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Computational docking approaches aim to overcome the limited availability of experimental structural data on protein–protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling., This work was supported by grant BIO2016-79930-R from the Spanish ‘Programa Estatal I+D+I’, and EFA086/15 PIREPRED from the EU European Regional Development Fund (ERDF) Program Interreg V-A Spain-France-Andorra (POCTEFA).

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands., Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC001003

Graphical abstract, Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein–protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein–protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein–ligand docking. The proposed strategy will be useful for many protein–protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.

Motivation Single protein residue mutations may reshape the binding affinity of protein¿protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein¿protein complexes trained on interactome data. Results Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein¿protein mutations using UEP, a fast and reliable open-source code. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online., This work was supported by a predoctoral fellowship from the Government of Catalonia (2018FI_B_00873 to P.A.-R.) and grants from the Spanish government ‘Programa Estatal I+D+i’ (BIO2016-79930-R), (PID2019-110167RB-I00) and (CTQ2016-79138-R), and by grant PIREPRED from the EU European Regional Development Fund program Interreg V-A Spain-France-Andorra (POCTEFA). This work was also received funding from the IBM-BSC Deep Learning Center (2016).

[Motivation] Computational prediction of protein–protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed., [Results] We describe here a new multi-scale protein–protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigidbody docking, especially in flexible cases., B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness. This work was supported by grants BIO2013-48213-R, SEV-2015-0493, TIN2015-65316-P, and BIO2016- 79930-R from the Spanish Ministry of Economy and Competitiveness, GA 687698 from the EU H2020 program, and 2014-SGR-1051 from Universitats i Empresa program of Generalitat de Catalunya.

The seventh CAPRI edition imposed new challenges to the modeling of protein-protein complexes, such as multimeric oligomerization, protein-peptide, and protein-oligosaccharide interactions. Many of the proposed targets needed the efficient integration of rigid-body docking, template-based modeling, flexible optimization, multiparametric scoring, and experimental restraints. This was especially relevant for the multimolecular assemblies proposed in the CASP12-CAPRI37 and CASP13-CAPRI46 joint rounds, which were described and evaluated elsewhere. Focusing on the purely CAPRI targets of this edition (rounds 38-45), we have participated in all 17 assessed targets (considering heteromeric and homomeric interfaces in T125 as two separate targets) both as predictors and as scorers, by using integrative modeling based on our docking and scoring approaches: pyDock, IRaPPA, and LightDock. In the protein-protein and protein-peptide targets, we have also participated with our webserver (pyDockWeb). On these 17 CAPRI targets, we submitted acceptable models (or better) within our top 10 models for 10 targets as predictors, 13 targets as scorers, and 4 targets as servers. In summary, our participation in this CAPRI edition confirmed the capabilities of pyDock for the scoring of docking models, increasingly used within the context of integrative modeling of protein interactions and multimeric assemblies., This work has been funded by grant number BIO2016-79930-R from the Spanish “Programa Estatal I+D+i”, “PIREPRED” grant from the EU European Regional Development Fund (ERDF) Program Interreg V-A Spain-France-Andorra (POCTEFA), Research Contract with SIDRA Medicine (Qatar), and contract 676566 (grant “MuG”) from the European Union H2020 programme. B. J. -G. and M. R. were supported by FPI fellowships from the Spanish “Programa Estatal I+D+i” and the Severo Ochoa program, respectively.

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc.

One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments., This research was funding by the EU European Regional Development Fund (ERDF) through the Program Interreg V-A Spain-France-Andorra (POCTEFA), by the CSIC (intramural grant number 201720I031), and by the Spanish Ministry of Economy and Competitiveness (grants BIO2016-79930-R and SAF2016-80255-R). M.R. is recipient of an FPI fellowship from the Severo Ochoa program, We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein-protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein-protein interactions, or providing modeled structural data for drug discovery targeting protein-protein interactions.

[Introduction] Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions., [Areas covered] In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions., [Expert opinion] A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis, This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions. Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level., This work was funded by grants number BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness, and grant number EFA086/15 from Interreg POCTEFA. D. Barradas-Bautista was supported by a CONACyT predoctoral fellowship from the Mexican Government.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Conformational fluctuations in proteins play key roles in their functions and interactions. In this work, validated conformational ensembles for ubiquitin have been used in docking trials. The ensembles were used in a systematic predictive study of known ubiquitin complexes by applying a cross-docking strategy against the bound structure of each partner. The global docking predictions obtained with the complete ubiquitin ensembles were significantly better than those obtained with the crystallographic structure of free ubiquitin. Importantly, in all cases we identified an individual ensemble member that performed equally well, or even better, than the bound structure of ubiquitin. These results unequivocally demonstrate that, for proteins that recognize binding partners by conformational selection, the availability of conformational ensembles can greatly improve the performance of automatic docking predictions. Our results highlight the need for docking methodologies to capitalize on validated ensemble representations of biomacromolecules.

