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.     

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.     

Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc.     

Export chaperone SecB uses one surface of interaction for diverse unfolded polypeptide ligands

SecB, a remarkable chaperone involved in protein export, binds diverse ligands rapidly with high affinity and low specificity. Site‐directed spin labeling and electron paramagnetic resonance spectroscopy were used to investigate the surface of interaction on the export chaperone SecB. We examined SecB in complex with the unfolded precursor form of outer membrane protein OmpA as well as with a truncated version of OmpA that includes the transmembrane domain and lacks both the signal peptide and the periplasmic domain. In addition, we studied the binding of SecB to the unfolded mature form of galactose‐binding protein, a soluble periplasmic protein. We have previously used the same strategy to map the binding surface for the precursor of galactose‐binding protein. We show that for all ligands tested the patterns of contact are the same.     

A compound‐based computational approach for the accurate determination of hot spots

A plethora of both experimental and computational methods have been proposed in the past 20 years for the identification of hot spots at a protein–protein interface. The experimental determination of a protein–protein complex followed by alanine scanning mutagenesis, though able to determine hot spots with much precision, is expensive and has no guarantee of success while the accuracy of the current computational methods for hot‐spot identification remains low. Here, we present a novel structure‐based computational approach that accurately determines hot spots through docking into a set of proteins homologous to only one of the two interacting partners of a compound capable of disrupting the protein–protein interaction (PPI). This approach has been applied to identify the hot spots of human activin receptor type II (ActRII) critical for its binding toward Cripto‐I. The subsequent experimental confirmation of the computationally identified hot spots portends a potentially accurate method for hot‐spot determination in silico given a compound capable of disrupting the PPI in question. The hot spots of human ActRII first reported here may well become the focal points for the design of small molecule drugs that target the PPI. The determination of their interface may have significant biological implications in that it suggests that Cripto‐I plays an important role in both activin and nodal signal pathways.     

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.     

Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.     

Inferring the microscopic surface energy of protein–protein interfaces from mutation data

Mutations at protein–protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical values, yielding mean energies of ?48 cal mol^?1 Å^?2 for interactions between hydrophobic surfaces, ?51 to ?80 cal mol^?1 Å^?2 for surfaces of complementary charge, and 66–83 cal mol^?1 Å^?2 for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at ?24 cal mol^?1 Å^?2. Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid‐body interactions (r = 0.66). When used to rerank docking poses, it can place near‐native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50–95% of the cohesive energy. The model is available open‐source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/ . Proteins 2015; 83:640–650. © 2015 Wiley Periodicals, Inc.     

Combined computational design of a zinc‐binding site and a protein–protein interaction: One open zinc coordination site was not a robust hotspot for de novo ubiquitin binding

We computationally designed a de novo protein–protein interaction between wild‐type ubiquitin and a redesigned scaffold. Our strategy was to incorporate zinc at the designed interface to promote affinity and orientation specificity. A large set of monomeric scaffold surfaces were computationally engineered with three‐residue zinc coordination sites, and the ubiquitin residue H68 was docked to the open coordination site to complete a tetrahedral zinc site. This single coordination bond was intended as a hotspot and polar interaction for ubiquitin binding, and surrounding residues on the scaffold were optimized primarily as hydrophobic residues using a rotamer‐based sequence design protocol in Rosetta. From thousands of independent design simulations, four sequences were selected for experimental characterization. The best performing design, called Spelter, binds tightly to zinc (K_d < 10 nM) and binds ubiquitin with a K_d of 20 µM in the presence of zinc and 68 µM in the absence of zinc. Mutagenesis studies and nuclear magnetic resonance chemical shift perturbation experiments indicate that Spelter interacts with H68 and the target surface on ubiquitin; however, H68 does not form a hotspot as intended. Instead, mutation of H68 to alanine results in tighter binding. Although a 3/1 zinc coordination arrangement at an interface cannot be ruled out as a means to improve affinity, our study led us to conclude that 2/2 coordination arrangements or multiple‐zinc designs are more likely to promote high‐affinity protein interactions. Proteins 2013; 81:1245–1255. © 2013 Wiley Periodicals, Inc.     

Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph‐theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions.     

Molecular dynamics simulations reveal structural insights into inhibitor binding modes and functionality in human Group IIA phospholipase A2

Human Group IIA phospholipase A₂ (hGIIA) promotes inflammation in immune‐mediated pathologies by regulating the arachidonic acid pathway through both catalysis‐dependent and ‐independent mechanisms. The hGIIA crystal structure, both alone and inhibitor‐bound, together with structures of closely related snake‐venom‐derived secreted phospholipase enzymes has been well described. However, differentiation of biological and nonbiological contacts and the relevance of structures determined from snake venom enzymes to human enzymes are not clear. We employed molecular dynamics (MD) and docking approaches to understand the binding of inhibitors that selectively or nonselectively block the catalysis‐independent mechanism of hGIIA. Our results indicate that hGIIA behaves as a monomer in the solution environment rather than a dimer arrangement that is in the asymmetric unit of some crystal structures. The binding mode of a nonselective inhibitor, KH064, was validated by a combination of the experimental electron density and MD simulations. The binding mode of the selective pentapeptide inhibitor FLSYK to hGIIA was stipulated to be different to that of the snake venom phospholipases A₂ of Daboia russelli pulchella (svPLA₂). Our data suggest that the application of MD approaches to crystal structure data is beneficial in evaluating the robustness of conclusions drawn based on crystal structure data alone. Proteins 2017; 85:827–842. © 2016 Wiley Periodicals, Inc.     

Hot‐spot analysis to dissect the functional protein–protein interface of a tRNA‐modifying enzyme

Interference with protein–protein interactions of interfaces larger than 1500 Ų by small drug‐like molecules is notoriously difficult, particularly if targeting homodimers. The tRNA modifying enzyme Tgt is only functionally active as a homodimer. Thus, blocking Tgt dimerization is a promising strategy for drug therapy as this protein is key to the development of Shigellosis. Our goal was to identify hot‐spot residues which, upon mutation, result in a predominantly monomeric state of Tgt. The detailed understanding of the spatial location and stability contribution of the individual interaction hot‐spot residues and the plasticity of motifs involved in the interface formation is a crucial prerequisite for the rational identification of drug‐like inhibitors addressing the respective dimerization interface. Using computational analyses, we identified hot‐spot residues that contribute particularly to dimer stability: a cluster of hydrophobic and aromatic residues as well as several salt bridges. This in silico prediction led to the identification of a promising double mutant, which was validated experimentally. Native nano‐ESI mass spectrometry showed that the dimerization of the suggested mutant is largely prevented resulting in a predominantly monomeric state. Crystal structure analysis and enzyme kinetics of the mutant variant further support the evidence for enhanced monomerization and provide first insights into the structural consequences of the dimer destabilization. Proteins 2014; 82:2713–2732. © 2014 Wiley Periodicals, Inc.     

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.     

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.     

Efficient identification of tubby‐binding proteins by an improved system of T7 phage display

Mutation in the tubby gene causes adult‐onset obesity, progressive retinal, and cochlear degeneration with unknown mechanism. In contrast, mutations in tubby‐like protein 1 (Tulp1), whose C‐terminus is highly homologous to tubby, only lead to retinal degeneration. We speculate that their diverse N‐terminus may define their distinct disease profile. To elucidate the binding partners of tubby, we used tubby N‐terminus (tubby‐N) as bait to identify unknown binding proteins with open‐reading‐frame (ORF) phage display. T7 phage display was engineered with three improvements: high‐quality ORF phage display cDNA library, specific phage elution by protease cleavage, and dual phage display for sensitive high throughput screening. The new system is capable of identifying unknown bait‐binding proteins in as fast as ～4–7 days. While phage display with conventional cDNA libraries identifies high percentage of out‐of‐frame unnatural short peptides, all 28 tubby‐N‐binding clones identified by ORF phage display were ORFs. They encode 16 proteins, including 8 nuclear proteins. Fourteen proteins were analyzed by yeast two‐hybrid assay and protein pull‐down assay with ten of them independently verified. Comparative binding analyses revealed several proteins binding to both tubby and Tulp1 as well as one tubby‐specific binding protein. These data suggest that tubby‐N is capable of interacting with multiple nuclear and cytoplasmic protein binding partners. These results demonstrated that the newly‐engineered ORF phage display is a powerful technology to identify unknown protein–protein interactions. Copyright © 2009 John Wiley & Sons, Ltd.     

