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.     

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.     

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.     

Structural quality of unrefined models in protein docking

Structural characterization of protein‐protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template‐based docking techniques that utilize structural similarity between target protein‐protein interaction and cocrystallized protein‐protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template‐free docking. However, the template‐based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template‐based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template‐based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template‐based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template‐based docking are comparable to those in the free docking, due to the overall higher quality of the template‐based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template‐based docking predictions as well. Proteins 2016; 85:39–45. © 2016 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.     

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.     

Protein–protein alternative binding modes do not overlap

Proteins often bind other proteins in more than one way. Thus alternative binding modes is an essential feature of protein interactions. Such binding modes may be detected by X‐ray crystallography and thus reflected in Protein Data Bank. The alternative binding is often observed not for the protein itself but for its structural homolog. The results of this study based on the analysis of a comprehensive set of co‐crystallized protein–protein complexes show that the alternative binding modes generally do not overlap, but are spatially separated. This effect is based on molecular recognition characteristics of the protein structures. The results are also in excellent agreement with the intermolecular energy funnel size estimates obtained previously by an independent methodology. The results provide an important insight into the principles of protein association, as well as potential guidelines for modeling of protein complexes and the design of protein interfaces.     

Linkers in the structural biology of protein–protein interactions

Linkers or spacers are short amino acid sequences created in nature to separate multiple domains in a single protein. Most of them are rigid and function to prohibit unwanted interactions between the discrete domains. However, Gly‐rich linkers are flexible, connecting various domains in a single protein without interfering with the function of each domain. The advent of recombinant DNA technology made it possible to fuse two interacting partners with the introduction of artificial linkers. Often, independent proteins may not exist as stable or structured proteins until they interact with their binding partner, following which they gain stability and the essential structural elements. Gly‐rich linkers have been proven useful for these types of unstable interactions, particularly where the interaction is weak and transient, by creating a covalent link between the proteins to form a stable protein–protein complex. Gly‐rich linkers are also employed to form stable covalently linked dimers, and to connect two independent domains that create a ligand‐binding site or recognition sequence. The lengths of linkers vary from 2 to 31 amino acids, optimized for each condition so that the linker does not impose any constraints on the conformation or interactions of the linked partners. Various structures of covalently linked protein complexes have been described using X‐ray crystallography, nuclear magnetic resonance and cryo‐electron microscopy techniques. In this review, we evaluate several structural studies where linkers have been used to improve protein quality, to produce stable protein–protein complexes, and to obtain protein dimers.     

Structural templates for comparative protein docking

Structural characterization of protein‐protein interactions is important for understanding life processes. Because of the inherent limitations of experimental techniques, such characterization requires computational approaches. Along with the traditional protein‐protein docking (free search for a match between two proteins), comparative (template‐based) modeling of protein‐protein complexes has been gaining popularity. Its development puts an emphasis on full and partial structural similarity between the target protein monomers and the protein‐protein complexes previously determined by experimental techniques (templates). The template‐based docking relies on the quality and diversity of the template set. We present a carefully curated, nonredundant library of templates containing 4950 full structures of binary complexes and 5936 protein‐protein interfaces extracted from the full structures at 12 Å distance cut‐off. Redundancy in the libraries was removed by clustering the PDB structures based on structural similarity. The value of the clustering threshold was determined from the analysis of the clusters and the docking performance on a benchmark set. High structural quality of the interfaces in the template and validation sets was achieved by automated procedures and manual curation. The library is included in the Dockground resource for molecular recognition studies at http://dockground.bioinformatics.ku.edu . Proteins 2015; 83:1563–1570. © 2014 Wiley Periodicals, Inc.     

