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4 results on '"Jorge Roel-Touris"'

1. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Author

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)

Subjects
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

Published
2021
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2. An overview of data‐driven HADDOCK strategies in CAPRI rounds 38‐45

Author

Adrien S. J. Melquiond, Mikael Trellet, Rodrigo V. Honorato, Jörg Schaarschmidt, Cunliang Geng, Francesco Ambrosetti, Anna Vangone, Zeynep Kurkcuoglu, Jorge Roel-Touris, Li C. Xue, Irina S. Moreira, Panagiotis I. Koukos, and Alexandre M. J. J. Bonvin

Subjects
Protein Conformation, alpha-Helical, Technology, biomolecular interactions, Computer science, computer.software_genre, Ligands, Biochemistry, Force field (chemistry), Data-driven, 03 medical and health sciences, Structural Biology, Protein Interaction Mapping, integrative modeling, Humans, Desolvation, Protein Interaction Domains and Motifs, Amino Acid Sequence, Linear combination, Conformational sampling, Molecular Biology, 030304 developmental biology, 0303 health sciences, complexes, Binding Sites, biology, 030302 biochemistry & molecular biology, scoring, Proteins, Haddock, prediction, biology.organism_classification, Molecular Docking Simulation, Docking (molecular), Research Design, Structural Homology, Protein, Thermodynamics, Model quality, Protein Conformation, beta-Strand, Data mining, Protein Multimerization, Peptides, ddc:600, computer, Software, Protein Binding
Abstract

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

Published
2020
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3. LightDock: a new multi-scale approach to protein–protein docking

Author

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

Subjects
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
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4. Information-Driven Modelling of Antibody-Antigen Complexes

Author

Brian Jiménez-García, Francesco Ambrosetti, Alexandre M. J. J. Bonvin, and Jorge Roel-Touris

Subjects
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.

Published
2019
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