31 results on '"Brian Jiménez-García"'
Search Results
2. pyDockDNA: A new web server for energy-based protein-DNA docking and scoring
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Luis Angel Rodríguez-Lumbreras, Brian Jiménez-García, Silvia Giménez-Santamarina, and Juan Fernández-Recio
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structural modeling ,Ab initio docking ,protein-DNA interaction ,scoring function ,docking benchmark ,nucleotide parameters ,Biology (General) ,QH301-705.5 - Abstract
Proteins and nucleic acids are essential biological macromolecules for cell life. Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate (as benchmarked on a standard dataset of protein-DNA complex structures, where DNA is in B-DNA conformation). The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use (https://model3dbio.csic.es/pydockdna).
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- 2022
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3. Integrative modeling of membrane-associated protein assemblies
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Jorge Roel-Touris, Brian Jiménez-García, and Alexandre M. J. J. Bonvin
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Science - Abstract
Most approaches for modeling the membrane protein complexes are not capable of incorporating the topological information provided by the membrane. Here authors present an integrative computational protocol for the modeling of membrane-associated protein assemblies, specifically complexes consisting of a membrane-embedded protein and a soluble partner.
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- 2020
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4. Editorial: Web Tools for Modeling and Analysis of Biomolecular Interactions
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Jessica Andreani, Masahito Ohue, and Brian Jiménez-García
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web tools ,biomolecular interactions ,macromolecular structure ,macromolecular evolution ,protein docking ,protein-ligand complexes ,Biology (General) ,QH301-705.5 - Published
- 2022
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5. Towards design of drugs and delivery systems with the Martini coarse-grained model
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Lisbeth R. Kjølbye, Gilberto P. Pereira, Alessio Bartocci, Martina Pannuzzo, Simone Albani, Alessandro Marchetto, Brian Jiménez-García, Juliette Martin, Giulia Rossetti, Marco Cecchini, Sangwook Wu, Luca Monticelli, and Paulo C. T. Souza
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coarse-grained models ,molecular dynamics ,Martini ,drug design ,drug delivery ,cryptic pockets ,transmembrane proteins ,protein-protein interactions ,soft delivery systems ,PROTACS ,lipid nanoparticles ,Biotechnology ,TP248.13-248.65 ,Biology (General) ,QH301-705.5 - Abstract
Coarse-grained (CG) modelling with the Martini force field has come of age. By combining a variety of bead types and sizes with a new mapping approach, the newest version of the model is able to accurately simulate large biomolecular complexes at millisecond timescales. In this perspective, we discuss possible applications of the Martini 3 model in drug discovery and development pipelines and highlight areas for future development. Owing to its high simulation efficiency and extended chemical space, Martini 3 has great potential in the area of drug design and delivery. However, several aspects of the model should be improved before Martini 3 CG simulations can be routinely employed in academic and industrial settings. These include the development of automatic parameterisation protocols for a variety of molecule types, the improvement of backmapping procedures, the description of protein flexibility and the development of methodologies enabling efficient sampling. We illustrate our view with examples on key areas where Martini could give important contributions such as drugs targeting membrane proteins, cryptic pockets and protein–protein interactions and the development of soft drug delivery systems.
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- 2022
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6. Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem
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Rodrigo V. Honorato, Panagiotis I. Koukos, Brian Jiménez-García, Andrei Tsaregorodtsev, Marco Verlato, Andrea Giachetti, Antonio Rosato, and Alexandre M. J. J. Bonvin
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structural biology ,distributed computing ,web portal ,e-infrastructure ,web services ,Biology (General) ,QH301-705.5 - Abstract
Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is critical for understanding the vast majority of cellular processes, with direct applications in health and food sciences. Since 2010, the WeNMR project (www.wenmr.eu) has implemented numerous web-based services to facilitate the use of advanced computational tools by researchers in the field, using the high throughput computing infrastructure provided by EGI. These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4,000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. The current set of active WeNMR portals are described, together with the complex backend machinery that allows distributed computing resources to be harvested efficiently.
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- 2021
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7. The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions
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Brian Jiménez-García, Jorge Roel-Touris, and Didier Barradas-Bautista
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Genetics - Abstract
Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at: https://server.lightdock.org/
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- 2023
8. Rational Prediction of PROTAC-compatible Protein-Protein Interfaces by Molecular Docking
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Gilberto P. Pereira, Brian Jiménez-García, Riccardo Pellarin, Guillaume Launay, Sangwook Wu, Juliette Martin, and Paulo C. T. Souza
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Proteolysis targeting chimeras (PROTACS) are ligands that can mediate interaction between a protein target and a E3 ligase, forming a ternary complex bound to the ubiquitination machinery, leading to target degradation. This technology has become an exciting new avenue for therapeutic development, with currently two PROTACS in clinical trials targeting cancer. Nonetheless, several PROTAC drug discovery campaigns still rely on serendipity. Here, we describe a general and efficient protocol which combines restraint-based LightDock, energy-based rescoring and minimal solvent-accessible surface distance filtering to produce PROTAC-compatible PPIs. Benchmarking our protocol using a manually curated dataset of 16 ternary complex crystals, accuracies of 94% and 70% were achieved on the redocking and realistic docking experiments starting from unbound protein structures, respectively, evaluated using the CAPRI standards and DockQ. Our protocol is both accurate and computationally efficient, with potential to accelerate PROTAC drug design campaigns, particularly when the ternary complex or PROTAC are unknown.
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- 2023
9. Discriminating physiological from non-physiological interfaces in structures of protein complexes: a community-wide study
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Hugo Schweke, Qifang Xu, Gerardo Tauriello, Lorenzo Pantolini, Torsten Schwede, Frédéric Cazals, Alix Lhéritier, Juan Fernandez-Recio, Luis Ángel Rodríguez-Lumbreras, Ora Schueler-Furman, Julia K. Varga, Brian Jiménez-García, Manon F. Réau, Alexandre Bonvin, Castrense Savojardo, Pier-Luigi Martelli, Rita Casadio, Jérôme Tubiana, Haim Wolfson, Romina Oliva, Didier Barradas-Bautista, Tiziana Ricciardelli, Luigi Cavallo, Česlovas Venclovas, Kliment Olechnovič, Raphael Guerois, Jessica Andreani, Juliette Martin, Xiao Wang, Daisuke Kihara, Anthony Marchand, Bruno Correia, Xiaoqin Zou, Sucharita Dey, Roland Dunbrack, Emmanuel Levy, and Shoshana Wodak
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Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94 respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines were shown to recall the physiological dimers with significantly higher accuracy than the non-physiological set, lending support for the pertinence of our benchmark dataset. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.
