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.     

Intuitive,but not simple: Including explicit water molecules in protein–protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high‐resolution crystallographically characterized “dry” protein–protein complexes and was shown to reliably identify native‐like models. However, most current protein–protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the “truly” bridging waters at the 30 protein–protein interfaces and we utilized them in “solvated” docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ～24% in the average hit‐count within the top‐10 predictions the protein–protein dataset was seen, compared to standard “dry” docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native‐like structure predictions. Proteins 2014; 82:916–932. © 2013 Wiley Periodicals, Inc.     

Interolog interfaces in protein–protein docking

Proteins are essential elements of biological systems, and their function typically relies on their ability to successfully bind to specific partners. Recently, an emphasis of study into protein interactions has been on hot spots, or residues in the binding interface that make a significant contribution to the binding energetics. In this study, we investigate how conservation of hot spots can be used to guide docking prediction. We show that the use of evolutionary data combined with hot spot prediction highlights near‐native structures across a range of benchmark examples. Our approach explores various strategies for using hot spots and evolutionary data to score protein complexes, using both absolute and chemical definitions of conservation along with refinements to these strategies that look at windowed conservation and filtering to ensure a minimum number of hot spots in each binding partner. Finally, structure‐based models of orthologs were generated for comparison with sequence‐based scoring. Using two data sets of 22 and 85 examples, a high rate of top 10 and top 1 predictions are observed, with up to 82% of examples returning a top 10 hit and 35% returning top 1 hit depending on the data set and strategy applied; upon inclusion of the native structure among the decoys, up to 55% of examples yielded a top 1 hit. The 20 common examples between data sets show that more carefully curated interolog data yields better predictions, particularly in achieving top 1 hits. Proteins 2015; 83:1940–1946. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Monte Carlo replica‐exchange based ensemble docking of protein conformations

A replica‐exchange Monte Carlo (REMC) ensemble docking approach has been developed that allows efficient exploration of protein–protein docking geometries. In addition to Monte Carlo steps in translation and orientation of binding partners, possible conformational changes upon binding are included based on Monte Carlo selection of protein conformations stored as ordered pregenerated conformational ensembles. The conformational ensembles of each binding partner protein were generated by three different approaches starting from the unbound partner protein structure with a range spanning a root mean square deviation of 1–2.5 Å with respect to the unbound structure. Because MC sampling is performed to select appropriate partner conformations on the fly the approach is not limited by the number of conformations in the ensemble compared to ensemble docking of each conformer pair in ensemble cross docking. Although only a fraction of generated conformers was in closer agreement with the bound structure the REMC ensemble docking approach achieved improved docking results compared to REMC docking with only the unbound partner structures or using docking energy minimization methods. The approach has significant potential for further improvement in combination with more realistic structural ensembles and better docking scoring functions. Proteins 2017; 85:924–937. © 2016 Wiley Periodicals, Inc.     

DockRank: Ranking docked conformations using partner‐specific sequence homology‐based protein interface prediction

Selecting near‐native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner‐specific sequence homology‐based protein–protein interface predictor (PS‐HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state‐of‐the‐art docking scoring functions using Success Rate (the percentage of cases that have at least one near‐native conformation among the top m conformations) and Hit Rate (the percentage of near‐native conformations that are included among the top m conformations). In cases where it is possible to obtain partner‐specific (PS) interface predictions from PS‐HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state‐of‐the‐art energy‐based scoring functions (improving Success Rate by up to 4‐fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39‐fold). The latter result underscores the importance of using partner‐specific interface residues in scoring docked conformations. We show that DockRank, when used to re‐rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/ . Proteins 2014; 82:250–267. © 2013 Wiley Periodicals, Inc.     

DockTrina: Docking triangular protein trimers

In spite of the abundance of oligomeric proteins within a cell, the structural characterization of protein–protein interactions is still a challenging task. In particular, many of these interactions involve heteromeric complexes, which are relatively difficult to determine experimentally. Hence there is growing interest in using computational techniques to model such complexes. However, assembling large heteromeric complexes computationally is a highly combinatorial problem. Nonetheless the problem can be simplified greatly by considering interactions between protein trimers. After dimers and monomers, triangular trimers (i.e. trimers with pair‐wise contacts between all three pairs of proteins) are the most frequently observed quaternary structural motifs according to the three‐dimensional (3D) complex database. This article presents DockTrina, a novel protein docking method for modeling the 3D structures of nonsymmetrical triangular trimers. The method takes as input pair‐wise contact predictions from a rigid body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation test. Finally, it ranks the predictions using a scoring function which combines triples of pair‐wise contact terms and a geometric clash penalty term. The overall approach takes less than 2 min per complex on a modern desktop computer. The method is tested and validated using a benchmark set of 220 bound and seven unbound protein trimer structures. DockTrina will be made available at http://nano‐d.inrialpes.fr/software/docktrina . Proteins 2014; 82:34–44. © 2013 Wiley Periodicals, Inc.     