An important aspect of protein functionality is the formation of specific complexes with other proteins, which are involved in the majority of biological processes. The functional characterization of such interactions at molecular level is necessary, not only to understand biological and pathological phenomena but also to design improved, or even new interfaces, or to develop new therapeutic approaches. X-ray crystallography and NMR spectroscopy have increased the number of 3D protein complex structures deposited in the Protein Data Bank (PDB). However, one of the more challenging objectives in biological research is to functionally characterize protein interactions and thus identify residues that significantly contribute to the binding. Considering that the experimental characterization of protein interfaces remains expensive, time-consuming, and labor-intensive, computational approaches represent a significant breakthrough in proteomics, assisting or even replacing experimental efforts. Thanks to the technological advances in computing and data processing, these techniques now cover a vast range of protocols, from the estimation of the evolutionary conservation of amino acid positions in a protein, to the energetic contribution of each residue to the binding affinity. In this chapter, we review several existing computational protocols to model the phylogenetic, structural, and energetic properties of residues within protein-protein interfaces.

An important aspect of protein functionality is the formation of specific complexes with other proteins, which are involved in the majority of biological processes. The functional characterization of such interactions at molecular level is necessary, not only to understand biological and pathological phenomena but also to design improved, or even new interfaces, or to develop new therapeutic approaches. X-ray crystallography and NMR spectroscopy have increased the number of 3D protein complex structures deposited in the Protein Data Bank (PDB). However, one of the more challenging objectives in biological research is to functionally characterize protein interactions and thus identify residues that significantly contribute to the binding. Considering that the experimental characterization of protein interfaces remains expensive, timeconsuming, and labor-intensive, computational approaches represent a significant breakthrough in proteomics, assisting or even replacing experimental efforts. Thanks to the technological advances in computing and data processing, these techniques now cover a vast range of protocols, from the estimation of the evolutionary conservation of amino acid positions in a protein, to the energetic contribution of each residue to the binding affinity. In this chapter, we review several existing computational protocols to model the phylogenetic, structural, and energetic properties of residues within protein–protein interfaces.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. We are most grateful to the PDBe at the European Bioinformatics Institute in Hinxton, UK, for hosting the CAPRI website. Our deepest thanks go to all the structural biologists and to the following structural genomics initiatives: Northeast Structural Genomics Consortium, Joint Center for Structural Genomics, NatPro PSI:Biology, New York Structural Genomics Research Center, Midwest Center for Structural Genomics, Structural Genomics Consortium, for contributing the targets for this joint CASP-CAPRI experiment. MFL acknowledges support from the FRABio FR3688 Research Federation “Structural & Functional Biochemistry of Biomolecular Assemblies.”

The sixth CAPRI edition included new modeling challenges, such as the prediction of protein-peptide complexes, and the modeling of homo-oligomers and domain-domain interactions as part of the first joint CASP-CAPRI experiment. Other non-standard targets included the prediction of interfacial water positions and the modeling of the interactions between proteins and nucleic acids. We have participated in all proposed targets of this CAPRI edition both as predictors and as scorers, with new protocols to efficiently use our docking and scoring scheme pyDock in a large variety of scenarios. In addition, we have participated for the first time in the servers section, with our recently developed webserver, pyDockWeb. Excluding the CASP-CAPRI cases, we submitted acceptable models (or better) for 7 out of the 18 evaluated targets as predictors, 4 out of the 11 targets as scorers, and 6 out of the 18 targets as servers. The overall success rates were below those in past CAPRI editions. This shows the challenging nature of this last edition, with many difficult targets for which no participant submitted a single acceptable model. Interestingly, we submitted acceptable models for 83% of the evaluated protein-peptide targets. As for the 25 cases of the CASP-CAPRI experiment, in which we used a larger variety of modeling techniques (template-based, symmetry restraints, literature information, etc.), we submitted acceptable models for 56% of the targets. In summary, this CAPRI edition showed that pyDock scheme can be efficiently adapted to the increasing variety of problems that the protein interactions field is currently facing. Proteins 2017; 85:487-496. © 2016 Wiley Periodicals, Inc.

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc.

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.