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- 2023
10. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment
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Xiaoqin Zou, Théo Mauri, Hang Shi, Shaowen Zhu, Justas Dapkūnas, Yuanfei Sun, Didier Barradas-Bautista, Raphael A. G. Chaleil, Ragul Gowthaman, Sohee Kwon, Xianjin Xu, Zuzana Jandova, Genki Terashi, Ryota Ashizawa, Petras J. Kundrotas, Shuang Zhang, Tunde Aderinwale, Jian Liu, Sandor Vajda, Paul A. Bates, Jianlin Cheng, Daisuke Kihara, Luis A. Rodríguez-Lumbreras, Carlos A. Del Carpio Muñoz, Liming Qiu, Guillaume Brysbaert, Jorge Roel-Touris, Česlovas Venclovas, Tereza Clarence, Rui Yin, Amar Singh, Patryk A. Wesołowski, Rafał Ślusarz, Adam Liwo, Guangbo Yang, Agnieszka S. Karczyńska, Yoshiki Harada, Sergei Kotelnikov, Yuya Hanazono, Charlotte W. van Noort, Marc F. Lensink, Jonghun Won, Adam K. Sieradzan, Israel Desta, Xufeng Lu, Charles Christoffer, Anna Antoniak, Taeyong Park, Sheng-You Huang, Tsukasa Nakamura, Brian G. Pierce, Usman Ghani, Yang Shen, Luigi Cavallo, Chaok Seok, Hao Li, Nurul Nadzirin, Ghazaleh Taherzadeh, Jacob Verburgt, Rodrigo V. Honorato, Artur Giełdoń, Jeffrey J. Gray, Dima Kozakov, Ming Liu, Shan Chang, Eiichiro Ichiishi, Manon Réau, Rui Duan, Francesco Ambrosetti, Johnathan D. Guest, Juan Fernández-Recio, Alexandre M. J. J. Bonvin, Ilya A. Vakser, Farhan Quadir, Yumeng Yan, Ren Kong, Sameer Velankar, Sergei Grudinin, Mateusz Kogut, Mikhail Ignatov, Yasuomi Kiyota, Hyeonuk Woo, Shoshana J. Wodak, Ameya Harmalkar, Shinpei Kobayashi, Panagiotis I. Koukos, Zhen Cao, Kliment Olechnovič, Cezary Czaplewski, Xiao Wang, Agnieszka G. Lipska, Kathryn A. Porter, Peicong Lin, Emilia A. Lubecka, Nasser Hashemi, Bin Liu, Mayuko Takeda-Shitaka, Karolina Zięba, Dzmitry Padhorny, Zhuyezi Sun, Daipayan Sarkar, Romina Oliva, Andrey Alekseenko, Siri Camee van Keulen, Mireia Rosell, Raj S. Roy, Brian Jiménez-García, Jinsol Yang, Martyna Maszota-Zieleniak, Cancer Research UK, Department of Energy and Climate Change (UK), European Commission, Institut National de Recherche en Informatique et en Automatique (France), Medical Research Council (UK), Japan Society for the Promotion of Science, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), National Institute of General Medical Sciences (US), National Institutes of Health (US), National Natural Science Foundation of China, National Science Foundation (US), Unité de Glycobiologie Structurale et Fonctionnelle (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Biomolecular Modelling Laboratory [London], The Francis Crick Institute [London], Jiangsu University of Technology [Changzhou], Department of Electrical Engineering and Computer Science [Columbia] (EECS), University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, Institute for Data Science and Informatics [Columbia], University of Gdańsk (UG), Faculty of Electronics, Telecommunications and Informatics [GUT Gdańsk] (ETI), Gdańsk University of Technology (GUT), Medical University of Gdańsk, Graduate School of Medical Sciences [Nagoya], Nagoya City University [Nagoya, Japan], International University of Health and Welfare Hospital (IUHW Hospital), Department of Chemical and Biomolecular Engineering [Baltimore], Johns Hopkins University (JHU), Bijvoet Center of Biomolecular Research [Utrecht], Utrecht University [Utrecht], Stony Brook University [SUNY] (SBU), State University of New York (SUNY), Innopolis University, Boston University [Boston] (BU), Russian Academy of Sciences [Moscow] (RAS), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Universidad de La Rioja (UR), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Huazhong University of Science and Technology [Wuhan] (HUST), Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Graduate School of Information Sciences [Sendaï], Tohoku University [Sendai], National Institutes for Quantum and Radiological Science and Technology (QST), University of Maryland [Baltimore], King Abdullah University of Science and Technology (KAUST), University of Naples Federico II, Texas A&M University [Galveston], Seoul National University [Seoul] (SNU), Kitasato University, University of Kansas [Lawrence] (KU), Vilnius University [Vilnius], University of Missouri System, VIB-VUB Center for Structural Biology [Bruxelles], VIB [Belgium], Sub NMR Spectroscopy, Sub Overig UiLOTS, Sub Mathematics Education, NMR Spectroscopy, Université de Lille, CNRS, Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576, European Bioinformatics Institute [Hinxton] [EMBL-EBI], Department of Electrical Engineering and Computer Science [Columbia] [EECS], Faculty of Chemistry [Univ Gdańsk], Faculty of Electronics, Telecommunications and Informatics [GUT Gdańsk] [ETI], International University of Health and Welfare Hospital [IUHW Hospital], Johns Hopkins University [JHU], Stony Brook University [SUNY] [SBU], Department of Biomedical Engineering [Boston], Instituto de Ciencias de la Vid y el Vino [ICVV], Huazhong University of Science and Technology [Wuhan] [HUST], Indiana University - Purdue University Indianapolis [IUPUI], National Institutes for Quantum and Radiological Science and Technology [QST], King Abdullah University of Science and Technology [KAUST], Università degli Studi di Napoli 'Parthenope' = University of Naples [PARTHENOPE], Seoul National University [Seoul] [SNU], University of Kansas [Lawrence] [KU], University of Missouri [Columbia] [Mizzou], Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Naples Federico II = Università degli studi di Napoli Federico II, European Project: 675728,H2020,H2020-EINFRA-2015-1,BioExcel(2015), European Project: 823830,H2020-EU.1.4.1.3. Development, deployment and operation of ICT-based e-infrastructures, H2020-EU.1.4. EXCELLENT SCIENCE - Research Infrastructures ,BioExcel-2(2019), European Project: 777536,H2020-EU.1.4.1.3. Development, deployment and operation of ICT-based e-infrastructures, and H2020-EU.1.4. EXCELLENT SCIENCE - Research Infrastructures,EOSC-hub(2018)
