Publications (14)59.63 Total impact
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Article: pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring.
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ABSTRACT: 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.Availability and implementation: The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock CONTACT: juanf@bsc.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Bioinformatics 05/2013; · 5.47 Impact Factor -
Article: Validated Conformational Ensembles Are Key for the Successful Prediction of Protein Complexes
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ABSTRACT: 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. ■ INTRODUCTION The association of two or more proteins to form macro-molecular complexes is a fundamental mechanism in cell function. 1 The characterization of the atomic details of such interactions is key to understand the underlying nature of the association process 2 and to modulate interactions of biomedical and biotechnological interest. This challenging task constitutes one of the major goals of structural biology, and interdiscipli-nary methodologies need to be implemented to overcome the technical limitations of current experimental techniques. 3,4 In response to this, a variety of protein−protein docking tools have been reported to model the structure of protein complexes starting from the coordinates of the individual components. 5 Although highly successful models can be produced in cases where the interacting proteins behave as rigid bodies, 6 the main challenge for the general applicability of docking predictions is how to accurately model the flexibility of the interacting proteins. 7,8 This problem is related to our lack of detailed understanding of the protein−protein association mechanism where, except for a few rigid-body cases that appear to follow a "lock-and-key" mechanism, 9 most of these interactions involve conformational changes and are therefore best explained by the induced-fit 10 or conformational selection 11−16 mechanisms of molecular recognition. Therefore, over the last 10 years, different state-of-the-art docking algorithms have attempted to improve the description of the association process by including conformational flexibility upon binding.Journal of Chemical Theory and Computation 02/2013; 9(1830):1837. · 5.22 Impact Factor -
Article: Community-wide assessment of protein-interface modeling suggests improvements to design methodology.
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ABSTRACT: The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.Journal of Molecular Biology 11/2011; 414(2):289-302. · 4.00 Impact Factor -
Article: H-bond network optimization in protein-protein complexes: are all-atom force field scores enough?
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ABSTRACT: Structural prediction of protein-protein complexes given the structures of the two interacting compounds in their unbound state is a key problem in biophysics. In addition to the problem of sampling of near-native orientations, one of the modeling main difficulties is to discriminate true from false positives. Here, we present a hierarchical protocol for docking refinement able to discriminate near native poses from a group of docking candidates. The main idea is to combine an efficient sampling of the full system hydrogen bond network and side chains, together with an all-atom force field and a surface generalized born implicit solvent. We tested our method on a set of twenty two complexes containing a near-native solution within the top 100 docking poses, obtaining a near native solution as the top pose in 70% of the cases. We show that all atom force fields optimized H-bond networks do improve significantly state of the art scoring functions.Proteins Structure Function and Bioinformatics 11/2011; 80(3):818-24. · 3.39 Impact Factor -
Article: Prediction of protein-binding areas by small-world residue networks and application to docking.
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ABSTRACT: 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. 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. 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.BMC Bioinformatics 09/2011; 12:378. · 2.75 Impact Factor -
Article: Scoring by intermolecular pairwise propensities of exposed residues (SIPPER): a new efficient potential for protein-protein docking.
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ABSTRACT: A detailed and complete structural knowledge of the interactome is one of the grand challenges in Biology, and a variety of computational docking approaches have been developed to complement experimental efforts and help in the characterization of protein-protein interactions. Among the different docking scoring methods, those based on physicochemical considerations can give the maximum accuracy at the atomic level, but they are usually computationally demanding and necessarily noisy when implemented in rigid-body approaches. Coarser-grained knowledge-based potentials are less sensitive to details of atomic arrangements, thus providing an efficient alternative for scoring of rigid-body docking poses. In this study, we have extracted new statistical potentials from intermolecular pairs of exposed residues in known complex structures, which were then used to score protein-protein docking poses. The new method, called SIPPER (scoring by intermolecular pairwise propensities of exposed residues), combines the value of residue desolvation based on solvent-exposed area with the propensity-based contribution of intermolecular residue pairs. This new scoring function found a near-native orientation within the top 10 predictions in nearly one-third of the cases of a standard docking benchmark and proved to be also useful as a filtering step, drastically reducing the number of docking candidates needed by energy-based methods like pyDock.Journal of Chemical Information and Modeling 01/2011; 51(2):370-7. · 4.68 Impact Factor -
Article: Optimization of pyDock for the new CAPRI challenges: Docking of homology-based models, domain-domain assembly and protein-RNA binding.