Protein–protein interactions are involved in the majority of cell processes, and their detailed structural and functional characterization has become one of the most important challenges in current structural biology. The first ideal goal is to determine the structure of the specific complex formed upon interaction of two or more given proteins. However, since this is not always technically possible, the practical approach is often to locate and characterize the protein residues that are involved in the interaction. This can be achieved by experimental means at expense of time and cost, so a growing number of computer tools are becoming available to complement experimental efforts. Reported methods for interface prediction are based on sequence information or on structural data, and make use of a variety of evolutionary, geometrical, and physicochemical parameters. As we show here, computer predictions can achieve a high degree of success, and they are of practical use to guide mutational experiments as well as to explain functional and mechanistic aspects of the interaction. Interestingly, it has been found that typically only a few of the interacting residues contribute significantly to the binding energy. The identification of such hot-spot residues is important for understanding basic aspects of protein association. In addition, these residues have received recent attention as possible targets for drug design, so several computer methods have been developed to predict them. We will review here existing computer approaches for the prediction of protein binding sites and hot-spot residues, with a discussion on their applicability and limitations. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 680–698 DOI: 10.1002/wcms.45

We describe here our results in the last CAPRI edition. We have participated in all targets, both as predictors and as scorers, using our pyDock docking methodology. The new challenges (homology-based modeling of the interacting subunits, domain–domain assembling, and protein-RNA interactions) have pushed our computer tools to the limits and have encouraged us to devise new docking approaches. Overall, the results have been quite successful, in line with previous editions, especially considering the high difficulty of some of the targets. Our docking approaches succeeded in five targets as predictors or as scorers (T29, T34, T35, T41, and T42). Moreover, with the inclusion of available information on the residues expected to be involved in the interaction, our protocol would have also succeeded in two additional cases (T32 and T40). In the remaining targets (except T37), results were equally poor for most of the groups. We submitted the best model (in ligand RMSD) among scorers for the unbound-bound target T29, the second best model among scorers for the protein-RNA target T34, and the only correct model among predictors for the domain assembly target T35. In summary, our excellent results for the new proposed challenges in this CAPRI edition showed the limitations and applicability of our approaches and encouraged us to continue developing methodologies for automated biomolecular docking. Proteins 2010. © 2010 Wiley-Liss, Inc.

Translin is a single-stranded RNA- and DNA-binding protein, which has been highly conserved in eukaryotes, from man to Schizosaccharomyces pombe. TRAX is a Translin paralog associated with Translin, which has coevolved with it. We generated structural models of the S. pombe Translin (spTranslin), based on the solved 3D structure of the human ortholog. Using several bioinformatics computation tools, we identified in the equatorial part of the protein a putative nucleic acids interaction surface, which includes many polar and positively charged residues, mostly arginines, surrounding a shallow cavity. Experimental verification of the bioinformatics predictions was obtained by assays of nucleic acids binding to amino acid substitution variants made in this region. Bioinformatics combined with yeast two-hybrid assays and proteomic analyses of deletion variants, also identified at the top of the spTranslin structure a region required for interaction with spTRAX, and for spTranslin dimerization. In addition, bioinformatics predicted the presence of a second protein-protein interaction site at the bottom of the spTranslin structure. Similar nucleic acid and protein interaction sites were also predicted for the human Translin. Thus, our results appear to generally apply to the Translin family of proteins, and are expected to contribute to a further elucidation of their functions.

The study of protein-protein interactions that are involved in essential life processes can largely benefit from the recent upraising of computational docking approaches. Predicting the structure of a protein-protein complex from their separate components is still a highly challenging task, but the field is rapidly improving. Recent advances in sampling algorithms and rigid-body scoring functions allow to produce, at least for some cases, high quality docking models that are perfectly suitable for biological and functional annotations, as it has been shown in the CAPRI blind tests. However, important challenges still remain in docking prediction. For example, in cases with significant mobility, such as multidomain proteins, fully unrestricted rigid-body docking approaches are clearly insufficient so they need to be combined with restraints derived from domain-domain linker residues, evolutionary information, or binding site predictions. Other challenging cases are weak or transient interactions, such as those between proteins involved in electron transfer, where the existence of alternative bound orientations and encounter complexes complicates the binding energy landscape. Docking methods also struggle when using in silico structural models for the interacting subunits. Bringing these challenges to a practical point of view, we have studied here the limitations of our docking and energy-based scoring approach, and have analyzed different parameters to overcome the limitations and improve the docking performance. For that, we have used the standard benchmark and some practical cases from CAPRI. Based on these results, we have devised a protocol to estimate the success of a given docking run.

