Optimal docking area: a new method for predicting protein-protein interaction sites

Understanding energetics and mechanism of protein-protein association remains one of the biggest theoretical problems in structural biology. It is assumed that desolvation must play an essential role during the association process, and indeed protein-protein interfaces in obligate complexes have been found to be highly hydrophobic. However, the identification of protein interaction sites from surface analysis of proteins involved in non-obligate protein-protein complexes is more challenging. Here we present Optimal Docking Area (ODA), a new fast and accurate method of analyzing a protein surface in search of areas with favorable energy change when buried upon protein-protein association. The method identifies continuous surface patches with optimal docking desolvation energy based on atomic solvation parameters adjusted for protein-protein docking. The procedure has been validated on the unbound structures of a total of 66 non-homologous proteins involved in non-obligate protein-protein hetero-complexes of known structure. Optimal docking areas with significant low-docking surface energy were found in around half of the proteins. The 'ODA hot spots' detected in X-ray unbound structures were correctly located in the known protein-protein binding sites in 80% of the cases. The role of these low-surface-energy areas during complex formation is discussed. Burial of these regions during protein-protein association may favor the complexed configurations with near-native interfaces but otherwise arbitrary orientations, thus driving the formation of an encounter complex. The patch prediction procedure is freely accessible at http://www.molsoft.com/oda and can be easily scaled up for predictions in structural proteomics.     

Rapid analysis of large protein-protein complexes using NMR-derived orientational constraints: the 95 kDa complex of LpxA with acyl carrier protein

Characterization of protein-protein interactions that are critical to the specific function of many biological systems has become a primary goal of structural biology research. Analysis of these interactions by structural techniques is, however, challenging due to inherent limitations of the techniques and because many of the interactions are transient, and suitable complexes are difficult to isolate. In particular, structural studies of large protein complexes by traditional solution NMR methods are difficult due to a priori requirement of extensive assignments and a large number of intermolecular restraints for the complex. An approach overcoming some of these challenges by utilizing orientational restraints from residual dipolar couplings collected on solution NMR samples is presented. The approach exploits existing structures of individual components, including the symmetry properties of some of these structures, to assemble rapidly models for relatively large protein-protein complexes. An application is illustrated with a 95 kDa homotrimeric complex of the acyltransferase protein, LpxA (UDP-N-acetylglucosamine acyltransferase), and acyl carrier protein. LpxA catalyzes the first step in the biosynthesis of the lipid A component of lipopolysaccharide in Gram-negative bacteria. The structural model generated for this complex can be useful in the design of new anti-bacterial agents that inhibit the biosynthesis of lipid A.     

Continuum electrostatic methods applied to pH-dependent properties of antibody-antigen association

Protein association events are a critical component of the functioning of biological systems. Antibody/antigen association, which involves extraordinarily specific interactions, has been a paradigm for the study of structural factors and intermolecular forces controlling protein-protein association. As new experimental approaches to the study of antibody/antigen affinity have become routine, and as more structures of complexes of antibodies and their antigens have become available, it has become possible to use computational approaches to study these interactions. Electrostatic interactions are known to play an important role in protein complex formation. In this review, we focus on the use of continuum electrostatic methods to compute pH-dependent properties of proteins and discuss the use of these methods in the study of antibody/antigen complexes.     

Wrapping mimicking in drug-like small molecules disruptive of protein-protein interfaces