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ABSTRACT: 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 Structure Function and Bioinformatics 11/2010; 78(15):3182-8. · 3.39 Impact Factor -
Article: Structural characterization of protein-protein complexes by integrating computational docking with small-angle scattering data.
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ABSTRACT: 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.Journal of Molecular Biology 10/2010; 403(2):217-30. · 4.00 Impact Factor -
Article: Structural prediction of protein-RNA interaction by computational docking with propensity-based statistical potentials.
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ABSTRACT: 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.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 01/2010; -
Article: Secondary bacterial peritonitis in cirrhosis: a retrospective study of clinical and analytical characteristics, diagnosis and management.
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ABSTRACT: Secondary bacterial peritonitis in cirrhotic patients is an uncommon entity that has been little reported. Our aim is to analyse the frequency, clinical characteristics, treatment and prognosis of patients with secondary peritonitis in comparison to those of patients with spontaneous bacterial peritonitis (SBP). Retrospective analysis of 24 cirrhotic patients with secondary peritonitis compared with 106 SBP episodes. Secondary peritonitis represented 4.5% of all peritonitis in cirrhotic patients. Patients with secondary peritonitis showed a significantly more severe local inflammatory response than patients with SBP. Considering diagnosis of secondary peritonitis, the sensitivity of Runyon's criteria was 66.6% and specificity 89.7%, Runyon's criteria and/or polymicrobial ascitic fluid culture were present in 95.6%, and abdominal computed tomography was diagnostic in 85% of patients in whom diagnosis was confirmed by surgery or autopsy. Mortality during hospitalization was higher in patients with secondary peritonitis than in those with SBP (16/24, 66.6% vs. 28/106, 26.4%) (p<0.001). There was a trend to lower mortality in secondary peritonitis patients who underwent surgery (7/13, 53.8%) than in those who received medical treatment only (9/11, 81.8%) (p=0.21). Considering surgically treated patients, the time between diagnostic paracentesis and surgery was shorter in survivors than in non-survivors (3.2+/-2.4 vs. 7.2+/-6.1 days, p=0.31). Secondary peritonitis is an infrequent complication in cirrhotic patients but mortality is high. A low threshold of suspicion on the basis of Runyon's criteria and microbiological data, together with an aggressive approach that includes prompt abdominal computed tomography and early surgical evaluation, could improve prognosis in these patients.Journal of Hepatology 10/2009; 52(1):39-44. · 9.26 Impact Factor -
Article: Pushing structural information into the yeast interactome by high-throughput protein docking experiments.
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ABSTRACT: The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.PLoS Computational Biology 09/2009; 5(8):e1000490. · 5.22 Impact Factor -
Article: Present and future challenges and limitations in protein-protein docking.
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ABSTRACT: 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.Proteins Structure Function and Bioinformatics 09/2009; 78(1):95-108. · 3.39 Impact Factor -
Article: FRODOCK: a new approach for fast rotational protein-protein docking.
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ABSTRACT: 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/Bioinformatics 08/2009; 25(19):2544-51. · 5.47 Impact Factor -
Article: Prediction and scoring of docking poses with pyDock.
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ABSTRACT: 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.Proteins Structure Function and Bioinformatics 01/2008; 69(4):852-8. · 3.39 Impact Factor
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Institutions
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2008–2011
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Barcelona Supercomputing Center
- Department of Life Sciences
Barcelona, Catalonia, Spain
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