There is increasing evidence of the important role of innate immune system in tumor surveillance (Vesely et al., 2011). The spontaneous immune response of T cells against tumors has a powerful prognostic value and can contribute to tumor control in patients. Indeed, T cell-based immunotherapies have shown to be effective in a variety of human cancers (Schumacher and Schreiber, 2015). A major player in this innate immune response is the stimulator of interferon genes (STING) protein, which is activated when a cell is infected by pathogens (Tanaka and Chen, 2012) or in the presence of tumor-derived cytosolic DNA (Woo et al., 2014). This pathway is critical for spontaneous T cell priming against tumors. Thus, understanding this mechanism at molecular level can be of paramount importance for improving prognosis and tumor-growth control in patients, as well as for developing new biomarkers and therapeutic vaccines. The activation of STING induces IFN-β production, and occurs after binding cyclic GMP-AMP (cGAMP), derived from pathogen or host-damaged cells. After that, the C-terminal tail (CTT) of STING is phosphorylated by TANK-binding kinase 1 (TBK1). Small-molecule ligands of STING are thus potential candidates for anti-cancer drugs or vaccine adjuvants. There are several available structures of cGAMP bound to STING, but none of them include the functionally relevant CTT segment, probably because of its flexibility. As a consequence, the structural changes that ligands such as cGAMP induce in STING CTT to make it phosphorylatable by TBK1 are not fully understood. The limitations of structure determination and the rather static picture that most of the currently available structural techniques provide are hampering the design of new STING ligands. In this context, computational methods such as molecular dynamics (MD) simulations can provide a detailed description of the thermodynamic properties and time-dependent phenomena of proteins (McCammon et al., 1977). Thanks to the exponential growth in computational power, MD simulations are currently applied to study dynamic events in proteins and other biomolecules at biologically relevant timescales (Shaw et al., 2010). This is changing the way we study the functional mechanisms of life systems, and the integration of structural data, molecular biology experiments and computational simulations will be essential for a full understanding of the complex biological and pathological processes that occur in cells. As 2013 Novel Laureate in Chemistry Michael Levitt said in 1998, “Computing has changed biology forever; most biologists just don't know it yet”. In EBioMedicine, Tsuchiya et al. (2016) identify the mechanism of STING activation at molecular level by using a combination of computational simulations and experimental approaches. They have studied the conformational effect induced by the ligand cGAMP on the flexible C-terminal tail (CTT) of the innate immune protein STING, by molecular dynamics (MD) simulations. The MD trajectories used in their study were long enough (1 μs) to observe the effects of cGAMP ligand binding on the flexibility of the CTT region. The findings from the molecular dynamics analyses were confirmed by molecular biology experiments, which allowed them to propose a new mechanism for STING activation. They found that in cGAMP-bound STING: i) the closed-Lid conformation was kept throughout the simulation, which was previously shown as essential for IFN-β activity; (Gao et al., 2014) ii) the end of CTT moved onto the Lid; and iii) the CTT region acquired a transient but defined β-sheet structure, which resembles the structure of the SER-rich region of IRF3 also phosphorylated by TBK1 (Takahasi et al., 2010). The importance of the residues forming the β-sheet in CTT for the activation of IFN-β was proved by site-directed mutagenesis experiments. The findings in Tsuchiya and colleagues' study open the door for future in silico screening studies in search of candidate molecules for therapeutic intervention. As they propose in their work, attention must be paid not only to the ligand-binding domain of STING but also to the effect of ligand binding in the flexible CTT region. The proposed model based on MD simulations can help to plan new virtual and experimental screening schemes in search of potent STING ligands that can promote IFN-β activity. This could lead to new anti-tumor therapeutic strategies through the enhancement of spontaneous immune response in patients. Tsuchiya and colleagues' study is a nice example of how the combination of computational simulations and molecular biology experiments can contribute to improve our understanding of relevant problems in biomedicine, such as the natural immune response against tumors.

RNA-binding proteins are involved in many important regulatory processes in cells and their study is essential for a complete understanding of living organisms. They show a large variability from both structural and functional points of view. However, several recent studies performed on protein-RNA crystal structures have revealed interesting common properties. RNA-binding sites usually constitute patches of positively charged or polar residues that make most of the specific and non-specific contacts with RNA. Negatively charged or aliphatic residues are less frequent at protein-RNA interfaces, although they can also be found either forming aliphatic and positive-negative pairs in protein RNA-binding sites or contacting RNA through their main chains. Aromatic residues found within these interfaces are usually involved in specific base recognition at RNA single-strand regions. This specific recognition, in combination with structural complementarity, represents the key source for specificity in protein-RNA association. From all this knowledge, a variety of computational methods for prediction of RNA-binding sites have been developed based either on protein sequence or on protein structure. Some reported methods are really successful in the identification of RNA-binding proteins or the prediction of RNA-binding sites. Given the growing interest in the field, all these studies and prediction methods will undoubtedly contribute to the identification and comprehension of protein-RNA interactions.

