Search

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]: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering., [Results]: We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations, which abolish detectable binding., This work has been supported by the European Molecular Biology Laboratory [I.H.M.]; Biotechnology and Biological Sciences Research Council [Future Leader Fellowship BB/N011600/1 to I.H.M.]; Spanish Ministry of Economy and Competitiveness (MINECO) [BIO2016-79930-R to J.F.R.]; Interreg POCTEFA [EFA086/15 to J.F.R.]; European Commission [H2020 grant 676566 (MuG)].

[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.

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.

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.

Motivation: Protein-protein interactions are key to understand biological processes at the molecular level. As a complement to experimental characterization of protein interactions, computational docking methods have become useful tools for the structural and energetics modeling of protein-protein complexes. A key aspect of such algorithms is the use of scoring functions to evaluate the generated docking poses and try to identify the best models. When the scoring functions are based on energetic considerations, they can help not only to provide a reliable structural model for the complex, but also to describe energetic aspects of the interaction. This is the case of the scoring function used in pyDock, a combination of electrostatics, desolvation and van der Waals energy terms. Its correlation with experimental binding affinity values of protein-protein complexes was explored in the past, but the per-residue decomposition of the docking energy was never systematically analyzed. Results: Here, we present pyDockEneRes (pyDock Energy per-Residue), a web server that provides pyDock docking energy partitioned at the residue level, giving a much more detailed description of the docking energy landscape. Additionally, pyDockEneRes computes the contribution to the docking energy of the side-chain atoms. This fast approach can be applied to characterize a complex structure in order to identify energetically relevant residues (hot-spots) and estimate binding affinity changes upon mutation to alanine., This work was supported by funding from the Spanish ‘Programa Estatal I+D+i’ (BIO2016-79930-R); and the EU European Regional Development Fund Program Interreg V-A Spain-France-Andorra (POCTEFA) (PIREPRED).

The 6th CAPRI edition included new modelling challenges, such as the prediction of protein-peptide complexes, and the modelling 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 modelling 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 server 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 modelling 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. This article is protected by copyright. All rights reserved.

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

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.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 A, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

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.

Motivation: In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results: Atomic modeling of protein–protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability and Implementation: IRaPPA has been implemented in the SwarmDock server (http://bmm.crick.ac.uk/∼SwarmDock/), pyDock server (http://life.bsc.es/pid/pydockrescoring/) and ZDOCK server (http://zdock.umassmed.edu/), with code available on request. This work was supported by the European Molecular Biology Laboratory [I.H.M.]; the European Commission [Marie Curie Actions PIEF-GA-2012-327899 to I.H.M.]; the Biotechnology and Biological Sciences Research Council [Future Leader Fellowship BB/N011600/1 to I.H.M.]; Consejo Nacional de Ciencia y Tecnología [217686 to D.B.]; The Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001003), the UK Medical Research Council (FC001003) and the Wellcome Trust (FC001003) [M.T., P.A.B.]; Ministerio de Economía y Competitividad [FPI fellowship to B.J.G., IþDþI Research Project BIO2013-48213-R to J.F.R.]; and National Institutes of Health [R01 GM116960 to ZW].

Metallocarboxypeptidase D (CPD) is a membrane-bound component of the trans-Golgi network that cycles to the cell surface through exocytic and endocytic pathways. Unlike other members of the metallocarboxypeptidase family, CPD is a multicatalytic enzyme with three carboxypeptidase-like domains, although only the first two domains are predicted to be enzymatically active. To investigate the enzymatic properties of each domain in human CPD, a critical active site Glu in domain I and/or II was mutated to Gln and the protein expressed, purified, and assayed with a wide variety of peptide substrates. CPD with all three domains intact displays >50% activity from pH 5.0 to 7.5 with a maximum at pH 6.5, as does CPD with mutation of domain I. In contrast, the domain II mutant displayed >50% activity from pH 6.5–7.5. CPD with mutations in both domains I and II was completely inactive towards all substrates and at all pH values. A quantitative peptidomics approach was used to compare the activities of CPD domains I and II towards a large number of peptides. CPD cleaved C-terminal Lys or Arg from a subset of the peptides. Most of the identified substrates of domain I contained C-terminal Arg, whereas comparable numbers of Lys- and Arg-containing peptides were substrates of domain II. We also report that some peptides with C-terminal basic residues were not cleaved by either domain I or II, showing the importance of the P1 position for CPD activity. Finally, the preference of domain I for C-terminal Arg was validated through molecular docking experiments. Together with the differences in pH optima, the different substrate specificities of CPD domains I and II allow the enzyme to perform distinct functions in the various locations within the cell. This work was funded by the Spanish Ministry of Innovation and Competitiveness grants BIO2013-44973-R and BIO2016-78057-R (to FXA), by Plan Estatal grant number BIO2016-79960-R from the Spanish Ministry of Economy and Competitiveness (to JFR), and by grant R01-DA004494 from the United States’ National Institute of Health (to LDF).