Landscape of protein–small ligand binding modes

Elucidating the mechanisms of specific small‐molecule (ligand) recognition by proteins is a long‐standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein–ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all‐against‐all comparison of 20,040 protein–ligand complexes provided the landscape of the protein–ligand binding modes and its relationships with protein‐ and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R² = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein–ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.     

iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement

Protein‐protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure‐based force field for intramolecular contributions. The approach was systematically evaluated on a large protein‐protein docking benchmark, starting from an enriched decoy set of rigidly docked protein–protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%. Proteins 2015; 83:248–258. © 2014 Wiley Periodicals, Inc.     

Amino acid substitutions at protein–protein interfaces that modulate the oligomeric state

Despite similarities in their sequence and structure, there are a number of homologous proteins that adopt various oligomeric states. Comparisons of these homologous protein pairs, in terms of residue substitutions at the protein–protein interfaces, have provided fundamental characteristics that describe how proteins interact with each other. We have prepared a dataset composed of pairs of related proteins with different homo‐oligomeric states. Using the protein complexes, the interface residues were identified, and using structural alignments, the shadow‐interface residues have been defined as the surface residues that align with the interface residues. Subsequently, we investigated residue substitutions between the interfaces and the shadow interfaces. Based on the degree of the contributions to the interactions, the aligned sites of the interfaces and shadow interfaces were divided into primary and secondary sites; the primary sites are the focus of this work. The primary sites were further classified into two groups (i.e. exposed and buried) based on the degree to which the residue is buried within the shadow interfaces. Using these classifications, two simple mechanisms that mediate the oligomeric states were identified. In the primary‐exposed sites, the residues on the shadow interfaces are replaced by more hydrophobic or aromatic residues, which are physicochemically favored at protein–protein interfaces. In the primary‐buried sites, the residues on the shadow interfaces are replaced by larger residues that protrude into other proteins. These simple rules are satisfied in 23 out of 25 Structural Classification of Proteins (SCOP) families with a different‐oligomeric‐state pair, and thus represent a basic strategy for modulating protein associations and dissociations. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

How different from random are docking predictions when ranked by scoring functions?

Docking algorithms predict the structure of protein–protein interactions. They sample the orientation of two unbound proteins to produce various predictions about their interactions, followed by a scoring step to rank the predictions. We present a statistical assessment of scoring functions used to rank near‐native orientations, applying our statistical analysis to a benchmark dataset of decoys of protein–protein complexes and assessing the statistical significance of the outcome in the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment. A P value was assigned that depended on the number of near‐native structures in the sampling. We studied the effect of filtering out redundant structures and tested the use of pair‐potentials derived using ZDock and ZRank. Our results show that for many targets, it is not possible to determine when a successful reranking performed by scoring functions results merely from random choice. This analysis reveals that changes should be made in the design of the CAPRI scoring experiment. We propose including the statistical assessment in this experiment either at the preprocessing or the evaluation step. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

A combinatorial scoring function for protein–RNA docking

Protein–RNA docking is still an open question. One of the main challenges is to develop an effective scoring function that can discriminate near‐native structures from the incorrect ones. To solve the problem, we have constructed a knowledge‐based residue‐nucleotide pairwise potential with secondary structure information considered for nonribosomal protein–RNA docking. Here we developed a weighted combined scoring function RpveScore that consists of the pairwise potential and six physics‐based energy terms. The weights were optimized using the multiple linear regression method by fitting the scoring function to L_rmsd for the bound docking decoys from Benchmark II. The scoring functions were tested on 35 unbound docking cases. The results show that the scoring function RpveScore including all terms performs best. Also RpveScore was compared with the statistical mechanics‐based method derived potential ITScore‐PR, and the united atom‐based statistical potentials QUASI‐RNP and DARS‐RNP. The success rate of RpveScore is 71.6% for the top 1000 structures and the number of cases where a near‐native structure is ranked in top 30 is 25 out of 35 cases. For 32 systems (91.4%), RpveScore can find the binding mode in top 5 that has no lower than 50% native interface residues on protein and nucleotides on RNA. Additionally, it was found that the long‐range electrostatic attractive energy plays an important role in distinguishing near‐native structures from the incorrect ones. This work can be helpful for the development of protein–RNA docking methods and for the understanding of protein–RNA interactions. RpveScore program is available to the public at http://life.bjut.edu.cn/kxyj/kycg/2017116/14845362285362368_1.html Proteins 2017; 85:741–752. © 2016 Wiley Periodicals, Inc.     

Predicting protein complex geometries with a neural network

A major challenge of the protein docking problem is to define scoring functions that can distinguish near‐native protein complex geometries from a large number of non‐native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom‐pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near‐native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge‐based energy functions for scoring. We show that a distance‐dependent atom pair potential performs much better than a simple atom‐pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge‐based scoring functions such as ZDOCK 3.0, ZRANK, ITScore‐PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network‐based scoring function achieves a reasonable performance in rigid‐body unbound docking of proteins. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Protein social behavior makes a stronger signal for partner identification than surface geometry

Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.     

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods.     