The ICM Docking and Interface Side-Chain Optimization (ICM-DISCO) showed promising predictive results during the first CAPRI experiment by successfully finding medium- or high-accuracy models in 3 of the 7 targets. A key factor was the ability to recognize near-native rigid-body geometries in a relatively low number of alternative docking poses, together with the successful refinement of the rigid-body docking interfaces. Since then, we have focused on improving the scoring function to optimally discriminate the near-native rigid-body conformations. For that, we have defined a new desolvation descriptor for rigid-body docking, based on atomic solvation parameters (ASPs) derived from octanol-water transfer experiments. This and other new approaches have been gradually incorporated into our docking procedure during our participation on the second CAPRI experiment. Overall, we produced reasonable models for 8 of the 9 official targets. Especially encouraging were those cases in which a homology model of 1 of the subunits had to be used during the docking simulations. And not less gratifying has been the successful prediction of antibody-antigen targets in a completely automatic, unrestrained fashion. In summary, our success rate (89%) shows a consistent improvement over the previous CAPRI rounds, and suggests that a correct desolvation description is key for improved protein-protein docking predictions.

Background Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling. Results We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically. Conclusions All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

In addition to protein-protein docking, this CAPRI edition included new challenges, like protein-water and protein-sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein-protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small-angle X-ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein-protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water-mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein-carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics. Proteins 2013; 81:2192-2200. © 2013 Wiley Periodicals, Inc.

The computational evaluation of protein–protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning.

Background: Computational approaches such as docking and scoring are becoming routine in drug discovery as a complement to other more traditional techniques. However, so far, computer drug design methods have been applied to inhibit the function of individual proteins, and there is little available data on the use of these computational techniques to target protein–protein interactions. Objective: To establish a strategy for the use of current computational tools in drug discovery targeting protein–protein interactions. Method: Individual techniques applied to specific cases could be studied to derive a general strategy for targeting protein–protein interactions. Conclusion: Protein docking, interface prediction and hot-spot identification can contribute to the discovery of small molecule inhibitors targeting protein interactions of therapeutic interest, especially when little structural information is available.

The covalent attachment of adenosine monophosphate (AMP) to proteins, a process called AMPylation (adenylylation), has recently emerged as a novel theme in microbial pathogenesis. Although several AMPylating enzymes have been characterized, the only known virulence protein with de-AMPylation activity is SidD from the human pathogen Legionella pneumophila. SidD de-AMPylates mammalian Rab1, a small GTPase involved in secretory vesicle transport, thereby targeting the host protein for inactivation. The molecular mechanisms underlying Rab1 recognition and de-AMPylation by SidD are unclear. Here, we report the crystal structure of the catalytic region of SidD at 1.6 Å resolution. The structure reveals a phosphatase-like fold with additional structural elements not present in generic PP2C-type phosphatases. The catalytic pocket contains a binuclear metal-binding site characteristic of hydrolytic metalloenzymes, with strong dependency on magnesium ions. Subsequent docking and molecular dynamics simulations between SidD and Rab1 revealed the interface contacts and the energetic contribution of key residues to the interaction. In conjunction with an extensive structure-based mutational analysis, we provide in vivo and in vitro evidence for a remarkable adaptation of SidD to its host cell target Rab1 which explains how this effector confers specificity to the reaction it catalyses., Author Summary The covalent attachment of adenosine monophosphate (AMP) to proteins, a process called AMPylation (adenylylation), has recently emerged as a novel theme in microbial pathogenesis. While AMPylases from various pathogenic microorganisms have recently been characterized, the only virulence protein with de-AMPylation activity known to date is the Legionella pneumophila effector SidD which catalyzes AMP removal from the host GTPase Rab1. Thus, both AMPylation and de-AMPylation constitute a novel catalytic mechanism to precisely control the function and membrane dynamics of a host Rab GTPase. In spite of this pivotal role, the molecular mechanism of AMP removal and the structural determinants for Rab1 recognition by SidD have remained largely unexplored. Here, we present the crystal structure of the de-adenylylation domain of SidD and reveal the catalytic mechanism of Rab1 de-adenylylation. Surprisingly, the structure of SidD is not related to the other known enzyme with de-AMPylation activity, the Escherichia coli GS-ATase. Instead, the catalitic domain of SidD is remarkably similar to that of the metal-dependent protein phosphatases (PPMs), however with distinctive structural features to distinguish AMPylated Rab1 from similarly modified substrates. Importantly, we provide a model for the SidD-Rab1 complex which sheds light into the specific details of substrate recognition and catalysis by this virulence factor.