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.”

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.

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.

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 aminoacid 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. Proteins 2010. © 2009 Wiley-Liss, Inc.

The association of two biological macromolecules is a fundamental biological phenomenon and an unsolved theoretical problem. Docking methods for ab initio prediction of association of two independently determined protein structures usually fail when they are applied to a large set of complexes, mostly because of inaccuracies in the scoring function and/or difficulties on simulating the rearrangement of the interface residues on binding. In this work we present an efficient pseudo-Brownian rigid-body docking procedure followed by Biased Probability Monte Carlo Minimization of the ligand interacting side-chains. The use of a soft interaction energy function precalculated on a grid, instead of the explicit energy, drastically increased the speed of the procedure. The method was tested on a benchmark of 24 protein–protein complexes in which the three-dimensional structures of their subunits (bound and free) were available. The rank of the near-native conformation in a list of candidate docking solutions was

The accurate scoring of rigid-body docking orientations represents one of the major difficulties in protein-protein docking prediction. Other challenges are the development of faster and more efficient sampling methods and the introduction of receptor and ligand flexibility during simulations. Overall, good discrimination of near-native docking poses from the very early stages of rigid-body protein docking is essential step before applying more costly interface refinement to the correct docking solutions. Here we explore a simple approach to scoring of rigid-body docking poses, which has been implemented in a program called pyDock. The scheme is based on Coulombic electrostatics with distance dependent dielectric constant, and implicit desolvation energy with atomic solvation parameters previously adjusted for rigid-body protein-protein docking. This scoring function is not highly dependent on specific geometry of the docking poses and therefore can be used in rigid-body docking sets generated by a variety of methods. We have tested the procedure in a large benchmark set of 80 unbound docking cases. The method is able to detect a near-native solution from 12,000 docking poses and place it within the 100 lowest-energy docking solutions in 56% of the cases, in a completely unrestricted manner and without any other additional information. More specifically, a near-native solution will lie within the top 20 solutions in 37% of the cases. The simplicity of the approach allows for a better understanding of the physical principles behind protein-protein association, and provides a fast tool for the evaluation of large sets of rigid-body docking poses in search of the near-native orientation.

BaR domains are found in proteins that bind and remodel membranes and participate in cytoskeletal and nuclear processes. Here, we report the crystal structure of the BAR domain from the human Bin1 protein at 2.0 Å resolution. Both the quaternary and tertiary architectures of the homodimeric Bin1 BAR domain are built upon “knobs-into-holes” packing of side chains, like those found in conventional left-handed coiled-coils, and this packing governs the curvature of a putative membrane-engaging concave face. Our calculations indicate that the Bin1 BAR domain contains two potential sites for protein-protein interactions on the convex face of the dimer. Comparative analysis of structural features reveals that at least three architectural subtypes of the BAR domain are encoded in the human genome, represented by the Arfaptin, Bin1/ Amphiphysin, and IRSp53 BAR domains. We discuss how these principal groups may differ in their potential to form regulatory heterotypic interactions.