The discovery of small-molecule drugs aimed at disrupting protein-protein associations is expected to lead to promising therapeutic strategies. The small molecule binds to the target protein thus replacing its natural protein partner. Noteworthy, structural analysis of complexes between successful disruptive small molecules and their target proteins has suggested the possibility that such ligands might somehow mimic the binding behavior of the protein they replace. In these cases, the molecules show a spatial and "chemical" (i.e., hydrophobicity) similarity with the residues of the partner protein involved in the protein-protein complex interface. However, other disruptive small molecules do not seem to show such spatial and chemical correspondence with the replaced protein. In turn, recent progress in the understanding of protein-protein interactions and binding hot spots has revealed the main role of intermolecular wrapping interactions: three-body cooperative correlations in which nonpolar groups in the partner protein promote dehydration of a two-body electrostatic interaction of the other protein. Hence, in the present work, we study some successful complexes between already discovered small disruptive drug-like molecules and their target proteins already reported in the literature and we compare them with the complexes between such proteins and their natural protein partners. Our results show that the small molecules do in fact mimic to a great extent the wrapping behavior of the protein they replace. Thus, by revealing the replacement the small molecule performs of relevant wrapping interactions, we convey precise physical meaning to the mimicking concept, a knowledge that might be exploited in future drug-design endeavors.     

Functional protein microarray: an ideal platform for investigating protein binding property

Functional protein microarray is an important tool for high-throughput and large-scale systems biology studies.Besides the progresses that have been made for protein microarray fabrication,significant ...     

An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes

Hydrogen bonding is a key contributor to the specificity of intramolecular and intermolecular interactions in biological systems. Here, we develop an orientation-dependent hydrogen bonding potential based on the geometric characteristics of hydrogen bonds in high-resolution protein crystal structures, and evaluate it using four tests related to the prediction and design of protein structures and protein-protein complexes. The new potential is superior to the widely used Coulomb model of hydrogen bonding in prediction of the sequences of proteins and protein-protein interfaces from their structures, and improves discrimination of correctly docked protein-protein complexes from large sets of alternative structures.     

Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines

Biological systems and processes rely on a complex network of molecular interactions. While the association of biological macromolecules is a fundamental biochemical phenomenon crucial for the understanding of complex living systems, protein-protein docking methods aim for the computational prediction of protein complexes from individual subunits. Docking algorithms generally produce large numbers of putative protein complexes with only few of these conformations resembling the native complex structure within an acceptable degree of structural similarity. A major challenge in the field of docking is to extract near-native structure(s) out of the large pool of solutions, the so called scoring or ranking problem. A series of structural, chemical, biological and physical properties are used in this work to classify docked protein-protein complexes. These properties include specialized energy functions, evolutionary relationship, class specific residue interface propensities, gap volume, buried surface area, empiric pair potentials on residue and atom level as well as measures for the tightness of fit. Efficient comprehensive scoring functions have been developed using probabilistic Support Vector Machines in combination with this array of properties on the largest currently available protein-protein docking benchmark. The established classifiers are shown to be specific for certain types of protein-protein complexes and are able to detect near-native complex conformations from large sets of decoys with high sensitivity. Using classification probabilities the ranking of near-native structures was drastically improved, leading to a significant enrichment of near-native complex conformations within the top ranks. It could be shown that the developed schemes outperform five other previously published scoring functions.     

Applications of BRET to study dynamic G-protein coupled receptor interactions in living cells

Summary Protein-protein interactions are fundamental processes for many biological systems including those involving the superfamily of G-protein coupled receptors (GPCRs). When addressing key questions concerning the regulation of GPCR-protein complexes and their functional significance, the development and refinement of non-invasive techniques to study these interactions will be of great value. One such technique, bioluminescence resonance energy transfer (BRET), is a recently described biophysical method that represents a powerful tool with which to measure protein-protein interactions in live cells, in real time. This minireview highlights the impact that evolving techniques such as BRET have had on the study of dynamic protein interactions involving GPCRs. In particular, the application of BRET to the study of protein interactions involving the receptors for hypothalamic peptide hormones, thyrotropin-releasing hormone (TRH) and gonadotropin-releasing hormone (GnRH), will be discussed. Using these receptors, BRET has successfully been used to demonstrate formation of both agonist-dependent and independent GPCR-GPCR complexes (oligomerization) and the agonist-dependent interaction of GPCRs with their intracellular adaptor protein partners, the arrestins. In summary, BRET is a highly snnsitive method that will not only aid in advancing our understanding of GPCR signalling and trafficking bout coud also potentially lead to the development of novel therapeutics that target these GPCR-protein complexes.     