Most processes in living organisms occur through an intricate network of protein-protein interactions, in which any malfunctioning can lead to pathological situations. Therefore, current research in biomedicine is starting to focus on protein interaction networks. A detailed structural knowledge of these interactions at molecular level will be necessary for drug discovery targeting protein-protein interactions. The challenge from a structural biology point of view is determining the structure of the specific complex formed upon interaction of two or several proteins, and/or locating the surface residues involved in the interaction and identify which of them are the most important ones for binding (hot-spots). In this line, an increasing number of computer tools are available to complement experimental efforts. Docking algorithms can achieve successful predictive rates in many complexes, as shown in the community assessment experiment CAPRI, and have already been applied to a variety of cases of biomedical interest. On the other side, many methods for interface and hotspot prediction have been reported, based on a variety of evolutionary, geometrical and physico-chemical parameters. Computer predictions are reaching a significant level of maturity, and can be very useful to guide experiments and suggest mutations, or to provide a mechanistic framework to the experimental results on a given interaction. We will review here existing computer approaches for proteinprotein docking, interface prediction and hot-spot identification, with focus to drug discovery targeting protein-protein interactions.

No abstract is available in fulltext.

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.

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.

Protein-protein interactions are fundamental for the majority of biological processes, so their structural, functional, and energetic characterization is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational docking approaches to the structural prediction of protein-protein complexes have been reported, with encouraging results. However, a major bottleneck is found in cases with conformational movements upon binding, for which docking algorithms have to be extended beyond the rigid-body framework by introducing flexibility. Given the high computational cost of flexible docking, coarse-grained models offer an efficient alternative to full-atom descriptions. This work describes pyDockCG, a new coarse-grained potential for protein-protein docking scoring and refinement, based on the known UNRES model for polypeptide chains. The main novelty is the inclusion of two new terms accounting for the Coulomb electrostatics and the solvation energy. The latter has been devised by adapting the EEF1 model to the coarse-grained approach, with optimal parameters for protein-protein docking. The coarse-grained potential yielded highly similar values to the full-atom scoring function pyDock when applied to the rigid body docking sets, but at much lower computational cost. This efficiency makes it suitable for the treatment of flexibility during docking.

Despite the importance of protein-RNA interactions in the cellular context, the number of available protein-RNA complex structures is still much lower than those of other biomolecules. As a consequence, few computational studies have been addressed towards protein-RNA complexes, and to our knowledge, no systematic benchmarking of protein-RNA docking has been reported. In this study we have extracted new pairwise residue-ribonucleotide interface propensities for protein-RNA, which can be used as statistical potentials for scoring of protein-RNA docking poses. We show here a new protein-RNA docking approach based on FTDock generation of rigid-body docking poses, which are later scored by these statistical residue-ribonucleotide potentials. The method has been successfully benchmarked in a set of 12 protein-RNA cases. The results show that FTDock is able to generate near-native solutions in more than half of the cases, and that it can rank near-native solutions significantly above random. In practically all these cases, our propensity-based scoring helps to improve the docking results, finding a near-native solution within rank 100 in 43% of them. In a remarkable case, the near-native solution was ranked 1 after the propensity-based scoring. Other previously described propensity potentials can also be used for scoring, with slightly worse performance. This new protein-RNA docking protocol permits a fast scoring of rigid-body docking poses in order to select a small number of docking orientations, which can be later evaluated with more sophisticated energy-based scoring functions.

Protein-RNA interactions are essential in living organisms and they are involved in very different and important cellular processes. Thus, understanding protein-RNA recognition at molecular level is a key goal not only from a basic biological point of view but also for biotechnological and therapeutic purposes. On basis of the most updated available set of nonredundant X-ray structures of protein-RNA complexes, we have computed protein-RNA interface propensities for ribonucleotides and amino acid residues. The results show several protein residues with high tendency to bind RNA, such as arginine, lysine, and histidine. However, we could not observe any clear preferences for protein binding among the different ribonucleotides. We applied these propensity values to predict RNA-binding areas on proteins, using an ad hoc algorithm called OPRA (Optimal Protein-RNA Area). First, for each protein residue, we derived a predictive score from its corresponding protein-RNA interface propensity weighed by its accessible surface area (ASA). Then, optimal patch energy scores were computed for each residue by adding up the individual scores of the neighboring surface residues. The resulting patch scores correlate well with the known RNA-binding sites on protein surfaces. The OPRA method has been benchmarked on a test set of 30 unbound proteins involved in protein-RNA complexes of known structure, where it is able to successfully predict RNA-binding sites on protein surfaces with around 80% positive predictive value. This can be useful for identifying potential RNA-binding sites on proteins, and can help to model protein-RNA interactions of biological and therapeutic interest.