A systematic analysis of scoring functions in rigid‐body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining

Protein‐protein interactions play fundamental roles in biological processes including signaling, metabolism, and trafficking. While the structure of a protein complex reveals crucial details about the interaction, it is often difficult to acquire this information experimentally. As the number of interactions discovered increases faster than they can be characterized, protein‐protein docking calculations may be able to reduce this disparity by providing models of the interacting proteins. Rigid‐body docking is a widely used docking approach, and is often capable of generating a pool of models within which a near‐native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. Recently, more than 100 scoring functions from the CCharPPI server were evaluated for this task using decoy structures generated with SwarmDock. Here, we extend this analysis to identify the predictive success rates of the scoring functions on decoys from three rigid‐body docking programs, ZDOCK, FTDock, and SDOCK, allowing us to assess the transferability of the functions. We also apply set‐theoretic measure to test whether the scoring functions are capable of identifying near‐native poses within different subsets of the benchmark. This information can provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts. Proteins 2017; 85:1287–1297. © 2017 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.     

An inhibitory mechanism of action of coiled‐coil peptides against type three secretion system from enteropathogenic Escherichia coli

Human pathogenic gram‐negative bacteria, such as enteropathogenic Escherichia coli (EPEC), rely on type III secretion systems (T3SS) to translocate virulence factors directly into host cells. The coiled‐coil domains present in the structural proteins of T3SS are conformed by amphipathic alpha‐helical structures that play an important role in the protein‐protein interaction and are essential for the assembly of the translocation complex. To investigate the inhibitory capacity of these domains on the T3SS of EPEC, we synthesized peptides between 7 and 34 amino acids based on the coiled‐coil domains of proteins that make up this secretion system. This analysis was performed through in vitro hemolysis assays by assessing the reduction of T3SS‐dependent red blood cell lysis in the presence of the synthesized peptides. After confirming its inhibitory capacity, we performed molecular modeling assays using combined techniques, docking‐molecular dynamic simulations, and quantum‐mechanic calculations of the various peptide‐protein complexes, to improve the affinity of the peptides to the target proteins selected from T3SS. These techniques allowed us to demonstrate that the peptides with greater inhibitory activity, directed against the coiled‐coil domain of the C‐terminal region of EspA, present favorable hydrophobic and hydrogen bond molecular interactions. Particularly, the hydrogen bond component is responsible for the stabilization of the peptide‐protein complex. This study demonstrates that compounds targeting T3SS from pathogenic bacteria can indeed inhibit bacterial infection by presenting a higher specificity than broad‐spectrum antibiotics. In turn, these peptides could be taken as initial structures to design and synthesize new compounds that mimic their inhibitory pharmacophoric pattern.     

Protein–protein association rates captured in a single geometric parameter

Understanding factors that drive protein–protein association is of fundamental importance. We show that a single geometric parameter in crystal structures of protein–protein complexes, the angle between the electric dipole of one subunit and the partner‐generated electric field at the same subunit, linearly correlates with experimentally determined protein–protein association rates. Imprint of a dynamic kinetic process in a single static geometric parameter, associated with mutual electrostatic orientation of subunits in protein–protein complexes, is elegant and demonstrates the universality of electrostatic steering in attenuating protein–protein association rates. That the essence of a complex phenomenon could be captured by properties of the final crystal structure of the complex implies that the electrostatic orientations of protein subunits in crystal structures and the associated transition states are nearly identical. Further, the cosine of the angle, alone, is shown to be sufficient in predicting association rate constants, with accuracies comparable to currently available predictors that use more intricate methodologies. Our results offer mechanistic insights and could be useful in development of coarse‐grained models. Proteins 2015; 83:1557–1562. © 2015 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.     

Distance restraints from crosslinking mass spectrometry: Mining a molecular dynamics simulation database to evaluate lysine–lysine distances