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Models, Molecular ,blind prediction ,CAPRI ,CASP ,docking ,oligomeric state ,protein assemblies ,protein complexes ,protein docking ,protein–protein interaction ,template-based modeling ,Computer science ,[SDV]Life Sciences [q-bio] ,Machine learning ,computer.software_genre ,Biochemistry ,Article ,protein-protein interaction ,03 medical and health sciences ,Sequence Analysis, Protein ,Structural Biology ,Server ,Protein Interaction Domains and Motifs ,Molecular Biology ,ComputingMilieux_MISCELLANEOUS ,030304 developmental biology ,0303 health sciences ,Binding Sites ,business.industry ,030302 biochemistry & molecular biology ,Computational Biology ,Proteins ,3. Good health ,Molecular Docking Simulation ,Artificial intelligence ,business ,computer ,Software - Abstract
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
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- 2021
11. Author response for 'Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment'
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Yumeng Yan, Mateusz Kogut, Sohee Kwon, Israel Desta, Petras J. Kundrotas, Xiaoqin Zou, Xiao Wang, Dima Kozakov, Eiichiro Ichiishi, Kathryn A. Porter, Johnathan D. Guest, Brian G. Pierce, Daisuke Kihara, Česlovas Venclovas, Agnieszka G. Lipska, Luigi Cavallo, Panagiotis I. Koukos, Yang Shen, Ren Kong, Brian Jiménez-García, Kliment Olechnovič, Cezary Czaplewski, Peicong Lin, Sameer Velankar, Shoshana J. Wodak, Agnieszka S. Karczyńska, Emilia A. Lubecka, Mikhail Ignatov, Shan Chang, Daipayan Sarkar, Sheng-You Huang, Chaok Seok, Nurul Nadzirin, Hao Li, Anna Antoniak, Manon Réau, Hyeonuk Woo, Siri Camee van Keulen, Ryota Ashizawa, Nasser Hashemi, Adam Liwo, Zhen Cao, Yoshiki Harada, Genki Terashi, Ameya Harmalkar, Farhan Quadir, Shinpei Kobayashi, Sandor Vajda, Zuzana Jandova, Juan Fernández-Recio, Amar Singh, Martyna Maszota-Zieleniak, Rodrigo V. Honorato, Usman Ghani, Sergei Grudinin, Xufeng Lu, Jorge Roel-Touris, Ming Liu, Paul A. Bates, Ghazaleh Taherzadeh, Adam K. Sieradzan, Patryk A. Wesołowski, Théo Mauri, Ilya A. Vakser, Francesco Ambrosetti, Jinsol Yang, Sergei Kotelnikov, Hang Shi, Shuang Zhang, Marc F. Lensink, Justas Dapkūnas, Yasuomi Kiyota, Taeyong Park, Mayuko Takeda-Shitaka, Andrey Alekseenko, Jian Liu, Artur Giełdoń, Ragul Gowthaman, Jonghun Won, Tsukasa Nakamura, Tunde Aderinwale, Yuanfei Sun, Guillaume Brysbaert, Jeffrey J. Gray, Luis A. Rodríguez-Lumbreras, Yuya Hanazono, Charlotte W. van Noort, Carlos A. Del Carpio Muñoz, Rui Duan, Alexandre M. J. J. Bonvin, Jianlin Cheng, Liming Qiu, Tereza Clarence, Rui Yin, Guangbo Yang, Shaowen Zhu, Didier Barradas-Bautista, Rafał Ślusarz, Raphael A. G. Chaleil, Charles Christoffer, Jacob Verburgt, Dzmitry Padhorny, Zhuyezi Sun, Romina Oliva, Mireia Rosell, Raj S. Roy, Bin Liu, and Karolina Zięba
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Frontier ,Computer science ,Econometrics - Published
- 2021
12. Author response for '<scp>PDB‐Tools</scp> Web: A user‐friendly interface for the manipulation of PDB files'
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Amjj Bonvin, Jpglm Rodrigues, Jmc Teixeira, Mikael Trellet, and Brian Jiménez-García
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User Friendly ,Database ,Computer science ,Interface (Java) ,Protein Data Bank (RCSB PDB) ,computer.software_genre ,computer - Published
- 2020
13. PDB-Tools Web: A user-friendly interface for the manipulation of PDB files
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Mikael Trellet, Alexandre M. J. J. Bonvin, João M.C. Teixeira, João P. G. L. M. Rodrigues, Brian Jiménez-García, NMR Spectroscopy, and Sub NMR Spectroscopy
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PDB ,Service (systems architecture) ,User Friendly ,web server ,business.industry ,Computer science ,Interface (computing) ,bioinformatics ,computer.file_format ,File format ,Protein Data Bank ,Biochemistry ,World Wide Web ,Upload ,Software ,Structural Biology ,business ,Molecular Biology ,computer ,Graphical user interface - Abstract
The Protein Data Bank (PDB) file format remains a popular format used and supported by many software to represent coordinates of macromolecular structures. It however suffers from drawbacks such as error-prone manual editing. Because of that, various software toolkits have been developed to facilitate its editing and manipulation, but, to date, there is no online tool available for this purpose. Here we present PDB-Tools Web, a flexible online service for manipulating PDB files. It offers a rich and user-friendly graphical user interface that allows users to mix-and-match more than 40 individual tools from the pdb-tools suite. Those can be combined in a few clicks to perform complex pipelines, which can be saved and uploaded. The resulting processed PDB files can be visualized online and downloaded. The web server is freely available at https://wenmr.science.uu.nl/pdbtools.