The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/., Author Summary Proteins are the main perpetrators of most biological processes. However, they seldom act alone, and most cellular functions are, in fact, carried out by large macromolecular complexes and regulated through intricate protein-protein interaction networks. Consequently, large efforts have been devoted to unveil protein interrelationships in a high-throughput manner, and the last several years have seen the consecution of the first interactome drafts for several model organisms. Unfortunately, these studies only reveal whether two proteins interact, but not the molecular bases of these interactions. A full comprehension of how proteins bind and form complexes can only come from high-resolution, three-dimensional (3D) structures, since they provide the key quasi-atomic details necessary to understand how the individual components in a complex or pathway are assembled and coordinated to function as a molecular unit. Here, we use protein docking experiments, in a high-throughput manner, to predict the 3D structure of over 3,000 interactions in yeast, which will be used to complement the complex structures obtained within the 3D-Repertoire pan-European initiative (http://www.3drepertoire.org).

Motivation: Prediction of protein–protein complexes from the coordinates of their unbound components usually starts by generating many potential predictions from a rigid-body 6D search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new method to effectively address the complexity and sampling requirements of the initial exhaustive search. In this approach we combine the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations to accelerate the search. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. The interaction-energy minima are identified by a novel, fast and exhaustive rotational docking search combined with a simple translational scanning. Results obtained on standard protein–protein benchmarks demonstrate its general applicability and robustness. The accuracy is comparable to that of existing state-of-the-art initial exhaustive rigid-body docking tools, but achieving superior efficiency. Moreover, a parallel version of the method performs the docking search in just a few minutes, opening new application opportunities in the current ‘omics’ world. Availability: http://sbg.cib.csic.es/Software/FRODOCK/ Contact: Pablo@cib.csic.es Supplementary information: Supplementary data are available at Bioinformatics online.

Background The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex. Results We have applied here normalized interface propensity (NIP) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex. Conclusion The NIP values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.

The two previous CAPRI experiments showed the success of our rigid-body and refinement approach. For this third edition of CAPRI, we have used a new faster protocol called pyDock, which uses electrostatics and desolvation energy to score docking poses generated with FFT-based algorithms. In target T24 (unbound/model), our best prediction had the highest value of fraction of native contacts (40%) among all participants, although it was not considered as acceptable by the CAPRI criteria. In target T25 (unbound/bound), we submitted a model with medium quality. In target T26 (unbound/unbound), we did not submit any acceptable model (but we would have submitted acceptable predictions if we had included available mutational information about the binding site). For targets T27 (unbound/unbound) and T28 (homo-dimer using model), nobody (including us) submitted any acceptable model. Intriguingly, the crystal structure of target T27 shows an alternative interface that correlates with available biological data (we would have submitted acceptable predictions if we had included this). We also participated in all targets of the SCORERS experiment, with at least acceptable accuracy in all valid cases. We submitted two medium and four acceptable scoring models of T25. Using additional distance restraints (from mutational data), we had two medium and two acceptable scoring models of T26. For target T27, we submitted two acceptable scoring models of the alternative interface in the crystal structure. In summary, CAPRI showed the excellent capabilities of pyDock in identifying near-native docking poses.

The discovery that the functions of most eukaryotic gene products are mediated through multi-protein complexes makes the prediction of protein interactions one of the most important current challenges in structural biology. Rigid-body docking methods can generate a large number of alternative candidates, but it is difficult to discriminate the near-native interactions from the large number of false positives. Many different scoring functions have been developed for this purpose, but in most cases, experimental and biological information is still required for accurate predictions. We explore here the use of evolutionary restraints in evaluating rigid-body docking geometries. In order to identify potential interface residues we identify functional residues based on the comparison of observed amino acid substitutions with those predicted from local environment. The interface residues identified by this method are correctly located in 85% of the cases. These predicted interface residues are used to define distance restraints that help to score rigid-body docking solutions. We have developed the pyDockRST software, which uses the percentage of satisfied distance restraints, together with the electrostatics and desolvation binding energy, to identify correct docking orientations. This methodology dramatically improves the docking results when compared to the use of energy criteria alone, and is able to find the correct orientation within the top 20 docking solutions in 80% of the cases.