Botrytis cinerea is a phytopathogenic fungus that causes gray mold in >1,000 plant species. During infection, it secretes several endopolygalacturonases (PGs) to degrade cell wall pectin, and among them, BcPG1 is constitutively expressed and is an important virulence factor. To counteract the action of PGs, plants express polygalacturonase-inhibiting proteins (PGIPs) that have been shown to inhibit a variety of PGs with different inhibition kinetics, both competitive and noncompetitive. The PG-PGIP interaction promotes the accumulation of oligogalacturonides, fragments of the plant cell wall that are general elicitors of plant defense responses. Here, we characterize the enzymatic activity of BcPG1 and investigate its interaction with PGIP isoform 2 from Phaseolus vulgaris (PvPGIP2) by means of inhibition assays, homology modeling, and molecular docking simulations. Our results indicate a mixed mode of inhibition. This is compatible with a model for the interaction where PvPGIP2 binds the N-terminal portion of BcPG1, partially covering its active site and decreasing the enzyme affinity for the substrate. The structural framework provided by the docking model is confirmed by site-directed mutagenesis of the residues that distinguish PvPGIP2 from the isoform PvPGIP1. The finding that PvPGIP2 inhibits BcPG1 with a mixed-type kinetics further indicates the versatility of PGIPs to evolve different recognition specificities.

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.

Flavodoxins are classified in two groups according to the presence or absence of a approximately 20-residue loop of unknown function. In the accompanying paper (36), we have shown that the differentiating loop from the long-chain Anabaena PCC 7119 flavodoxin is a peripheral structural element that can be removed without preventing the proper folding of the apoprotein. Here we investigate the role played by the loop in the stability and folding mechanism of flavodoxin by comparing the equilibrium and kinetic behavior of the full-length protein with that of loop-lacking, shortened variants. We show that, when the loop is removed, the three-state equilibrium thermal unfolding of apoflavodoxin becomes two-state. Thus, the loop is responsible for the complexity shown by long-chain apoflavodoxins toward thermal denaturation. As for the folding reaction, both shortened and wild type apoflavodoxins display three-state behavior but their folding mechanisms clearly differ. Whereas the full-length protein populates an essentially off-pathway transient intermediate, the additional state observed in the folding of the shortened variant analyzed seems to be simply an alternative native conformation. This finding suggests that the long loop may also be responsible for the accumulation of the kinetic intermediate observed in the full-length protein. Most revealing, however, is that the influence of the loop on the overall conformational stability of apoflavodoxin is quite low and the natively folded shortened variant Delta(120-139) is almost as stable as the wild type protein. The fact that the loop, which is not required for a proper folding of the polypeptide, does not even play a significant role in increasing the conformational stability of the protein supports our proposal (36) that the differentiating loop of long-chain flavodoxins may be related to a recognition function, rather than serving a structural purpose.

The ICM-DISCO (Docking and Interface Side-Chain Optimization) protein-protein-docking method is a direct stochastic global energy optimization from multiple starting positions of the ligand. The first step is performed by docking of a rigid all-atom ligand molecule to a set of soft receptor potentials precalculated on a 0.5 A grid from realistic solvent-corrected force-field energies. This step finds the correct solution as the lowest energy conformation in almost 100% of the cases in which interfaces do not change on binding. The second step is needed to deal with the induced changes and includes the global optimization of the interface side-chains of up to 400 best solutions. The CAPRI predictions were performed fully automatically with this method. Available experimental information was included as a filtering step to favor expected docking surfaces. In three of the seven proposed targets, the ICM-DISCO method found a good solution (>50% of correct contacts) within the five submitted models. The procedure is global and fully automated. We demonstrate that the algorithm handles the induced changes of surface side-chains but is less successful if the backbone undergoes large-scale rearrangements.

The folding reaction of Anabaena apoflavodoxin has been studied by stopped-flow kinetics and site-directed mutagenesis. Although the urea unfolding equilibrium is two-state, a transient intermediate accumulates during the folding reaction. The intermediate is monomeric, and it is not related to proline isomerization. Unlike many cases where the presence of an intermediate has been detected either by a burst phase or by the curvature, at low urea concentration, of the otherwise only observable kinetic phase, two kinetic phases are observed in apoflavodoxin folding whose total amplitude equals the amplitude of unfolding. To determine the role of the intermediate in the folding reaction, the apoflavodoxin kinetic data have been fitted to all conceivable three-species kinetic models (either linear or triangular). Using a stepwise fitting procedure, we find that the off-pathway mechanism explains most of the kinetic data (not a slow unfolding phase), the on-pathway mechanism being rejected. By using global analysis, good overall agreement between data and fit is found when a triangular mechanism is considered. The fitted values of the microscopic constants indicate that most of the unfolded molecules refold from the denatured state. Apoflavodoxin thus folds via a triangular, but essentially off-pathway, mechanism. We calculate that the retardation of the folding caused by the off-pathway intermediate is not large. Some unusual properties of the intermediate are discussed.

