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.     

Scoring optimisation of unbound protein-protein docking including protein binding site predictions

The prediction of the structure of the protein-protein complex is of great importance to better understand molecular recognition processes. During systematic protein-protein docking, the surface of a protein molecule is scanned for putative binding sites of a partner protein. The possibility to include external data based on either experiments or bioinformatic predictions on putative binding sites during docking has been systematically explored. The external data were included during docking with a coarse-grained protein model and on the basis of force field weights to bias the docking search towards a predicted or known binding region. The approach was tested on a large set of protein partners in unbound conformations. The significant improvement of the docking performance was found if reliable data on the native binding sites were available. This was possible even if data for single key amino acids at a binding interface are included. In case of binding site predictions with limited accuracy, only modest improvement compared with unbiased docking was found. The optimisation of the protocol to bias the search towards predicted binding sites was found to further improve the docking performance resulting in approximately 40% acceptable solutions within the top 10 docking predictions compared with 22% in case of unbiased docking of unbound protein structures.     

A novel method for protein-protein interaction site prediction using phylogenetic substitution models

Protein-protein binding events mediate many critical biological functions in the cell. Typically, functionally important sites in proteins can be well identified by considering sequence conservation. However, protein-protein interaction sites exhibit higher sequence variation than other functional regions, such as catalytic sites of enzymes. Consequently, the mutational behavior leading to weak sequence conservation poses significant challenges to the protein-protein interaction site prediction. Here, we present a phylogenetic framework to capture critical sequence variations that favor the selection of residues essential for protein-protein binding. Through the comprehensive analysis of diverse protein families, we show that protein binding interfaces exhibit distinct amino acid substitution as compared with other surface residues. On the basis of this analysis, we have developed a novel method, BindML, which utilizes the substitution models to predict protein-protein binding sites of protein with unknown interacting partners. BindML estimates the likelihood that a phylogenetic tree of a local surface region in a query protein structure follows the substitution patterns of protein binding interface and nonbinding surfaces. BindML is shown to perform well compared to alternative methods for protein binding interface prediction. The methodology developed in this study is very versatile in the sense that it can be generally applied for predicting other types of functional sites, such as DNA, RNA, and membrane binding sites in proteins.     

Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.     

Great interactions: How binding incorrect partners can teach us about protein recognition and function

Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

A knowledge-based forcefield for protein-protein interface design

A distance-dependent knowledge-based potential for protein-protein interactions is derived and tested for application in protein design. Information on residue type specific C(alpha) and C(beta) pair distances is extracted from complex crystal structures in the Protein Data Bank and used in the form of radial distribution functions. The use of only backbone and C(beta) position information allows generation of relative protein-protein orientation poses with minimal sidechain information. Further coarse-graining can be done simply in the same theoretical framework to give potentials for residues of known type interacting with unknown type, as in a one-sided interface design problem. Both interface design via pose generation followed by sidechain repacking and localized protein-protein docking tests are performed on 39 nonredundant antibody-antigen complexes for which crystal structures are available. As reference, Lennard-Jones potentials, unspecific for residue type and biasing toward varying degrees of residue pair separation are used as controls. For interface design, the knowledge-based potentials give the best combination of consistently designable poses, low RMSD to the known structure, and more tightly bound interfaces with no added computational cost. 77% of the poses could be designed to give complexes with negative free energies of binding. Generally, larger interface separation promotes designability, but weakens the binding of the resulting designs. A localized docking test shows that the knowledge-based nature of the potentials improves performance and compares respectably with more sophisticated all-atoms potentials.     

Survey of the geometric association of domain-domain interfaces

Considering the limited success of the most sophisticated docking methods available and the amount of computation required for systematic docking, cataloging all the known interfaces may be an alternative basis for the prediction of protein tertiary and quaternary structures. We classify domain interfaces according to the geometry of domain-domain association. By applying a simple and efficient method called "interface tag clustering," more than 4,000 distinct types of domain interfaces are collected from Protein Quaternary Structure Server and Protein Data Bank. Given a pair of interacting domains, we define "face" as the set of interacting residues in each single domain and the pair of interacting faces as an "interface." We investigate how the geometry of interfaces relates to a network of interacting protein families, such as how many different binding orientations are possible between two families or whether a family uses distinct surfaces or the same surface when the family has diverse interaction partners from various families. We show there are, on average, 1.2-1.9 different types of interfaces between interacting domains and a significant number of family pairs associate in multiple orientations. In general, a family tends to use distinct faces for each partner when the family has diverse interaction partners. Each face is highly specific to its interaction partner and the binding orientation. The relative positions of interface residues are generally well conserved within the same type of interface even between remote homologs. The classification result is available at http://www.biotec.tu-dresden.de/~wkim/supplement.     