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

Computational prediction of protein functional sites can be a critical first step for analysis of large or complex proteins. Contemporary methods often require several homologous sequences and/or a known protein structure, but these resources are not available for many proteins. Leucine-rich repeats (LRRs) are ligand interaction domains found in numerous proteins across all taxonomic kingdoms, including immune system receptors in plants and animals. We devised Repeat Conservation Mapping (RCM), a computational method that predicts functional sites of LRR domains. RCM utilizes two or more homologous sequences and a generic representation of the LRR structure to identify conserved or diversified patches of amino acids on the predicted surface of the LRR. RCM was validated using solved LRR+ligand structures from multiple taxa, identifying ligand interaction sites. RCM was then used for de novo dissection of two plant microbe-associated molecular pattern (MAMP) receptors, EF-TU RECEPTOR (EFR) and FLAGELLIN-SENSING 2 (FLS2). In vivo testing of Arabidopsis thaliana EFR and FLS2 receptors mutagenized at sites identified by RCM demonstrated previously unknown functional sites. The RCM predictions for EFR, FLS2 and a third plant LRR protein, PGIP, compared favorably to predictions from ODA (optimal docking area), Consurf, and PAML (positive selection) analyses, but RCM also made valid functional site predictions not available from these other bioinformatic approaches. RCM analyses can be conducted with any LRR-containing proteins at www.plantpath.wisc.edu/RCM, and the approach should be modifiable for use with other types of repeat protein domains.

Proteins perform many useful molecular tasks, and their biotechnological use continues to increase. As protein activity requires a stable native conformation, protein stabilisation is a major scientific and practical issue. Towards that end, many successful protein stabilisation strategies have been devised in recent years. In most cases, model proteins with a two-state folding equilibrium have been used to study and demonstrate protein stabilisation. Many proteins, however, display more complex folding equilibria where stable intermediates accumulate. Stabilising these proteins requires specifically stabilising the native state relative to the intermediates, as these are expected to lack activity. Here we discuss how to investigate the ‘relevant’ stability of proteins with equilibrium intermediates and propose a way to dissect the contribution of side chain interactions to the overall stability into the ‘relevant’ and ‘nonrelevant’ terms. Examples of this analysis performed on apoflavodoxin and in a single-chain mini antibody are presented.

To understand cellular processes at the molecular level we need to improve our knowledge of protein−protein interactions, from a structural, mechanistic, and energetic point of view. Current theoretical studies and computational docking simulations show that protein dynamics plays a key role in protein association and support the need for including protein flexibility in modeling protein interactions. Assuming the conformational selection binding mechanism, in which the unbound state can sample bound conformers, one possible strategy to include flexibility in docking predictions would be the use of conformational ensembles originated from unbound protein structures. Here we present an exhaustive computational study about the use of precomputed unbound ensembles in the context of protein docking, performed on a set of 124 cases of the Protein−Protein Docking Benchmark 3.0. Conformational ensembles were generated by conformational optimization and refinement with MODELLER and by short molecular dynamics trajectories with AMBER. We identified those conformers providing optimal binding and investigated the role of protein conformational heterogeneity in protein−protein recognition. Our results show that a restricted conformational refinement can generate conformers with better binding properties and improve docking encounters in medium-flexible cases. For more flexible cases, a more extended conformational sampling based on Normal Mode Analysis was proven helpful. We found that successful conformers provide better energetic complementarity to the docking partners, which is compatible with recent views of binding association. In addition to the mechanistic considerations, these findings could be exploited for practical docking predictions of improved efficiency.

Deciphering the structural and energetic determinants of protein-RNA interactions harbors the potential to understand key cell processes at molecular level, such as gene expression and regulation. With this purpose, computational methods like docking aim to complement current biophysical and structural biology efforts. However, the few reported docking algorithms for protein-RNA interactions show limited predictive success rates, mainly due to incomplete sampling of the conformational space of both the protein and the RNA molecules, as well as to the difficulties of the scoring function in identifying the correct docking models. Here, we have tested the predictive value of a variety of knowledge-based and energetic scoring functions on a recently published protein-RNA docking benchmark and developed a scoring function able to efficiently discriminate docking decoys. We first performed docking calculations with the bound conformation, which allowed us to analyze the problem in optimal conditions. We found that geometry-based terms and electrostatics were the most important scoring terms, while binding propensities and desolvation were much less relevant for the scoring of protein-RNA models. This is in contrast with what we observed for protein-protein docking. The results also showed an interesting dependence of the predictive rates on the flexibility of the protein molecule, which arises from the observed higher positive charge of flexible interfaces and provides hints for future development of more efficient protein-RNA docking methods. This work is supported by grant BIO2013-48213-R from Plan Nacional I+D+i (Spanish Ministry of Economy and Competitiveness). LP-C was recipient of an FPU fellowship from the Spanish Ministry of Science.

Background Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces. Results Here we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved. Conclusions The network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.