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- 2020
14. Structural Characterization of Protein-Protein Interactions with pyDockSAXS
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Brian, Jiménez-García, Pau, Bernadó, and Juan, Fernández-Recio
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Molecular Docking Simulation ,X-Ray Diffraction ,Protein Conformation ,Scattering, Small Angle ,Proteins ,Protein Interaction Domains and Motifs ,Software - Abstract
Structural characterization of protein-protein interactions can provide essential details to understand biological functions at the molecular level and to facilitate their manipulation for biotechnological and biomedical purposes. Unfortunately, the 3D structure is available for only a small fraction of all possible protein-protein interactions, due to the technical limitations of high-resolution structural determination methods. In this context, low-resolution structural techniques, such as small-angle X-ray scattering (SAXS), can be combined with computational docking to provide structural models of protein-protein interactions at large scale. In this chapter, we describe the pyDockSAXS web server ( https://life.bsc.es/pid/pydocksaxs ), which uses pyDock docking and scoring to provide structural models that optimally satisfy the input SAXS data. This server, which is freely available to the scientific community, provides an automatic pipeline to model the structure of a protein-protein complex from SAXS data.
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- 2020
15. Integrative modeling of protein-protein interactions with pyDock for the new docking challenges
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Lucía Díaz, Brian Jiménez-García, Miguel Romero-Durana, Juan Fernández-Recio, Luis A. Rodríguez-Lumbreras, Mireia Rosell, Ministerio de Economía y Competitividad (España), and European Commission
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Protein Conformation, alpha-Helical ,Multimeric assemblies ,Computer science ,Oligosaccharides ,Computational biology ,Ligands ,Biochemistry ,Complex structure ,Protein–protein interaction ,Protein-oligosaccharide complexes ,03 medical and health sciences ,Structural Biology ,Server ,Protein Interaction Mapping ,Humans ,Protein Interaction Domains and Motifs ,Amino Acid Sequence ,pyDock ,Molecular Biology ,030304 developmental biology ,Protein-protein docking ,0303 health sciences ,Binding Sites ,030302 biochemistry & molecular biology ,Proteins ,Molecular Docking Simulation ,Protein-peptide interactions ,Docking (molecular) ,Research Design ,Structural Homology, Protein ,Protein Conformation, beta-Strand ,Protein Multimerization ,Peptides ,CAPRI ,Software ,Protein Binding - Abstract
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.
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- 2020
16. Structural characterization of protein–protein interactions with pyDockSAXS
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Brian Jiménez-García, Juan Fernández-Recio, Pau Bernadó, Barcelona Supercomputing Center, Gáspári, Z., Ministerio de Economía y Competitividad (España), European Commission, Labex EpiGenMed, and Agence Nationale de la Recherche (France)
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Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC] ,FTDock ,0303 health sciences ,Computer science ,fungi ,030302 biochemistry & molecular biology ,Protein-protein interactions ,Protein–protein interactions ,computer.software_genre ,Proteïnes -- Anàlisi -- Informàtica ,Computational docking ,Protein–protein interaction ,03 medical and health sciences ,Protein conformation ,Docking (molecular) ,CRYSOL ,Structural modeling ,Data mining ,pyDock ,computer ,Small-angle X-ray scattering (SAXS) ,030304 developmental biology - Abstract
Structural characterization of protein–protein interactions can provide essential details to understand biological functions at the molecular level and to facilitate their manipulation for biotechnological and biomedical purposes. Unfortunately, the 3D structure is available for only a small fraction of all possible protein–protein interactions, due to the technical limitations of high-resolution structural determination methods. In this context, low-resolution structural techniques, such as small-angle X-ray scattering (SAXS), can be combined with computational docking to provide structural models of protein–protein interactions at large scale. In this chapter, we describe the pyDockSAXS web server (https://life.bsc.es/pid/pydocksaxs), which uses pyDock docking and scoring to provide structural models that optimally satisfy the input SAXS data. This server, which is freely available to the scientific community, provides an automatic pipeline to model the structure of a protein–protein complex from SAXS data., This work was supported by the Spanish Ministry of Science (grant BIO2016-79930-R), the European Union H2020 programme (grant MuG 676566), and the Labex EpiGenMed, an “Investissements d’avenir” program (ANR-10-LABX-12-01). The CBS is a member of France-BioImaging (FBI) and the French Infrastructure for Integrated Structural Biology (FRISBI), two national infrastructures supported by the French National Research Agency (ANR-10-INSB-04-01 and ANR-10-INSB-05, respectively).
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- 2020
17. SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation
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Brian Jiménez-García, Justas Dapkunas, Iain H. Moal, Juan Fernández-Recio, Justina Jankauskaite, Barcelona Supercomputing Center, European Molecular Biology Laboratory, Biotechnology and Biological Sciences Research Council (UK), Ministerio de Economía y Competitividad (España), and European Commission
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Statistics and Probability ,Cell signaling ,Binding free energy ,Protein-protein interactions ,Binding energy ,Kinetics ,Thermodynamics ,Plasma protein binding ,Protein–protein interactions ,Biochemistry ,Protein–protein interaction ,03 medical and health sciences ,Databases, Protein ,protein-protein ,binding energy ,kinetics ,thermodynamics ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,Chemistry ,Protein protein ,030302 biochemistry & molecular biology ,Protein engineering ,Proteïnes--Investigació ,Original Papers ,Structural Bioinformatics ,Receptor–ligand kinetics ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,Mutation ,SKEMPI ,Protein Binding ,Ciències de la salut [Àrees temàtiques de la UPC] ,Entropy (order and disorder) - Abstract
[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)].