Dissecting protein-protein recognition sites

The recognition sites in 70 pairwise protein-protein complexes of known three-dimensional structure are dissected in a set of surface patches by clustering atoms at the interface. When the interface buries <2000 A2 of protein surface, the recognition sites usually form a single patch on the surface of each component protein. In contrast, larger interfaces are generally multipatch, with at least one pair of patches that are equivalent in size to a single-patch interface. Each recognition site, or patch within a site, contains a core made of buried interface atoms, surrounded by a rim of atoms that remain accessible to solvent in the complex. A simple geometric model reproduces the number and distribution of atoms within a patch. The rim is similar in composition to the rest of the protein surface, but the core has a distinctive amino acid composition, which may help in identifying potential protein recognition sites on single proteins of known structures.     

Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data

The number of structures of protein-protein complexes deposited to the Protein Data Bank is growing rapidly. These structures embed important information for predicting structures of new protein complexes. This motivated us to develop the PPISP method for predicting interface residues in protein-protein complexes. In PPISP, sequence profiles and solvent accessibility of spatially neighboring surface residues were used as input to a neural network. The network was trained on native interface residues collected from the Protein Data Bank. The prediction accuracy at the time was 70% with 47% coverage of native interface residues. Now we have extensively improved PPISP. The training set now consisted of 1156 nonhomologous protein chains. Test on a set of 100 nonhomologous protein chains showed that the prediction accuracy is now increased to 80% with 51% coverage. To solve the problem of over-prediction and under-prediction associated with individual neural network models, we developed a consensus method that combines predictions from multiple models with different levels of accuracy and coverage. Applied on a benchmark set of 68 proteins for protein-protein docking, the consensus approach outperformed the best individual models by 3-8 percentage points in accuracy. To demonstrate the predictive power of cons-PPISP, eight complex-forming proteins with interfaces characterized by NMR were tested. These proteins are nonhomologous to the training set and have a total of 144 interface residues identified by chemical shift perturbation. cons-PPISP predicted 174 interface residues with 69% accuracy and 47% coverage and promises to complement experimental techniques in characterizing protein-protein interfaces. .     

An iterative knowledge-based scoring function for protein-protein recognition

Using an efficient iterative method, we have developed a distance-dependent knowledge-based scoring function to predict protein-protein interactions. The function, referred to as ITScore-PP, was derived using the crystal structures of a training set of 851 protein-protein dimeric complexes containing true biological interfaces. The key idea of the iterative method for deriving ITScore-PP is to improve the interatomic pair potentials by iteration, until the pair potentials can distinguish true binding modes from decoy modes for the protein-protein complexes in the training set. The iterative method circumvents the challenging reference state problem in deriving knowledge-based potentials. The derived scoring function was used to evaluate the ligand orientations generated by ZDOCK 2.1 and the native ligand structures on a diverse set of 91 protein-protein complexes. For the bound test cases, ITScore-PP yielded a success rate of 98.9% if the top 10 ranked orientations were considered. For the more realistic unbound test cases, the corresponding success rate was 40.7%. Furthermore, for faster orientational sampling purpose, several residue-level knowledge-based scoring functions were also derived following the similar iterative procedure. Among them, the scoring function that uses the side-chain center of mass (SCM) to represent a residue, referred to as ITScore-PP(SCM), showed the best performance and yielded success rates of 71.4% and 30.8% for the bound and unbound cases, respectively, when the top 10 orientations were considered. ITScore-PP was further tested using two other published protein-protein docking decoy sets, the ZDOCK decoy set and the RosettaDock decoy set. In addition to binding mode prediction, the binding scores predicted by ITScore-PP also correlated well with the experimentally determined binding affinities, yielding a correlation coefficient of R = 0.71 on a test set of 74 protein-protein complexes with known affinities. ITScore-PP is computationally efficient. The average run time for ITScore-PP was about 0.03 second per orientation (including optimization) on a personal computer with 3.2 GHz Pentium IV CPU and 3.0 GB RAM. The computational speed of ITScore-PP(SCM) is about an order of magnitude faster than that of ITScore-PP. ITScore-PP and/or ITScore-PP(SCM) can be combined with efficient protein docking software to study protein-protein recognition.     