This paper presents a strategy to tune the orientation of immobilized proteins on electrodes of general applicability to different types of proteins. We orient ferredoxin:NADP+ reductase molecules onto a modified gold electrode by introducing a genetically engineered metal binding site on a selected region of the protein surface and covering the gold surface with a self-assembled monolayer of thiols appended with nitrilotriacetic acid groups complexed with metal transition ions. Two mutants were designed to have a histidine pair (His-X3-His) on surface-exposed α-helices located in one of the two protein domains. It was first demonstrated that the mutant proteins in solution retain their full activity and that the kinetic constants of the redox catalytic steps are not affected by the mutations. The enzyme-modified gold electrodes were then analyzed for the amount and distribution of protein on their surface and for their activity using atomic force microscopy and cyclic voltammetry. The two electrode-bound...

The enzyme ferredoxin-NADP+ reductase (FNR) can be used in amperometric enzyme electrodes for NADP+ detection. In order to evaluate the performance of such electrodes, a critical step is the comparison of the catalytic rate constants of immobilized FNR enzyme to the FNR in solution. In this work, electrochemical techniques such as cyclic voltammetry have been applied to investigate the catalysis of NADPH oxidation by ferredoxin-NADP+ reductase using one-electron redox mediators such as ferrocenemethanol. The activity of the enzyme in solution mediated by ferrocenemethanol is comparable to other one-electron redox mediators previously measured.

Heteromeric amino acid transporters (HATs) are the unique example, known in all kingdoms of life, of solute transporters composed of two subunits linked by a conserved disulfide bridge. In metazoans, the heavy subunit is responsible for the trafficking of the heterodimer to the plasma membrane, and the light subunit is the transporter. HATs are involved in human pathologies such as amino acidurias, tumor growth and invasion, viral infection and cocaine addiction. However structural information about interactions between the heavy and light subunits of HATs is scarce. In this work, transmission electron microscopy and single-particle analysis of purified human 4F2hc/L-type amino acid transporter 2 (LAT2) heterodimers overexpressed in the yeast Pichia pastoris, together with docking analysis and crosslinking experiments, reveal that the extracellular domain of 4F2hc interacts with LAT2, almost completely covering the extracellular face of the transporter. 4F2hc increases the stability of the light subunit LAT2 in detergent-solubilized Pichia membranes, allowing functional reconstitution of the heterodimer into proteoliposomes. Moreover, the extracellular domain of 4F2hc suffices to stabilize solubilized LAT2. The interaction of 4F2hc with LAT2 gives insights into the structural bases for light subunit recognition and the stabilizing role of the ancillary protein in HATs. This work was supported by Spanish Ministry of Science and Innovation Grants BIO2010-22324 (to J.F.-R.) and SAF2012- 40080-C02-01, European Commission Frame Program 7 Grant 201924 (European Drug Initative on Channels and Transporters), Fundación Ramón Areces, and the Generalitat de Catalunya Grant SGR2009-1355 (to M.P.); by University of Bern, Swiss National Science Foundation Grants 31003A_125150 and 31003A_144168; the Bern University Research Foundation; the Novartis Foundation; the Marie Curie Actions International Fellowship Program; and National Center of Competence in Research TransCure (D.F.).

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.

Summary: Protein–protein interactions are central to almost all biological functions, and the atomic details of such interactions can yield insights into the mechanisms that underlie these functions. We present a web server that wraps and extends the SwarmDock flexible protein–protein docking algorithm. After uploading PDB files of the binding partners, the server generates low energy conformations and returns a ranked list of clustered docking poses and their corresponding structures. The user can perform full global docking, or focus on particular residues that are implicated in binding. The server is validated in the CAPRI blind docking experiment, against the most current docking benchmark, and against the ClusPro docking server, the highest performing server currently available. Availability: The server is freely available and can be accessed at: http://bmm.cancerresearchuk.org/%7ESwarmDock/. Contact: Paul.Bates@cancer.org.uk Supplementary information: Supplementary data are available at Bioinformatics online.

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.