- Published
- 2018
18. LightDock: a new multi-scale approach to protein–protein docking
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Jorge Roel-Touris, Juan Fernández-Recio, Miguel Romero-Durana, Brian Jiménez-García, Miquel Vidal, Daniel Jiménez-González, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions, Barcelona Supercomputing Center, Ministerio de Economía y Competitividad (España), European Commission, and Generalitat de Catalunya
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Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC] ,0301 basic medicine ,Statistics and Probability ,Protein Conformation ,Protein-protein interactions ,Computer science ,Computational biology ,Ligands ,Biochemistry ,Protein–protein interaction ,Protein docking ,03 medical and health sciences ,Structural bioinformatics ,Tryptophan Synthase ,Macromolecular docking ,Proteïnes -- Investigació ,Molecular Biology ,Protein protein ,Enginyeria biomèdica [Àrees temàtiques de la UPC] ,Computational Biology ,Proteins ,Proteïnes--Investigació ,Computer Science Applications ,Molecular Docking Simulation ,Computational Mathematics ,030104 developmental biology ,Computational Theory and Mathematics ,Protein–ligand docking ,Docking (molecular) ,Protein–protein ,Protein–protein interaction prediction ,Software ,Protein research ,Protein Binding - Abstract
[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.
- Published
- 2017
19. pyDockEneRes: per-residue decomposition of protein-protein docking energy
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Miguel Romero-Durana, Juan Fernández-Recio, Brian Jiménez-García, Ministerio de Economía y Competitividad (España), and European Commission
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Statistics and Probability ,Computer science ,Static Electricity ,Biochemistry ,Molecular Docking Simulation ,Protein–protein interaction ,03 medical and health sciences ,Desolvation ,Molecular Biology ,030304 developmental biology ,Alanine ,0303 health sciences ,Protein protein ,030302 biochemistry & molecular biology ,Energy landscape ,Proteins ,Ligand (biochemistry) ,Computer Science Applications ,Residue decomposition ,Computational Mathematics ,Computational Theory and Mathematics ,Docking (molecular) ,Biological system ,Algorithms ,Software ,Protein Binding - Abstract
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).
- Published
- 2019
20. MuGVRE. A virtual research environment for 3D/4D genomics
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Satish Sati, Genís Bayarri, François Serra, Giacomo Cavalli, Javier Conejero, Marco Pasi, Charles A. Laughton, Laia Codó, McDowall, Diana Buitrago, Dmitry Repchevsky, Juan Fernández-Recio, Marti-Renom M, David Castillo, Josep Ll. Gelpi, Mike N. Goodstadt, Reham F, Isabelle Brun-Heath, Rosa M. Badia, Andrew D. Yates, Meletiou A, Javier Álvarez Cid-Fuentes, Ricard Illa, Jürgen Walther, Alcantara Ja, Modesto Orozco, Romina Royo, and Brian Jiménez-García
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0303 health sciences ,business.industry ,Computer science ,Interoperability ,Cloud computing ,Genomics ,DNA sequencing ,Virtual research environment ,Metadata ,03 medical and health sciences ,0302 clinical medicine ,Workflow ,Programming paradigm ,Software engineering ,business ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available athttps://vre.multiscalegenomics.euand documentation and general information athttps://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.
- Published
- 2019
21. Information-Driven Modelling of Antibody-Antigen Complexes
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Brian Jiménez-García, Francesco Ambrosetti, Alexandre M. J. J. Bonvin, and Jorge Roel-Touris
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0303 health sciences ,Computer science ,Molecular biology ,030302 biochemistry & molecular biology ,Computational biology ,Epitope ,3. Good health ,Biological drugs ,03 medical and health sciences ,Chemistry ,Antigen ,Docking (dog) ,Docking (molecular) ,Antibody antigen ,Antibody ,030304 developmental biology - Abstract
Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial to improve our ability of designing efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for those complexes. We investigate here how information about complementary determining regions and binding epitopes can be used to drive the modelling process and present a comparative study of four different docking software (ClusPro, LightDock, ZDOCK and HADDOCK) providing specific options for antibody-antigen modelling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models both in the presence and absence of information about the epitope on the antigen.
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- 2019
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22. pyDock scoring for the new modeling challenges in docking: Protein-peptide, homo-multimers, and domain-domain interactions
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Chiara Pallara, Brian Jiménez-García, Miguel Romero, Juan Fernández-Recio, and Iain H. Moal
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0301 basic medicine ,03 medical and health sciences ,030104 developmental biology ,030102 biochemistry & molecular biology ,Structural Biology ,Computer science ,Server ,Macromolecular docking ,Data mining ,computer.software_genre ,Molecular Biology ,Biochemistry ,computer - Abstract
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.
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- 2016
23. IRaPPA: Information retrieval based integration of biophysical models for protein assembly selection
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Thom Vreven, Didier Barradas-Bautista, Mieczyslaw Torchala, Paul A. Bates, Arjan van der Velde, Zhiping Weng, Iain H. Moal, Juan Fernández-Recio, Brian Jiménez-García, and Barcelona Supercomputing Center
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0301 basic medicine ,Statistics and Probability ,Molecular biology ,Protein Conformation ,Computer science ,Information Storage and Retrieval ,computer.software_genre ,Biochemistry ,Molecular Docking Simulation ,Article ,Atomic modeling ,03 medical and health sciences ,Protein structure ,Software ,Proteïnes--Anàlisi ,Protein Interaction Mapping ,IRaPPA (Integrative Ranking of Protein–Protein Assemblies) ,Molecular Biology ,Biologia molecular ,Internet ,Information retrieval ,030102 biochemistry & molecular biology ,business.industry ,Enginyeria biomèdica [Àrees temàtiques de la UPC] ,Protein interactions ,Computer Science Applications ,Computational Mathematics ,030104 developmental biology ,Computational Theory and Mathematics ,Data mining ,Protein–protein interactions (PPIs) ,business ,computer - Abstract
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].
