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Sant Jordi Desvalls, Spain

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.


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 | 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.


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.

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