Association of putative concave protein-binding sites with the fluctuation behavior of residues

Here, we propose a binding site prediction method based on the high frequency end of the spectrum in the native state of the protein structural dynamics. The spectrum is obtained using an elastic network model (GNM). High frequency vibrating (HFV) residues are determined from the fastest modes dynamics. HFV residue clusters and the associated surface patch residues are tested for their likelihood to locate at the binding interfaces using two different data sets, the Benchmark Set of mainly enzymes and antigen/antibodies and the Cluster Set of more diverse structures. The binding interface is identified to be within 7.5 A of the HFV residue clusters in the Benchmark Set and Cluster Set, for 77% and 70% of the structures, respectively. The success rate increases to 88% and 84%, respectively, by using the surface patches. The results suggest that concave binding interfaces, typically those of enzyme-binding sites, are enriched by the HFV residues. Thus, we expect that the association of HFV residues with the interfaces is mostly for enzymes. If, however, a binding region has invaginations and cavities, as in some of the antigen/antibodies and in cases in the Cluster data set, we expect it would be detected there too. This implies that binding sites possess several (inter-related) properties such as cavities, high packing density, conservation, and disposition for hotspots at binding surfaces. It further suggests that the high frequency vibrating residue-based approach is a potential tool for identification of regions likely to serve as protein-binding sites. The software is available at http://www.prc.boun.edu.tr/PRC/software.html.     

Refinement of unbound protein docking studies using biological knowledge

In this work we present two methods for the reranking of protein-protein docking studies. One scoring method searches the InterDom database for domains that are available in the proteins to be docked and evaluates the interaction of these domains in other complexes of known structure. The second one analyzes the interface of each proposed conformation with regard to the conservation of Phe, Met, and Trp and their polar neighbor residues. The special relevance of these residues is based on a publication by Ma et al. (Proc Natl Acad Sci USA 2003;100:5772-5777), who compared the conservation of all residues in the interface region to the conservation on the rest of the protein's surface. The scoring functions were tested on 30 unbound docking test cases. The evaluation of the methods is based on the ability to rerank the output of a Fast Fourier Transformation (FFT) docking. Both were able to improve the ranking of the docking output. The best improvement was achieved for enzyme-inhibitor examples. Especially the domain-based scoring function was successful and able to place a near-native solution on one of the first six ranks for 13 of 17 (76%) enzyme-inhibitor complexes [in 53% (nine complexes) even on the first rank]. The method evaluating residue conservation allowed us to increase the number of good solutions within the first 100 ranks out of approximately 9000 in 82% of the 17 enzyme-inhibitor test cases, and for seven (41%) out of 17 enzyme-inhibitor complexes, a near native solution was placed within the first seven ranks.     

The tissue factor/factor VIIa/factor Xa complex: a model built by docking and site-directed mutagenesis

Factor X is activated to factor Xa (fXa) in the extrinsic coagulation pathway by the tissue factor (TF)/factor VIIa (fVIIa) complex. Upon activation, the fXa molecule remains associated with the TF/fVIIa complex, and this ternary complex is known to activate protease-activated receptors (PARs) 1 and 2. Activation of fVII in the TF complex by fXa is also seen at physiologic concentrations. The ternary complexes TF/fVII/fXa, TF/fVIIa/fX, and TF/fVIIa/fXa are therefore all physiologically relevant and of interest as targets for inhibition of both coagulation and cell-signaling pathways that are important in cardiovascular disease and inflammation. We therefore present a model of the TF/fVIIa/fXa complex, built with the use of the available structures of the TF/fVIIa complex and fXa by protein-protein docking calculations with the program Surfdock. The fXa model has an extended conformation, similar to that of fVIIa in the TF/fVIIa complex, with extensive interactions with TF and the protease domain of fVIIa. All four domains of fXa are involved in the interaction. The gamma-carboxyglutamate (Gla) and epithelial growth factor (EGF1 and EGF2) domains of fVIIa are not significantly involved in the interaction. Docking of the Gla domain of fXa to TF/fVIIa has been reported previously. The docking results identify potential interface residues, allowing rational selection of target residues for site-directed mutagenesis. This combination of docking and mutagenesis confirms that residues Glu51 and Asn57 in the EGF1 domain, Asp92 and Asp95 in the EGF2 domain, and Asp 185a, Lys 186, and Lys134 in the protease domain of factor Xa are involved in the interaction with TF/fVIIa. Other fX protease domain residues predicted to be involved in the interaction come from the 160s loop and the N-terminus of the fX protease domain, which is oriented in such a way that activation of both fVII by fXa, and the reciprocal fX activation by fVIIa, is possible.     

Principles of flexible protein-protein docking

Treating flexibility in molecular docking is a major challenge in cell biology research. Here we describe the background and the principles of existing flexible protein-protein docking methods, focusing on the algorithms and their rational. We describe how protein flexibility is treated in different stages of the docking process: in the preprocessing stage, rigid and flexible parts are identified and their possible conformations are modeled. This preprocessing provides information for the subsequent docking and refinement stages. In the docking stage, an ensemble of pre-generated conformations or the identified rigid domains may be docked separately. In the refinement stage, small-scale movements of the backbone and side-chains are modeled and the binding orientation is improved by rigid-body adjustments. For clarity of presentation, we divide the different methods into categories. This should allow the reader to focus on the most suitable method for a particular docking problem.     