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- 2017
24. Blind prediction of interfacial water positions in CAPRI
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Pravin Muthu, Joy Sarmiento, John Wieting, Thom Vreven, Hasup Lee, Dima Kozakov, Haruki Nakamura, Julie C. Mitchell, Juan Fernández-Recio, Haim J. Wolfson, Sergei Grudinin, Yuko Tsuchiya, Iain H. Moal, Efrat Farkash, Chiara Pallara, Petras J. Kundrotas, Howook Hwang, Chaok Seok, Panagiotis L. Kastritis, Hahnbeom Park, Xiaoqin Zou, Junsu Ko, Justyna Aleksandra Wojdyla, Brian G. Pierce, Christophe Schmitz, Colin Kleanthous, Sanbo Qin, Shoshana J. Wodak, Paul A. Bates, Matsuyuki Shirota, Solène Grosdidier, Idit Buch, Ilya A. Vakser, Krishna Praneeth Kilambi, Jianqing Xu, Matthieu Chavent, Sandor Vajda, Adrien S. J. Melquiond, Marc F. Lensink, Shen You Huang, Martin Zacharias, David W. Ritchie, Brian Jiménez-García, Marc van Dijk, Ezgi Karaca, Yoichi Murakami, Daron M. Standley, Albert Solernou, Laura Pérez-Cano, Yang Shen, Miriam Eisenstein, Jeffrey J. Gray, Alexandre M. J. J. Bonvin, Zhiping Weng, Georgy Derevyanko, Kengo Kinoshita, Huan-Xiang Zhou, and Eiji Kanamori
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0303 health sciences ,010304 chemical physics ,Chemistry ,01 natural sciences ,Biochemistry ,Molecular Docking Simulation ,Force field (chemistry) ,Protein–protein interaction ,03 medical and health sciences ,Crystallography ,Molecular recognition ,Protein structure ,Structural Biology ,Docking (molecular) ,0103 physical sciences ,Critical assessment ,Macromolecular docking ,Biological system ,Molecular Biology ,030304 developmental biology - Abstract
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.
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- 2013
25. Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment
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Eichiro Ichiishi, Dmitri Beglov, Bernard Maigret, Gyu Rie Lee, Artem B. Mamonov, Shoshana J. Wodak, Jonathan C. Fuller, Dima Kozakov, Jong Young Joung, Petr Popov, Xiaofeng Yu, Keehyoung Joo, João P. G. L. M. Rodrigues, Anna Vangone, Koen M. Visscher, Xiaoqin Zou, Paul A. Bates, Andriy Kryshtafovych, Shourya S. Roy Burman, Daisuke Kihara, Romina Oliva, Efrat Ben-Zeev, Jeffrey J. Gray, Yang Shen, Li C. Xue, Sameer Velankar, Emilie Neveu, Shruthi Viswanath, Dina Schneidman-Duhovny, Juan Esquivel-Rodríguez, Mieczyslaw Torchala, Amit Roy, Alexandre M. J. J. Bonvin, David R. Hall, Tanggis Bohnuud, Xusi Han, David W. Ritchie, Ron Elber, Daisuke Kuroda, Zhiwei Ma, Joan Segura, Carlos A. Del Carpio, Nicholas A. Marze, Jong Yun Kim, Andrej Sali, Petras J. Kundrotas, Ezgi Karaca, Neil J. Bruce, Chaok Seok, Panagiotis L. Kastritis, Shen You Huang, Ilya A. Vakser, Lim Heo, Sanbo Qin, Raphael A. G. Chaleil, Adrien S. J. Melquiond, Miguel Romero-Durana, Anisah W. Ghoorah, Surendra S. Negi, Andrey Tovchigrechko, Françoise Ochsenbein, Narcis Fernandez-Fuentes, Liming Qiu, Miriam Eisenstein, Mehdi Nellen, Marie-Dominique Devignes, Lenna X. Peterson, Jinchao Yu, Minkyung Baek, Brian G. Pierce, Hasup Lee, Toshiyuki Oda, Rebecca C. Wade, Raphael Guerois, Juan Fernández-Recio, Iain H. Moal, Edrisse Chermak, Sergei Grudinin, Sangwoo Park, Ivan Anishchenko, Chengfei Yan, Thom Vreven, Kentaro Tomii, Bing Xia, Hyung Rae Kim, Chiara Pallara, Jooyoung Lee, Kazunori D. Yamada, Xianjin Xu, Kenichiro Imai, Zhiping Weng, Luigi Cavallo, Tyler M. Borrman, Jianlin Cheng, Marc F. Lensink, Huan-Xiang Zhou, Jilong Li, Gydo C. P. van Zundert, Brian Jiménez-García, Tsukasa Nakamura, Scott E. Mottarella, Sandor Vajda, Institut de Recherche Interdisciplinaire [Villeneuve d'Ascq] ( IRI ), Université de Lille, Sciences et Technologies-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique ( CNRS ), European Molecular Biology Laboratory, European Bioinformatics Institute, Genome Center [UC Davis], University of California at Davis, Research Support Computing [Columbia], University of Missouri-Columbia, Bioinformatics Consortium and Department of Computer Science [Columbia], Department of Bioengineering and Therapeutic Sciences, University of California [San Francisco] ( UCSF ), Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California [San Francisco] ( UCSF ) -California Institute for Quantitative Biosciences, GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, Centro Nacional de Biotecnología (CSIC), Institute of Biological, Environmental and Rural Sciences ( IBERS ), Institute for Computational Engineering and Sciences [Austin] ( ICES ), University of Texas at Austin [Austin], Department of Computer Science, Department of Chemistry, Algorithms for Modeling and Simulation of Nanosystems ( NANO-D ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire Jean Kuntzmann ( LJK ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), Moscow Institute of Physics and Technology [Moscow] ( MIPT ), Seoul National University [Seoul], Florida State University [Tallahassee] ( FSU ), Computational Algorithms for Protein Structures and Interactions ( CAPSID ), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Department of Complex Systems, Artificial Intelligence & Robotics ( LORIA - AIS ), Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), University of Mauritius, Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, G-INCPM, Weizmann Institute of Science, Chemical Research Support [Rehovot], Sealy Center for Structural Biology and Molecular Biophysics, The University of Texas Medical Branch ( UTMB ), Program in Bioinformatics and Integrative Biology [Worcester], University of Massachusetts Medical School [Worcester] ( UMASS ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Sud - Paris 11 ( UP11 ), Bijvoet Center for Biomolecular Research [Utrecht], Utrecht University [Utrecht], Dalton Cardiovascular Research Center [Columbia], Department of Computer Science [Columbia], Informatics Intitute, Department of Biochemistry, University of Missouri, UNIVERSITY OF MISSOURI, Toyota Technological Institute at Chicago [Chicago] ( TTIC ), Department of Biological Sciences, Purdue University, Purdue University [West Lafayette], Department of Computer Science [Purdue], Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies, Center for Molecular Biology ( ZMBH ), Universität Heidelberg [Heidelberg], Interdisciplinary Center for Scientific Computing ( IWR ), Department of Molecular Biosciences [Lawrence], University of Kansas [Lawrence] ( KU ), Computational Biology Research Center ( CBRC ), National Institute of Advanced Industrial Science and Technology ( AIST ), Graduate School of Frontier Sciences, The University of Tokyo, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center - Centro Nacional de Supercomputacion ( BSC - CNS ), Center for In-Silico Protein Science, Korea Institute for Advanced Study ( KIAS ), Center for Advanced Computation, Department of Biomedical Engineering [Boston], Boston University [Boston] ( BU ), Institute of Biological Diversity, International Pacific Institute of Indiana, Drosophila Genetic Resource Center, Kyoto Institute of Technology, International University of Health and Welfare Hospital ( IUHW Hospital ), International University of Health and Welfare Hospital, Department of Chemical and Biomolecular Engineering [Baltimore], Johns Hopkins University ( JHU ), Program in Molecular Biophysics [Baltimore], King Abdullah University of Science and Technology ( KAUST ), University of Naples, J Craig Venter Institute, Structural Biology Research Center, VIB, 1050 Brussels, Belgium, Institut de Recherche Interdisciplinaire [Villeneuve d'Ascq] (IRI), Université de Lille, Sciences et Technologies-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, University of California [Davis] (UC Davis), University of California (UC)-University of California (UC), University of Missouri [Columbia] (Mizzou), University of Missouri System, University of California [San Francisco] (UC San Francisco), Centro Nacional de Biotecnología [Madrid] (CNB-CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Biological, Environmental and Rural Sciences (IBERS), Institute for Computational Engineering and Sciences [Austin] (ICES), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Moscow Institute of Physics and Technology [Moscow] (MIPT), Seoul National University [Seoul] (SNU), Florida State University [Tallahassee] (FSU), Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Biomolecular Modelling Laboratory [London], The Francis Crick Institute [London], Weizmann Institute of Science [Rehovot, Israël], The University of Texas Medical Branch (UTMB), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Assemblage moléculaire et intégrité du génome (AMIG), Département Biochimie, Biophysique et Biologie Structurale (B3S), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Missouri System-University of Missouri System, Toyota Technological Institute at Chicago [Chicago] (TTIC), Department of Biological Sciences [Lafayette IN], Heidelberg Institute for Theoretical Studies (HITS ), Center for Molecular Biology (ZMBH), Universität Heidelberg [Heidelberg] = Heidelberg University, Interdisciplinary Center for Scientific Computing (IWR), University of Kansas [Lawrence] (KU), Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), The University of Tokyo (UTokyo), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Korea Institute for Advanced Study (KIAS), Boston University [Boston] (BU), International University of Health and Welfare Hospital (IUHW Hospital), Johns Hopkins University (JHU), King Abdullah University of Science and Technology (KAUST), University of Naples Federico II = Università degli studi di Napoli Federico II, J. Craig Venter Institute, VIB-VUB Center for Structural Biology [Bruxelles], VIB [Belgium], Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Droit et Santé-Université de Lille, Sciences et Technologies, University of California-University of California, University of California [San Francisco] (UCSF), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), University of Naples Federico II, Barcelona Supercomputing Center, NMR Spectroscopy, and Sub NMR Spectroscopy
- Subjects
0301 basic medicine ,Protein Conformation, alpha-Helical ,Protein Folding ,Computer science ,International Cooperation ,Amino Acid Motifs ,Oligomer state ,Homoprotein ,DATA-BANK ,computer.software_genre ,Molecular Docking Simulation ,Biochemistry ,CAPRI Round 30 ,DESIGN ,Structural Biology ,ALIGN ,Blind prediction ,AFFINITY ,Protein interaction ,Enginyeria biomèdica [Àrees temàtiques de la UPC] ,ZDOCK ,Oligomer State ,computer.file_format ,Articles ,Protein structure prediction ,Proteïnes--Investigació ,3. Good health ,WEB SERVER ,CASP ,Thermodynamics ,Data mining ,CAPRI ,Protein docking ,Molecular Biology ,Algorithms ,INTERFACES ,Protein Binding ,[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation ,Bioinformatics ,STRUCTURAL BIOLOGY ,Computational biology ,Molecular Dynamics Simulation ,Article ,03 medical and health sciences ,[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM] ,Heteroprotein ,Humans ,Protein binding ,Macromolecular docking ,Protein Interaction Domains and Motifs ,Homology modeling ,ALGORITHM ,Protein-protein docking ,Internet ,Binding Sites ,Models, Statistical ,030102 biochemistry & molecular biology ,Bacteria ,Sequence Homology, Amino Acid ,Computational Biology ,Proteins ,Protein Data Bank ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Protein Structure, Tertiary ,030104 developmental biology ,Structural biology ,Docking (molecular) ,Protein structure ,Protein Conformation, beta-Strand ,Protein Multimerization ,oligomer state ,blind prediction ,protein interaction ,protein docking ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,computer ,Software - Abstract
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.”
- Published
- 2016
26. A protein-RNA docking benchmark (II): Extended set from experimental and homology modeling data
- Author
-
Juan Fernández-Recio, Brian Jiménez-García, and Laura Pérez-Cano
- Subjects
Test case ,Protein structure ,Structural Biology ,Docking (molecular) ,A protein ,RNA ,Computational biology ,Homology modeling ,Nucleic acid structure ,Biology ,Molecular Biology ,Biochemistry ,Simulation - Abstract
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.