Structural characterization of the heme‐based oxygen sensor,AfGcHK, its interactions with the cognate response regulator,and their combined mechanism of action in a bacterial two‐component signaling system

The oxygen sensor histidine kinase AfGcHK from the bacterium Anaeromyxobacter sp. Fw 109‐5 forms a two‐component signal transduction system together with its cognate response regulator (RR). The binding of oxygen to the heme iron of its N‐terminal sensor domain causes the C‐terminal kinase domain of AfGcHK to autophosphorylate at His183 and then transfer this phosphate to Asp52 or Asp169 of the RR protein. Analytical ultracentrifugation revealed that AfGcHK and the RR protein form a complex with 2:1 stoichiometry. Hydrogen‐deuterium exchange coupled to mass spectrometry (HDX‐MS) suggested that the most flexible part of the whole AfGcHK protein is a loop that connects the two domains and that the heme distal side of AfGcHK, which is responsible for oxygen binding, is the only flexible part of the sensor domain. HDX‐MS studies on the AfGcHK:RR complex also showed that the N‐side of the H9 helix in the dimerization domain of the AfGcHK kinase domain interacts with the helix H1 and the β‐strand B2 area of the RR protein's Rec1 domain, and that the C‐side of the H8 helix region in the dimerization domain of the AfGcHK protein interacts mostly with the helix H5 and β‐strand B6 area of the Rec1 domain. The Rec1 domain containing the phosphorylable Asp52 of the RR protein probably has a significantly higher affinity for AfGcHK than the Rec2 domain. We speculate that phosphorylation at Asp52 changes the overall structure of RR such that the Rec2 area containing the second phosphorylation site (Asp169) can also interact with AfGcHK. Proteins 2016; 84:1375–1389. © 2016 Wiley Periodicals, Inc.     

MotifAnalyzer‐PDZ: A computational program to investigate the evolution of PDZ‐binding target specificity

Recognition of short linear motifs (SLiMs) or peptides by proteins is an important component of many cellular processes. However, due to limited and degenerate binding motifs, prediction of cellular targets is challenging. In addition, many of these interactions are transient and of relatively low affinity. Here, we focus on one of the largest families of SLiM‐binding domains in the human proteome, the PDZ domain. These domains bind the extreme C‐terminus of target proteins, and are involved in many signaling and trafficking pathways. To predict endogenous targets of PDZ domains, we developed MotifAnalyzer‐PDZ, a program that filters and compares all motif‐satisfying sequences in any publicly available proteome. This approach enables us to determine possible PDZ binding targets in humans and other organisms. Using this program, we predicted and biochemically tested novel human PDZ targets by looking for strong sequence conservation in evolution. We also identified three C‐terminal sequences in choanoflagellates that bind a choanoflagellate PDZ domain, the Monsiga brevicollis SHANK1 PDZ domain (mbSHANK1), with endogenously‐relevant affinities, despite a lack of conservation with the targets of a homologous human PDZ domain, SHANK1. All three are predicted to be signaling proteins, with strong sequence homology to cytosolic and receptor tyrosine kinases. Finally, we analyzed and compared the positional amino acid enrichments in PDZ motif‐satisfying sequences from over a dozen organisms. Overall, MotifAnalyzer‐PDZ is a versatile program to investigate potential PDZ interactions. This proof‐of‐concept work is poised to enable similar types of analyses for other SLiM‐binding domains (e.g., MotifAnalyzer‐Kinase). MotifAnalyzer‐PDZ is available at http://motifAnalyzerPDZ.cs.wwu.edu .