Accounting for global protein deformability during protein-protein and protein-ligand docking

Computational docking methods are valuable tools aimed to simplify the costly process of drug development and improvement. Most current approaches assume a rigid receptor structure to allow virtual screening of large numbers of possible ligands and putative binding sites on a receptor molecule. However, inclusion of receptor flexibility can be of critical importance since binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a ligand. Recent approaches to efficiently account for receptor flexibility during docking simulations are reviewed. In particular, accounting efficiently for global conformational changes of the protein backbone during docking is a still challenging unsolved problem. An approximate method has recently been suggested that is based on relaxing the receptor conformation during docking in pre-calculated soft collective degrees of freedom (M. Zacharias, Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP, Proteins: Struct., Funct., Genet. 54 (2004) 759-767). Test applications on protein-protein docking and on docking the inhibitor staurosporine to the apo-form of cAMP-dependent protein kinase A catalytic domain indicate significant improvement of docking results compared to rigid docking at a very modest computational demand. Accounting for receptor conformational changes in pre-calculated global degrees of freedom might offer a promising route to improve systematic docking screening simulations.     

Protein docking using a genetic algorithm

A genetic algorithm (GA) for protein-protein docking is described, in which the proteins are represented by dot surfaces calculated using the Connolly program. The GA is used to move the surface of one protein relative to the other to locate the area of greatest surface complementarity between the two. Surface dots are deemed complementary if their normals are opposed, their Connolly shape type is complementary, and their hydrogen bonding or hydrophobic potential is fulfilled. Overlap of the protein interiors is penalized. The GA is tested on 34 large protein-protein complexes where one or both proteins has been crystallized separately. Parameters are established for which 30 of the complexes have at least one near-native solution ranked in the top 100. We have also successfully reassembled a 1,400-residue heptamer based on the top-ranking GA solution obtained when docking two bound subunits.     

Exploiting sequence and structure homologs to identify protein-protein binding sites

A rapid increase in the number of experimentally derived three-dimensional structures provides an opportunity to better understand and subsequently predict protein-protein interactions. In this study, structurally conserved residues were derived from multiple structure alignments of the individual components of known complexes and the assigned conservation score was weighted based on the crystallographic B factor to account for the structural flexibility that will result in a poor alignment. Sequence profile and accessible surface area information was then combined with the conservation score to predict protein-protein binding sites using a Support Vector Machine (SVM). The incorporation of the conservation score significantly improved the performance of the SVM. About 52% of the binding sites were precisely predicted (greater than 70% of the residues in the site were identified); 77% of the binding sites were correctly predicted (greater than 50% of the residues in the site were identified), and 21% of the binding sites were partially covered by the predicted residues (some residues were identified). The results support the hypothesis that in many cases protein interfaces require some residues to provide rigidity to minimize the entropic cost upon complex formation.     

Accurate detection of protein:ligand binding sites using molecular dynamics simulations

Accurate prediction of location of cavities and surface grooves in proteins is important, as these are potential sites for ligand binding. Several currently available programs for cavity detection are unable to detect cavities near the surface or surface grooves. In the present study, an optimized molecular dynamics based procedure is described for detection and quantification of interior cavities as well as surface pockets. This is based on the observation that the mobility of water in such pockets is significantly lower than that of bulk water. The algorithm efficiently detects surface grooves that are sites of protein-ligand and protein-protein interaction. The algorithm was also used to substantially improve the performance of an automated docking procedure for docking monomers of nonobligate protein-protein complexes. In addition, it was applied to predict key residues involved in the binding of the E. coli toxin CcdB with its inhibitor. Predictions were subsequently validated by mutagenesis experiments.     

Proteome-wide prediction of overlapping small molecule and protein binding sites using structure

Small molecules that modulate protein-protein interactions are of great interest for chemical biology and therapeutics. Here I present a structure-based approach to predict 'bi-functional' sites able to bind both small molecule ligands and proteins, in proteins of unknown structure. First, I develop a homology-based annotation method that transfers binding sites of known three-dimensional structure onto protein sequences, predicting residues in ligand and protein binding sites with estimated true positive rates of 98% and 88%, respectively, at 1% false positive rates. Applying this method to the human proteome predicts 8463 proteins with bi-functional residues and correctly recovers the targets of known interaction modulators. Proteins with significantly (p < 0.01) more bi-functional residues than expected were found to be enriched in regulatory and depleted in metabolism functions. Finally, I demonstrate the utility of the method by describing examples of predicted overlap and evidence of their biological and therapeutic relevance. The results suggest that combining the structures of known binding sites with established fold detection algorithms can predict regions of protein-protein interfaces that are amenable to small molecule modulation. Open-source software and the results for several complete proteomes are available at http://pibase.janelia.org/homolobind.