A protein-RNA docking benchmark (I): nonredundant cases

We have developed a nonredundant protein-RNA docking benchmark dataset, which is derived from the available bound and unbound structures in the Protein Data Bank involving polypeptide and nucleic acid chains. It consists of nine unbound-unbound cases where both the protein and the RNA are available in the free form. The other 36 cases are of unbound-bound type where only the protein is available in the free form. The conformational change upon complex formation is calculated by a distance matrix alignment method, and based on that, complexes are classified into rigid, semi-flexible, and full flexible. Although in the rigid body category, no significant conformational change accompanies complex formation, the fully flexible test cases show large domain movements, RNA base flips, etc. The benchmark covers four major groups of RNA, namely, t-RNA, ribosomal RNA, duplex RNA, and single-stranded RNA. We find that RNA is generally more flexible than the protein in the complexes, and the interface region is as flexible as the molecule as a whole. The structural diversity of the complexes in the benchmark set should provide a common ground for the development and comparison of the protein-RNA docking methods. The benchmark can be freely downloaded from the internet.     

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.     

A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data

We present here an extended protein-RNA docking benchmark composed of 71 test cases in which the coordinates of the interacting protein and RNA molecules are available from experimental structures, plus an additional set of 35 cases in which at least one of the interacting subunits is modeled by homology. All cases in the experimental set have available unbound protein structure, and include five cases with available unbound RNA structure, four cases with a pseudo-unbound RNA structure, and 62 cases with the bound RNA form. The additional set of modeling cases comprises five unbound-model, eight model-unbound, 19 model-bound, and three model-model protein-RNA cases. The benchmark covers all major functional categories and contains cases with different degrees of difficulty for docking, as far as protein and RNA flexibility is concerned. The main objective of this benchmark is to foster the development of protein-RNA docking algorithms and to contribute to the better understanding and prediction of protein-RNA interactions. The benchmark is freely available at http://life.bsc.es/pid/protein-rna-benchmark.     

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.     

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.     

A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes

T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein–protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near‐native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes.     

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.     

DynaDock: A new molecular dynamics‐based algorithm for protein–peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well‐established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein–peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics‐based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft‐core potentials for the protein–peptide interactions and applies a new optimization scheme to the soft‐core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft‐core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein–peptide complexes with 2–16mer peptides. Docking poses with peptide RMSD values <2.10 Å from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 Å. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD ≤2.10 Å. Proteins 2010. © 2009 Wiley‐Liss, 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.     

DNABind: A hybrid algorithm for structure‐based prediction of DNA‐binding residues by combining machine learning‐ and template‐based approaches

Accurate prediction of DNA‐binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning‐ and template‐based methods. Our machine learning‐based method was based on the probabilistic combination of a structure‐based and a sequence‐based predictor, both of which were implemented using support vector machines algorithms. The former included our well‐designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA‐binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template‐based method depended on structural alignment and utilized the template structure from known protein–DNA complexes to infer DNA‐binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high‐quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state‐of‐art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/ . Proteins 2013; 81:1885–1899. © 2013 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.     

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.     

Protein–protein structure prediction by scoring molecular dynamics trajectories of putative poses

The prediction of protein–protein interactions and their structural configuration remains a largely unsolved problem. Most of the algorithms aimed at finding the native conformation of a protein complex starting from the structure of its monomers are based on searching the structure corresponding to the global minimum of a suitable scoring function. However, protein complexes are often highly flexible, with mobile side chains and transient contacts due to thermal fluctuations. Flexibility can be neglected if one aims at finding quickly the approximate structure of the native complex, but may play a role in structure refinement, and in discriminating solutions characterized by similar scores. We here benchmark the capability of some state‐of‐the‐art scoring functions (BACH‐SixthSense, PIE/PISA and Rosetta) in discriminating finite‐temperature ensembles of structures corresponding to the native state and to non‐native configurations. We produce the ensembles by running thousands of molecular dynamics simulations in explicit solvent starting from poses generated by rigid docking and optimized in vacuum. We find that while Rosetta outperformed the other two scoring functions in scoring the structures in vacuum, BACH‐SixthSense and PIE/PISA perform better in distinguishing near‐native ensembles of structures generated by molecular dynamics in explicit solvent. Proteins 2016; 84:1312–1320. © 2016 Wiley Periodicals, Inc.     

Flexible docking of peptides to proteins using CABS‐dock

Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS‐dock is a method for protein–peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS‐dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command‐line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large‐sized systems. In this article, we outline the current status of the CABS‐dock method, its recent developments, applications, and challenges ahead.     

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.     

The dataset for protein–RNA binding affinity

We have developed a non‐redundant protein–RNA binding benchmark dataset derived from the available protein–RNA structures in the Protein Database Bank. It consists of 73 complexes with measured binding affinity. The experimental conditions (pH and temperature) for binding affinity measurements are also listed in our dataset. This binding affinity dataset can be used to compare and develop protein–RNA scoring functions. The predicted binding free energy of the 73 complexes from three available scoring functions for protein–RNA docking has a low correlation with the binding Gibbs free energy calculated from K_d. © 2013 The Protein Society     

A benchmark testing ground for integrating homology modeling and protein docking

Protein docking procedures carry out the task of predicting the structure of a protein–protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved ‘target’ complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody–antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark , and is updated on a weekly basis in synchrony with new PDB releases. Proteins 2016; 85:10–16. © 2016 Wiley Periodicals, Inc.