National Institute of Bioinformatics INB

Sant Jordi Desvalls, Spain

National Institute of Bioinformatics INB

Sant Jordi Desvalls, Spain
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Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | Jimenez-Gonzalez D.,Barcelona Supercomputing Center | Jimenez-Gonzalez D.,Polytechnic University of Catalonia | And 6 more authors.
Bioinformatics | Year: 2012

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. © The Author 2012. Published by Oxford University Press. All rights reserved.


Wass M.N.,Spanish National Cancer Research Center | Wass M.N.,Imperial College London | Fuentes G.,Spanish National Cancer Research Center | Fuentes G.,Bioinformatics Institute | And 4 more authors.
Molecular Systems Biology | Year: 2011

Deciphering the whole network of protein interactions for a given proteome (interactome) is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well-established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non-redundant potential interactors. We additionally show that true interactions can be distinguished from non-likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed funnel-energy model; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non-binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks. © 2011 EMBO and Macmillan Publishers Limited.


Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | Fenwick R.B.,Barcelona Institute for Research in Biomedicine | Esteban-Martin S.,Barcelona Supercomputing Center | And 4 more authors.
Journal of Chemical Theory and Computation | Year: 2013

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. © 2013 American Chemical Society.


Perez-Cano L.,Barcelona Supercomputing Center | Solernou A.,Barcelona Supercomputing Center | Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | Fernandez-Recio J.,Barcelona Supercomputing Center
Pacific Symposium on Biocomputing 2010, PSB 2010 | Year: 2010

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. © 2010 World Scientific Publishing Co. Pte. Ltd.


Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | Grosdidier S.,Barcelona Supercomputing Center | Solernou A.,Barcelona Supercomputing Center | And 2 more authors.
Proteins: Structure, Function and Bioinformatics | Year: 2010

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 domaindomain 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. © 2009 Wiley-Liss, Inc.


Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | D'Abramo M.,Barcelona Institute for Research in Biomedicine | D'Abramo M.,University of Barcelona | And 6 more authors.
Journal of Molecular Biology | Year: 2010

X-ray crystallography and NMR can provide detailed structural information of protein-protein complexes, but technical problems make their application challenging in the high-throughput regime. Other methods such as small-angle X-ray scattering (SAXS) are more promising for large-scale application, but at the cost of lower resolution, which is a problem that can be solved by complementing SAXS data with theoretical simulations. Here, we propose a novel strategy that combines SAXS data and accurate protein-protein docking simulations. The approach has been benchmarked on a large pool of known structures with synthetic SAXS data, and on three experimental examples. The combined approach (pyDockSAXS) provided a significantly better success rate (43% for the top 10 predictions) than either of the two methods alone. Further analysis of the influence of different docking parameters made it possible to increase the success rates for specific cases, and to define guidelines for improving the data-driven protein-protein docking protocols. © 2010 Elsevier Ltd.


Jimenez-Garcia B.,Barcelona Supercomputing Center | Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | Fernandez-Recio J.,Barcelona Supercomputing Center
Bioinformatics | Year: 2013

Summary: pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy. © The Author 2013.


Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | Glaser F.,The Interdisciplinary Center | Fernandez-Recio J.,Barcelona Supercomputing Center
BMC Bioinformatics | Year: 2011

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. © 2011 Pons et al; licensee BioMed Central Ltd.


Pallara C.,Barcelona Supercomputing Center | Jimenez-Garcia B.,Barcelona Supercomputing Center | Perez-Cano L.,Barcelona Supercomputing Center | Romero-Durana M.,Barcelona Supercomputing Center | And 6 more authors.
Proteins: Structure, Function and Bioinformatics | Year: 2013

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.


Pons C.,Barcelona Supercomputing Center | Pons C.,National Institute of Bioinformatics INB | Solernou A.,Barcelona Supercomputing Center | Perez-Cano L.,Barcelona Supercomputing Center | And 2 more authors.
Proteins: Structure, Function and Bioinformatics | Year: 2010

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. © 2010 Wiley-Liss, Inc.

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