Benchmarking protein–protein interface predictions: Why you should care about protein size

A number of predictive methods have been developed to predict protein–protein binding sites. Each new method is traditionally benchmarked using sets of protein structures of various sizes, and global statistics are used to assess the quality of the prediction. Little attention has been paid to the potential bias due to protein size on these statistics. Indeed, small proteins involve proportionally more residues at interfaces than large ones. If a predictive method is biased toward small proteins, this can lead to an over‐estimation of its performance. Here, we investigate the bias due to the size effect when benchmarking protein‐protein interface prediction on the widely used docking benchmark 4.0. First, we simulate random scores that favor small proteins over large ones. Instead of the 0.5 AUC (Area Under the Curve) value expected by chance, these biased scores result in an AUC equal to 0.6 using hypergeometric distributions, and up to 0.65 using constant scores. We then use real prediction results to illustrate how to detect the size bias by shuffling, and subsequently correct it using a simple conversion of the scores into normalized ranks. In addition, we investigate the scores produced by eight published methods and show that they are all affected by the size effect, which can change their relative ranking. The size effect also has an impact on linear combination scores by modifying the relative contributions of each method. In the future, systematic corrections should be applied when benchmarking predictive methods using data sets with mixed protein sizes. Proteins 2014; 82:1444–1452. © 2014 Wiley Periodicals, Inc.     

Importance of the interaction protein–protein of the CaM–PDE1A and CaM–MLCK complexes in the development of new anti‐CaM drugs

Protein–protein interactions play central roles in physiological and pathological processes. The bases of the mechanisms of drug action are relevant to the discovery of new therapeutic targets. This work focuses on understanding the interactions in protein–protein–ligands complexes, using proteins calmodulin (CaM), human calcium/calmodulin‐dependent 3′,5′‐cyclic nucleotide phosphodiesterase 1A active human (PDE1A), and myosin light chain kinase (MLCK) and ligands αII–spectrin peptide (αII–spec), and two inhibitors of CaM (chlorpromazine (CPZ) and malbrancheamide (MBC)). The interaction was monitored with a fluorescent biosensor of CaM (hCaM M124C–mBBr). The results showed changes in the affinity of CPZ and MBC depending on the CaM–protein complex under analysis. For the Ca²⁺–CaM, Ca²⁺–CaM–PDE1A, and Ca²⁺–CaM–MLCK complexes, CPZ apparent dissociation constants (K_ds) were 1.11, 0.28, and 0.55 μM, respectively; and for MBC K_ds were 1.43, 1.10, and 0.61 μM, respectively. In competition experiments the addition of calmodulin binding peptide 1 (αII–spec) to Ca²⁺–hCaM M124C–mBBr quenched the fluorescence (K_d = 2.55 ± 1.75 pM) and the later addition of MBC (up to 16 μM) did not affect the fluorescent signal. Instead, the additions of αII–spec to a preformed Ca²⁺–hCaM M124C–mBBr–MBC complex modified the fluorescent signal. However, MBC was able to displace the PDE1A and MLCK from its complex with Ca²⁺–CaM. In addition, docking studies were performed for all complexes with both ligands showing an excellent correlation with experimental data. These experiments may help to explain why in vivo many CaM drugs target prefer only a subset of the Ca²⁺–CaM regulated proteins and adds to the understanding of molecular interactions between protein complexes and small ligands. Copyright © 2013 John Wiley & Sons, Ltd.     

Protein‐spanning water networks and implications for prediction of protein–protein interactions mediated through hydrophobic effects

Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self‐assembly processes. Protein–protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein–protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy‐based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue‐based water density and single‐linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces. Proteins 2014; 82:3312–3326. © 2014 Wiley Periodicals, Inc.     

Organic dyes as small molecule protein–protein interaction inhibitors for the CD40–CD154 costimulatory interaction

It is becoming increasingly clear that small molecules can often act as effective protein–protein interaction (PPI) inhibitors, an area of increasing interest for its many possible therapeutic applications. We have identified several organic dyes and related small molecules that (i) concentration‐dependently inhibit the important CD40–CD154 costimulatory interaction with activities in the low micromolar (µM) range, (ii) show selectivity toward this particular PPI, (iii) seem to bind on the surface of CD154, and (iv) concentration‐dependently inhibit the CD154‐induced B cell proliferation. They were identified through an iterative activity screening/structural similarity search procedure starting with suramin as lead, and the best smaller compounds, the main focus of the present work, achieved an almost 3‐fold increase in ligand efficiency (ΔG⁰/nonhydrogen atom = 0.8 kJ/N_nHa) approaching the average of known promising small‐molecule PPI inhibitors (～1.0 kJ/N_nHa). Since CD154 is a member of the tumor necrosis factor (TNF) superfamily of cell surface interaction molecules, inhibitory activities on the TNF‐R1–TNF‐α interactions were also determined to test for specificity, and the compounds selected here all showed more than 30‐fold selectivity toward the CD40–CD154 interaction. Because of their easy availability in various structural scaffolds and because of their good protein‐binding ability, often explored for tissue‐specific staining and other purposes, such organic dyes can provide a valuable addition to the chemical space searched to identify small molecule PPI inhibitors in general. Copyright © 2009 John Wiley & Sons, Ltd.