Recombinant Protein Expression for Structural Biology in HEK 293F Suspension Cells: A Novel and Accessible Approach

The expression and purification of large amounts of recombinant protein complexes is an essential requirement for structural biology studies. For over two decades, prokaryotic expression systems such as E. coli have dominated the scientific literature over costly and less efficient eukaryotic cell lines. Despite the clear advantage in terms of yields and costs of expressing recombinant proteins in bacteria, the absence of specific co-factors, chaperones and post-translational modifications may cause loss of function, mis-folding and can disrupt protein-protein interactions of certain eukaryotic multi-subunit complexes, surface receptors and secreted proteins. The use of mammalian cell expression systems can address these drawbacks since they provide a eukaryotic expression environment. However, low protein yields and high costs of such methods have until recently limited their use for structural biology. Here we describe a simple and accessible method for expressing and purifying milligram quantities of protein by performing transient transfections of suspension grown HEK (Human Embryonic Kidney) 293F cells.     

Potential of mean force for protein-protein interaction studies.

Calculating protein-protein interaction energies is crucial for understanding protein-protein associations. On the basis of the methodology of mean-field potential, we have developed an empirical approach to estimate binding free energy for protein-protein interactions. This knowledge-based approach has been used to derive distance-dependent free energies of protein complexes from a nonredundant training set in the Protein Data Bank (PDB), with a careful treatment of homology. We calculate atom pair potentials for 16 pair interactions, which can reflect the importance of hydrophobic interactions and specific hydrogen-bonding interactions. The derived potentials for hydrogen-bonding interactions show a valley of favorable interactions at a distance of approximately 3 A, corresponding to that of an established hydrogen bond. For the test set of 28 protein complexes, the calculated energies have a correlation coefficient of 0.75 compared with experimental binding free energies. The performance of the method in ranking the binding energies of different protein-protein complexes shows that the energy estimation can be applied to value binding free energies for protein-protein associations.     

Charged residues at protein interaction interfaces: unexpected conservation and orchestrated divergence

Protein-protein interactions play an essential role in the functioning of cell. The importance of charged residues and their diverse role in protein-protein interactions have been well studied using experimental and computational methods. Often, charged residues located in protein interaction interfaces are conserved across the families of homologous proteins and protein complexes. However, on a large scale, it has been recently shown that charged residues are significantly less conserved than other residue types in protein interaction interfaces. The goal of this work is to understand the role of charged residues in the protein interaction interfaces through their conservation patterns. Here, we propose a simple approach where the structural conservation of the charged residue pairs is analyzed among the pairs of homologous binary complexes. Specifically, we determine a large set of homologous interactions using an interaction interface similarity measure and catalog the basic types of conservation patterns among the charged residue pairs. We find an unexpected conservation pattern, which we call the correlated reappearance, occurring among the pairs of homologous interfaces more frequently than the fully conserved pairs of charged residues. Furthermore, the analysis of the conservation patterns across different superkingdoms as well as structural classes of proteins has revealed that the correlated reappearance of charged residues is by far the most prevalent conservation pattern, often occurring more frequently than the unconserved charged residues. We discuss a possible role that the new conservation pattern may play in the long-range electrostatic steering effect.     