Motivation: Empirical models for the prediction of how changes in sequence alter protein–protein binding kinetics and thermodynamics can garner insights into many aspects of molecular biology. However, such models require empirical training data and proper validation before they can be widely applied. Previous databases contained few stabilizing mutations and no discussion of their inherent biases or how this impacts model construction or validation. Results: We present SKEMPI, a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein–protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Changes in 713 association and dissociation rates and 127 enthalpies and entropies were also recorded. The existence of biases towards specific mutations, residues, interfaces, proteins and protein families is discussed in the context of how the data can be used to construct predictive models. Finally, a cross-validation scheme is presented which is capable of estimating the efficacy of derived models on future data in which these biases are not present. Availability: The database is available online at http://life.bsc.es/pid/mutation_database/ Contact: juanf@bsc.es

Summary: The application of docking to large-scale experiments or the explicit treatment of protein flexibility are part of the new challenges in structural bioinformatics that will require large computer resources and more efficient algorithms. Highly optimized fast Fourier transform (FFT) approaches are broadly used in docking programs but their optimal code implementation leaves hardware acceleration as the only option to significantly reduce the computational cost of these tools. In this work we present Cell-Dock, an FFT-based docking algorithm adapted to the Cell BE processor. We show that Cell-Dock runs faster than FTDock with maximum speedups of above 200×, while achieving results of similar quality. Availability and implementation: The source code is released under GNU General Public License version 2 and can be downloaded from http://mmb.pcb.ub.es/~cpons/Cell-Dock. Contact: djimenez@ac.upc.edu or juanf@bsc.es Supplementary Information: Supplementary data are available at Bioinformatics online.

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.

Alpha helices are useful scaffolds to build biologically active peptides. The intrinsic stability of an alpha-helix is a key feature that can be successfully designed, and it is governed by the constituting amino acid residues. Their individual contributions to helix stability are given, according to Lifson–Roig theory, by their w parameters, which are known for all proteinogenic amino acids, but not for non-natural ones. On the other hand, non-natural, conformationally-restricted amino acids can be used to impart biochemical stability to peptides intended for in vivo administration. Efficient design of peptides based on these amino acids requires the previous determination of their w parameters. We begin here this task by determining the w parameters of two restricted analogs of alanine: (α-methyl)alanine and 1-aminocyclopropanecarboxylic acid. According to their w values (α-methyl)alanine is almost as good a helix forming residue as alanine, while 1-aminocyclopropanecarboxylic acid is, similarly to proline, a helix breaker., We acknowledge financial support from grants: BFU2007- 61476/BMC and CTQ2007-62245 (Spain); PM048/2004 and PI078/08 (Gobierno de Aragón, Spain).

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.

Signal transducer and activator of transcription (STAT) proteins play a crucial role in the activation of gene transcription in response to extracellular stimuli. The regulation and activity of these proteins require a complex rearrangement of the domains. According to the established models, based on crystallographic data, STATs convert from a basal antiparallel inactive dimer into a parallel active one following phosphorylation. The simultaneous analysis of small-angle X-ray scattering data measured at different concentrations of unphosphorylated human STAT5a core domain unambiguously identifies the simultaneous presence of a monomer and a dimer. The dimer is the minor species but could be structurally characterized by SAXS in the presence of the monomer using appropriate computational tools and shown to correspond to the antiparallel assembly. The equilibrium is governed by a moderate dissociation constant of K(d) approximately 90 microM. Integration of these results with previous knowledge of the N-terminal domain structure and dissociation constants allows the modeling of the full-length protein. A complex network of intermolecular interactions of low or medium affinity is suggested. These contacts can be eventually formed or broken to trigger the dramatic modifications in the dimeric arrangement needed for STAT regulation and activity.

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 interactions between redox proteins are transient in nature. Therefore, very few crystal structures are available for the complexes formed between these proteins. Computational docking simulations thus provide a useful alternative method for studying the interactions between electron transfer proteins. In this paper, we have studied the interactions between the aa(3)-type cytochrome c oxidase of the cyanobacterium Phormidium laminosum and its redox partners plastocyanin and cytochrome c(6) using a combination of comparative modelling techniques and docking simulations. Rigid-body docking orientations were scored with a combined energy function that accounts for electrostatics and desolvation. These simulations have identified two plausible docking sites, one of which appears to be unique to the binding of plastocyanin to the oxidase. This unique binding site may be due to the presence of a long loop region in the subunit II of cyanobacterial oxidases. Control simulations were performed with the ba(3)-type cytochrome c oxidase and its redox partner cytochrome c(552) from Thermus thermophilus. The docking between cytochrome c oxidase and its redox partners plastocyanin and cytochrome c(6) is dominated by hydrophobic residues, a feature already observed from kinetic and structural studies in other complexes of P. laminosum (e.g. plastocyanin or cytochrome c(6) with cytochrome f and photosystem I).