Integrative structural biology attempts to model the structures of protein complexes that are challenging or intractable by classical structural methods (due to size, dynamics, or heterogeneity) by combining computational structural modeling with data from experimental methods. One such experimental method is chemical crosslinking mass spectrometry (XL‐MS), in which protein complexes are crosslinked and characterized using liquid chromatography‐mass spectrometry to pinpoint specific amino acid residues in close structural proximity. The commonly used lysine‐reactive N‐hydroxysuccinimide ester reagents disuccinimidylsuberate (DSS) and bis(sulfosuccinimidyl)suberate (BS³) have a linker arm that is 11.4 Å long when fully extended, allowing C_α (alpha carbon of protein backbone) atoms of crosslinked lysine residues to be up to ～24 Å apart. However, XL‐MS studies on proteins of known structure frequently report crosslinks that exceed this distance. Typically, a tolerance of ～3 Å is added to the theoretical maximum to account for this observation, with limited justification for the chosen value. We used the Dynameomics database, a repository of high‐quality molecular dynamics simulations of 807 proteins representative of diverse protein folds, to investigate the relationship between lysine–lysine distances in experimental starting structures and in simulation ensembles. We conclude that for DSS/BS³, a distance constraint of 26–30 Å between C_α atoms is appropriate. This analysis provides a theoretical basis for the widespread practice of adding a tolerance to the crosslinker length when comparing XL‐MS results to structures or in modeling. We also discuss the comparison of XL‐MS results to MD simulations and known structures as a means to test and validate experimental XL‐MS methods.     

PIE—Efficient filters and coarse grained potentials for unbound protein–protein docking

Identifying correct binding modes in a large set of models is an important step in protein–protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two‐chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near‐native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near‐native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Structural motifs in protein cores and at protein–protein interfaces are different

Structures of proteins and protein–protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein–protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure‐based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue–residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein–protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein–protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking—discrimination of the intramolecular information not directly relevant to docking.     

Protein models: The Grand Challenge of protein docking

Characterization of life processes at the molecular level requires structural details of protein–protein interactions (PPIs). The number of experimentally determined protein structures accounts only for a fraction of known proteins. This gap has to be bridged by modeling, typically using experimentally determined structures as templates to model related proteins. The fraction of experimentally determined PPI structures is even smaller than that for the individual proteins, due to a larger number of interactions than the number of individual proteins, and a greater difficulty of crystallizing protein–protein complexes. The approaches to structural modeling of PPI (docking) often have to rely on modeled structures of the interactors, especially in the case of large PPI networks. Structures of modeled proteins are typically less accurate than the ones determined by X‐ray crystallography or nuclear magnetic resonance. Thus the utility of approaches to dock these structures should be assessed by thorough benchmarking, specifically designed for protein models. To be credible, such benchmarking has to be based on carefully curated sets of structures with levels of distortion typical for modeled proteins. This article presents such a suite of models built for the benchmark set of the X‐ray structures from the Dockground resource ( http://dockground.bioinformatics.ku.edu ) by a combination of homology modeling and Nudged Elastic Band method. For each monomer, six models were generated with predefined C^α root mean square deviation from the native structure (1, 2, …, 6 Å). The sets and the accompanying data provide a comprehensive resource for the development of docking methodology for modeled proteins. Proteins 2014; 82:278–287. © 2013 Wiley Periodicals, Inc.     

Extension of a protein docking algorithm to membranes and applications to amyloid precursor protein dimerization

Novel adjustments are introduced to the docking algorithm, DOCK/PIERR, for the purpose of predicting structures of transmembrane protein complexes. Incorporating knowledge about the membrane environment is shown to significantly improve docking accuracy. The extended version of DOCK/PIERR is shown to perform comparably to other leading docking packages. This membrane version of DOCK/PIERR is applied to the prediction of coiled‐coil homodimer structures of the transmembrane region of the C‐terminal peptide of amyloid precursor protein (C99). Results from MD simulation of the C99 homodimer in POPC bilayer and docking are compared. Docking results are found to capture key aspects of the homodimer ensemble, including the existence of three topologically distinct conformers. Furthermore, the extended version of DOCK/PIERR is successful in capturing the effects of solvation in membrane and micelle. Specifically, DOCK/PIERR reproduces essential differences in the homodimer ensembles simulated in POPC bilayer and DPC micelle, where configurational entropy and surface curvature effects bias the handedness and topology of the homodimer ensemble. Proteins 2015; 83:2170–2185. © 2015 Wiley Periodicals, Inc.