WHISCY: What information does surface conservation yield? Application to data‐driven docking

Protein-protein interactions play a key role in biological processes. Identifying the interacting residues is a first step toward understanding these interactions at a structural level. In this study, the interface prediction program WHISCY is presented. It combines surface conservation and structural information to predict protein-protein interfaces. The accuracy of the predictions is more than three times higher than a random prediction. These predictions have been combined with another interface prediction program, ProMate [Neuvirth et al. J Mol Biol 2004;338:181-199], resulting in an even more accurate predictor. The usefulness of the predictions was tested using the data-driven docking program HADDOCK [Dominguez et al. J Am Chem Soc 2003;125:1731-1737] in an unbound docking experiment, with the goal of generating as many near-native structures as possible. Unrefined rigid body docking solutions within 10 A ligand RMSD from the true structure were generated for 22 out of 25 docked complexes. For 18 complexes, more than 100 of the 8000 generated models were correct. Our results demonstrates the potential of using interface predictions to drive protein-protein docking.     

Protein-protein interactions: mechanisms and modification by drugs

Protein-protein interactions form the proteinaceous network, which plays a central role in numerous processes in the cell. This review highlights the main structures, properties of contact surfaces, and forces involved in protein-protein interactions. The properties of protein contact surfaces depend on their functions. The characteristics of contact surfaces of short-lived protein complexes share some similarities with the active sites of enzymes. The contact surfaces of permanent complexes resemble domain contacts or the protein core. It is reasonable to consider protein-protein complex formation as a continuation of protein folding. The contact surfaces of the protein complexes have unique structure and properties, so they represent prospective targets for a new generation of drugs. During the last decade, numerous investigations have been undertaken to find or design small molecules that block protein dimerization or protein(peptide)-receptor interaction, or on the other hand, induce protein dimerization.     

Protein-protein interactions as a target for drugs in proteomics

Protein-protein interactions play a central role in numerous processes in the cell and are one of the main fields of functional proteomics. This review highlights the methods of bioinformatics and functional proteomics of protein-protein interaction investigation. The structures and properties of contact surfaces, forces involved in protein-protein interactions, kinetic and thermodynamic parameters of these reactions were considered. The properties of protein contact surfaces depend on their functions. The contact surfaces of permanent complexes resemble domain contacts or the protein core and it is reasonable to consider such complex formation as a continuation of protein folding. Characteristics of contact surfaces of temporary protein complexes share some similarities with active sites of enzymes. The contact surfaces of the temporary protein complexes have unique structure and properties and they are more conservative in comparison with active site of enzymes. So they represent prospective targets for a new generation of drugs. During the last decade, numerous investigations were undertaken to find or design small molecules that block protein dimerization or protein(peptide)-receptor interaction, or, on the contrary, to induce protein dimerization.     

Toward a “Structural BLAST”: Using structural relationships to infer function

We outline a set of strategies to infer protein function from structure. The overall approach depends on extensive use of homology modeling, the exploitation of a wide range of global and local geometric relationships between protein structures and the use of machine learning techniques. The combination of modeling with broad searches of protein structure space defines a “structural BLAST” approach to infer function with high genomic coverage. Applications are described to the prediction of protein–protein and protein–ligand interactions. In the context of protein–protein interactions, our structure‐based prediction algorithm, PrePPI, has comparable accuracy to high‐throughput experiments. An essential feature of PrePPI involves the use of Bayesian methods to combine structure‐derived information with non‐structural evidence (e.g. co‐expression) to assign a likelihood for each predicted interaction. This, combined with a structural BLAST approach significantly expands the range of applications of protein structure in the annotation of protein function, including systems level biological applications where it has previously played little role.     

Atomic contact vectors in protein-protein recognition

The ability to analyze and compare protein-protein interactions on the structural level is critical to our understanding of various aspects of molecular recognition and the functional interplay of components of biochemical networks. In this study, we introduce atomic contact vectors (ACVs) as an intuitive way to represent the physico-chemical characteristics of a protein-protein interface as well as a way to compare interfaces to each other. We test the utility of ACVs in classification by using them to distinguish between homodimers and crystal contacts. Our results compare favorably with those reported by other authors. We then apply ACVs to mine the PDB for all known protein-protein complexes and separate transient recognition complexes from permanent oligomeric ones. Getting at the basis of this difference is important for our understanding of recognition and we achieved a success rate of 91% for distinguishing these two classes of complexes. Although accessible surface area of the interface is a major discriminating feature, we also show that there are distinct differences in the contact preferences between the two kinds of complexes. Illustrating the superiority of ACVs as a basic comparison measure over a sequence-based approach, we derive a general rule of thumb to determine whether two protein-protein interfaces are redundant. With this method, we arrive at a nonredundant set of 209 recognition complexes--the largest set reported so far.     

