es/man wird wir werden ihr werdet sie/Sie werden

Examples: Ich werde ein Haus bauen. I shall build a house. (an intention) Wir werden sehen. We will see.

The future can also express some inescapable fate: Sie werden alle umkommen. They will all perish.

Future II
The Future II is formed with added "sein" oder "haben" and expresses that one action will happen before another one. Wenn sie das Abendessen gekocht haben wird, werden sie kommen. When she will have cooked the dinner, they will come. In the colloquial language, the perfect is often used for that.

In the colloquial language expresses the Future II often a speculation about the past.

Sie werden angekommen sein. literally "they will have arrived" - meaning "(I gather) they have arrived (by now)"

Sie werden es gemacht haben. "they will have done it"

In the colloquial language, the Futur II is normally used when speaking about something that should have happened already, but you are not sure or you can't prove it.

Alemán/Gramática/Verbos Germană/Gramatică/Verbe

title: German/Grammar/Verbs