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2. pyDockDNA: A new web server for energy-based protein-DNA docking and scoring

Author

Luis Angel Rodríguez-Lumbreras, Brian Jiménez-García, Silvia Giménez-Santamarina, and Juan Fernández-Recio

Subjects
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).

Published
2022
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3. Integrative modeling of membrane-associated protein assemblies

Author

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

Subjects
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.

Published
2020
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5. Towards design of drugs and delivery systems with the Martini coarse-grained model

Author

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

Subjects
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.

Published
2022
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6. Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem

Author

Rodrigo V. Honorato, Panagiotis I. Koukos, Brian Jiménez-García, Andrei Tsaregorodtsev, Marco Verlato, Andrea Giachetti, Antonio Rosato, and Alexandre M. J. J. Bonvin

Subjects
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.

Published
2021
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7. 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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8. Structural characterization of protein–protein interactions with pyDockSAXS

Author

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)

Subjects
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).

Published
2020

9. 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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10. IRaPPA: Information retrieval based integration of biophysical models for protein assembly selection

Author

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

Subjects
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].

Published
2017

11. Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment

Author

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
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12. pyDockSAXS: protein–protein complex structure by SAXS and computational docking

Author

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

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