The role of residue stability in transient protein–protein interactions involved in enzymatic phosphate hydrolysis. A computational study

Finding why protein–protein interactions (PPIs) are so specific can provide a valuable tool in a variety of fields. Statistical surveys of so‐called transient complexes (like those relevant for signal transduction mechanisms) have shown a tendency of polar residues to participate in the interaction region. Following this scheme, residues in the unbound partners have to compete between interacting with water or interacting with other residues of the protein. On the other hand, several works have shown that the notion of active site electrostatic preorganization can be used to interpret the high efficiency in enzyme reactions. This preorganization can be related to the instability of the residues important for catalysis. In some enzymes, in addition, conformational changes upon binding to other proteins lead to an increase in the activity of the enzymatic partner. In this article the linear response approximation version of the semimacroscopic protein dipoles Langevin dipoles (PDLD/S‐LRA) model is used to evaluate the stability of several residues in two phosphate hydrolysis enzymes upon complexation with their activating partners. In particular, the residues relevant for PPI and for phosphate hydrolysis in the CDK2/Cyclin A and Ras/GAP complexes are analyzed. We find that the evaluation of the stability of residues in these systems can be used to identify not only active site regions but it can also be used as a guide to locate “hot spots” for PPIs. We also show that conformational changes play a major role in positioning interfacing residues in a proper “energetic” orientation, ready to interact with the residues in the partner protein surface. Thus, we extend the preorganization theory to PPIs, extrapolating the results we obtained from the above‐mentioned complexes to a more general case. We conclude that the correlation between stability of a residue in the surface and the likelihood that it participates in the interaction can be a general fact for transient PPIs. Proteins 2006. © 2005 Wiley‐Liss, Inc.     

Ligand binding by TPR domains

Tetratricopeptide repeat (TPR) domains bind specific peptide ligands and are thought to mediate protein-protein interactions in a variety of biological systems. Here we compare peptide ligand-binding by several different TPR domains. We present specific examples that demonstrate that TPR domains typically undergo little or no structural rearrangement upon ligand binding. Our data suggest that, contrary to a recent proposal, coupled folding and binding is not the common mechanism of ligand recognition by TPR domains.     

The thermal adaptation of the nitrogenase Fe protein from thermophilic Methanobacter thermoautotrophicus

The nitrogenase Fe protein is a key component of the biochemical machinery responsible for the process of biological nitrogen fixation. The Fe protein is a member of a class of nucleotide-binding proteins that couple the binding and hydrolysis of nucleoside triphosphates to conformational changes. The nucleotide-dependent conformational changes modulate the formation of a macromolecular complex, and some members of the class include Galpha, EF-Tu, and myosin. The members of this class are highly interesting model systems for the analysis of aspects of thermal adaptability, since their mechanisms involve protein conformational change and protein-protein interactions. In this study, we have used our extensive knowledge of the structure of the Azotobacter vinelandii nitrogenase Fe protein in multiple structural conformations, and standard homology modeling approaches have been used to generate reliable models of the Fe protein from thermophilic Methanobacter thermoautotrophicus in the analogous structural conformations. The resulting structural comparison reveals that thermal adaptation of the M. thermoautotrophicus Fe protein is conferred by a number of factors, including increased structural rigidity that results from various structural changes within the protein interior. The analysis of hypothetical docking models and nitrogenase complex structures provides insights into the thermal adaptation of the protein-protein interactions that support macromolecular complex formation and catalysis at higher temperatures.