- Published
- 2012
27. pyDockSAXS: protein–protein complex structure by SAXS and computational docking
- Author
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Brian Jiménez-García, Dmitri I. Svergun, Juan Fernández-Recio, Pau Bernadó, Carles Pons, and Barcelona Supercomputing Center
- Subjects
FTDOCK ,Web server ,Protein-protein complex ,Protein-protein interactions ,Biology ,Protein–protein interactions ,computer.software_genre ,Bioinformatics ,Molecular Docking Simulation ,Computational science ,Upload ,Software ,X-Ray Diffraction ,Atomic resolution ,Protein Interaction Mapping ,Scattering, Small Angle ,Genetics ,Web Server issue ,Small-angle X-ray scattering (SAXS) ,Internet ,business.industry ,Small-angle X-ray scattering ,Enginyeria biomèdica [Àrees temàtiques de la UPC] ,Proteïnes--Enginyeria genètica ,Docking (molecular) ,Multiprotein Complexes ,business ,computer - Abstract
Structural characterization of protein–protein interactions at molecular level is essential to understand biological processes and identify new therapeutic opportunities. However, atomic resolution structural techniques cannot keep pace with current advances in interactomics. Low-resolution structural techniques, such as small-angle X-ray scattering (SAXS), can be applied at larger scale, but they miss atomic details. For efficient application to protein–protein complexes, low-resolution information can be combined with theoretical methods that provide energetic description and atomic details of the interactions. Here we present the pyDockSAXS web server (http://life.bsc.es/pid/pydocksaxs) that provides an automatic pipeline for modeling the structure of a protein–protein complex from SAXS data. The method uses FTDOCK to generate rigid-body docking models that are subsequently evaluated by a combination of pyDock energy-based scoring function and their capacity to describe SAXS data. The only required input files are structural models for the interacting partners and a SAXS curve. The server automatically provides a series of structural models for the complex, sorted by the pyDockSAXS scoring function. The user can also upload a previously computed set of docking poses, which opens the possibility to filter the docking solutions by potential interface residues or symmetry restraints. The server is freely available to all users without restriction. Programa Estatal I+D+i, the SpanishMinistry of Economy andCompetitiveness [BIO2013-48213-R to J.F.-R.]; Agence Nationale de la Recherche [SPIN-HD-ANR-CHEX-2011 and ATIP-Avenir Program to P.B.]; FPU Fellowship, the Spanish Ministry of Science and Innovation [BES-2011-045634 to B.J.G.]. Funding for open access charge: Spanish Ministry of Economy and Competitiveness [BIO2013-48213-R]; Agence Nationale de la Recherche [SPIN-HDANR- CHEX-2011].
- Published
- 2015
28. Expanding the frontiers of protein-protein modeling: from docking and scoring to binding affinity predictions and other challenges
- Author
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Miguel Romero-Durana, Iain H. Moal, Brian Jiménez-García, Solène Grosdidier, Chiara Pallara, Juan Fernández-Recio, Laura Pérez-Cano, Albert Solernou, and Carles Pons
- Subjects
Computer science ,Protein Conformation ,Carbohydrates ,Computational biology ,01 natural sciences ,Biochemistry ,03 medical and health sciences ,X-Ray Diffraction ,Structural Biology ,0103 physical sciences ,Scattering, Small Angle ,Molecular Biology ,Simulation ,030304 developmental biology ,Binding affinities ,High rate ,0303 health sciences ,010304 chemical physics ,Protein protein ,Computational Biology ,Proteins ,Water ,Molecular Docking Simulation ,Docking (molecular) ,Mutation ,Software ,Protein Binding - Abstract
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.
- Published
- 2013
29. A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data
- Author
-
Laura, Pérez-Cano, Brian, Jiménez-García, and Juan, Fernández-Recio
- Subjects
Models, Molecular ,Models, Chemical ,Protein Conformation ,Computational Biology ,Nucleic Acid Conformation ,RNA ,RNA-Binding Proteins ,Databases, Protein ,Algorithms ,Protein Binding - Abstract
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.
- Published
- 2011
30. pyDockWEB: a web server for rigid-body protein–protein docking using electrostatics and desolvation scoring
- Author
-
Brian Jiménez-García, Carles Pons, and Juan Fernández-Recio
- Subjects
Statistics and Probability ,Web server ,Computer science ,Static Electricity ,computer.software_genre ,Biochemistry ,Molecular Docking Simulation ,Computational science ,Software ,Protein Interaction Mapping ,Desolvation ,Molecular Biology ,Simulation ,Internet ,business.industry ,Protein protein ,Rigid body ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,Docking (molecular) ,Multiprotein Complexes ,The Internet ,business ,computer ,Algorithms - 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.The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock.Supplementary data are available at Bioinformatics online.
- Published
- 2013
31. CCharPPI web server: computational characterization of protein–protein interactions from structure
- Author
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Juan Fernández-Recio, Iain H. Moal, Brian Jiménez-García, and Barcelona Supercomputing Center
- Subjects
Statistics and Probability ,Web server ,Theoretical computer science ,Computer science ,Protein-protein interactions ,Distributed computing ,Static Electricity ,Protein–protein interactions ,computer.software_genre ,Biochemistry ,Protein–protein interaction ,03 medical and health sciences ,Software ,Proteïnes--Anàlisi ,Protein Interaction Mapping ,Humans ,Desolvation ,Structure-based potentials ,CCharPPI ,Molecular Biology ,030304 developmental biology ,Simulació, Mètodes de ,Internet ,0303 health sciences ,Biological systems--Computer simulation ,Hydrogen bond ,business.industry ,030302 biochemistry & molecular biology ,Enginyeria biomèdica [Àrees temàtiques de la UPC] ,Protein interactions ,Hydrogen Bonding ,Parameter calculation tools ,Proteïnes--Enginyeria genètica ,Proteïnes--Investigació ,Computer Science Applications ,Molecular Docking Simulation ,Computational Mathematics ,Computational Theory and Mathematics ,Docking (molecular) ,Multiprotein Complexes ,The Internet ,Proteins--Analysis ,business ,computer - Abstract
The atomic structures of protein–protein interactions are central to understanding their role in biological systems, and a wide variety of biophysical functions and potentials have been developed for their characterization and the construction of predictive models. These tools are scattered across a multitude of stand-alone programs, and are often available only as model parameters requiring reimplementation. This acts as a significant barrier to their widespread adoption. CCharPPI integrates many of these tools into a single web server. It calculates up to 108 parameters, including models of electrostatics, desolvation and hydrogen bonding, as well as interface packing and complementarity scores, empirical potentials at various resolutions, docking potentials and composite scoring functions. The methods implemented have come from many laboratories, and the authors would like to thank all those who have made their parameters, code and software available, and for clarifying questions regarding licencing. Funding: The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007-2013) under REA grant agreement PIEF-GA-2012-327899 and grant BIO2013-48213-R from Spanish Ministry of Economy and Competitiveness.
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