Ferredoxin (Fd) interacts with ferredoxin-NADP+ reductase (FNR) to transfer two electrons to the latter, one by one, which will finally be used to reduce NADP+ to NADPH. The formation of a transient complex between Fd and FNR is required for the electron transfer, and extensive mutational and crystallographic studies have been reported to characterize such protein-protein interaction. However, some aspects of the association mechanism still remain unclear. Moreover, in spite of their structural differences, flavodoxin (Fld) can replace Fd in its function and interact with FNR to transfer electrons with only slightly lower efficiency. Although crystallographic structures for the FNR:Fd association have been reported, experimental structural data for the FNR:Fld interaction are highly elusive. We have modeled here the interactions between FNR and both of its protein partners, Fd and Fld, using surface energy analysis, computational rigid-body docking simulations, and interface side-chain refinement. The results, consistent with previous experimental data, suggest the existence of alternative binding modes in these electron transfer proteins.

Carboxypeptidase A1 has been the subject of extensive research in the last 30 y and is one of the most widely studied zinc metalloenzymes. However, the three-dimensional structure of the human form of the enzyme is not yet available. This report describes the three-dimensional structure of human carboxypeptidase A1 (hCPA1) derived from crystals that belong to the tetragonal space group P4(3)2(1)2 and diffract to 1.6 angstroms resolution. A description of the ternary complex hCPA1-Zn2+-poly(acrylic acid) is included as a model of the interaction of mucoadhesive polymers with proteases in the gastrointestinal tract. The direct mode of interaction between poly(acrylic acid) and the active site of the target protease was confirmed by in vitro inhibition assays. The structure was further analyzed in silico through the optimal docking-area method. The characterization of binding sites on the surface of hCPA1 and a comparison with other available carboxypeptidase structures provided further insights into the formation of multiprotein complexes and the activation mechanisms of carboxypeptidase zymogens. The high-resolution structure of hCPA1 provides an excellent template for the modelling of physiologically relevant carboxypeptidases and could also contribute to the design of specific agents for biomedical purposes.

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 metallocarboxypeptidases (MCPs) belonging to the clan MC were studied by the Optimal Docking Area (ODA) method to evaluate protein-protein binding sites and to provide a basis for the identification of binding partners for this class of enzymes. The ODA method identifies surface patches with optimal desolvation energy based on the selection of low-energy docking regions, generated from a set of surface points around the protein. With few exceptions, the ODA method identified surface patches with a significant low-energy docking surface for all the MCPs with known three-dimensional structure. Overall, in 14 out of 24 cases, the detected ODA patches were correctly located (i.e. more than 50% of the predicted residues were in known protein-protein binding sites), yielding a global success rate of 58%. More specifically, the success rate increased up to 80% on the ODA patches detected for the catalytic domains of the M14A subfamily, independently on the partner. Interestingly, the ODA residues on the catalytic domain were correctly located in the interface with the N-terminal pro domain in all MCPs. The spatial distribution of the ODA patches for the different members of the family is in relation to the origin and function of the particular MCP, which allowed distinguishing between them. In good agreement with the experimentally characterized protein interfaces, the total average surface area of the theoretically derived ODA patches for the catalytic domain of MCPs is around 1700 A2 and their content in hydrophobic residues is about 40%. As a particular case, the average surface area of the ODA patches in MCPs of crop insect pests is about twice that of the MCPs of vertebrates, which might be related to their particular function. We recognized two binding regions for the catalytic domain of the MCPs, one of them accounting for nearly all the known intermolecular interactions made up by the enzymes. Protein inhibitors seem to have evolved to dock on this subset of ODA patches, evoking the binding mode of the N-terminal pro domains. The second binding region detected, for which no ligands have been identified so far, seems to be related to the acquisition/maintenance of the native structure of the peptidase. Overall, the ODA method has been successful in identifying low-energy docking areas in a set of structurally and functionally related proteins, suggesting that it can be easily extended to other families in the search for protein-protein binding sites and for their functional significance.