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7 results on '"Trellet, Mikael"'

1. Inhibition of the integrated stress response by viral proteins that block p-eIF2–eIF2B association

Author

Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, van Kuppeveld, Frank J M, LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, and Biomolecular Mass Spectrometry and Proteomics

Subjects
Microbiology (medical), Eukaryotic Initiation Factor-2, Immunology, Picornaviridae, Applied Microbiology and Biotechnology, Microbiology, Gene Knockout Techniques, Viral Proteins, 03 medical and health sciences, Eukaryotic translation, Stress, Physiological, Chlorocebus aethiops, Taverne, Genetics, Animals, Humans, Integrated stress response, Phosphorylation, Vero Cells, 030304 developmental biology, 0303 health sciences, eIF2, Binding Sites, biology, 030306 microbiology, Chemistry, Translation (biology), Cell Biology, Cell biology, Eukaryotic Initiation Factor-2B, Eukaryotic Cells, HEK293 Cells, eIF2B, biology.protein, Guanine nucleotide exchange factor, HeLa Cells, Protein Binding
Abstract

Eukaryotic cells, when exposed to environmental or internal stress, activate the integrated stress response (ISR) to restore homeostasis and promote cell survival. Specific stress stimuli prompt dedicated stress kinases to phosphorylate eukaryotic initiation factor 2 (eIF2). Phosphorylated eIF2 (p-eIF2) in turn sequesters the eIF2-specific guanine exchange factor eIF2B to block eIF2 recycling, thereby halting translation initiation and reducing global protein synthesis. To circumvent stress-induced translational shutdown, viruses encode ISR antagonists. Those identified so far prevent or reverse eIF2 phosphorylation. We now describe two viral proteins-one from a coronavirus and the other from a picornavirus-that have independently acquired the ability to counteract the ISR at its very core by acting as a competitive inhibitor of p-eIF2-eIF2B interaction. This allows continued formation of the eIF2-GTP-Met-tRNAi ternary complex and unabated global translation at high p-eIF2 levels that would otherwise cause translational arrest. We conclude that eIF2 and p-eIF2 differ in their interaction with eIF2B to such effect that p-eIF2-eIF2B association can be selectively inhibited.

Published
2020
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2. Inhibition of the integrated stress response by viral proteins that block p-eIF2-eIF2B association

Author

Rabouw, Huib H, Visser, Linda J, Passchier, Tim C, Langereis, Martijn A, Liu, Fan, Giansanti, Piero, van Vliet, Arno L W, Dekker, José G, van der Grein, Susanne G, Saucedo, Jesús G, Anand, Aditya A, Trellet, Mikael E, Bonvin, Alexandre M J J, Walter, Peter, Heck, Albert J R, de Groot, Raoul J, van Kuppeveld, Frank J M, LS Virologie, dI&I I&I-1, Sub Biomol.Mass Spectrometry & Proteom., Virologie, dB&C I&I, Sub NMR Spectroscopy, Afd Biomol.Mass Spect. and Proteomics, and Biomolecular Mass Spectrometry and Proteomics

Subjects
Taverne
Abstract

Eukaryotic cells, when exposed to environmental or internal stress, activate the integrated stress response (ISR) to restore homeostasis and promote cell survival. Specific stress stimuli prompt dedicated stress kinases to phosphorylate eukaryotic initiation factor 2 (eIF2). Phosphorylated eIF2 (p-eIF2) in turn sequesters the eIF2-specific guanine exchange factor eIF2B to block eIF2 recycling, thereby halting translation initiation and reducing global protein synthesis. To circumvent stress-induced translational shutdown, viruses encode ISR antagonists. Those identified so far prevent or reverse eIF2 phosphorylation. We now describe two viral proteins-one from a coronavirus and the other from a picornavirus-that have independently acquired the ability to counteract the ISR at its very core by acting as a competitive inhibitor of p-eIF2-eIF2B interaction. This allows continued formation of the eIF2-GTP-Met-tRNAi ternary complex and unabated global translation at high p-eIF2 levels that would otherwise cause translational arrest. We conclude that eIF2 and p-eIF2 differ in their interaction with eIF2B to such effect that p-eIF2-eIF2B association can be selectively inhibited.

Published
2020

3. Protein–Protein Modeling Using Cryo-EM Restraints

Author

Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., Gáspári, Zoltán, Sub NMR Spectroscopy, and NMR Spectroscopy

Subjects
Taverne, Cryo-EM data, Genetics, Information-driven docking, Molecular modeling, HADDOCK, Biomolecular interactions, Flexibility, Molecular Biology
Abstract

Recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low- to medium-resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modeling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore key aspects of molecular recognition, the energetics and flexibility of the interfaces. Moreover, this often restricts the modeling to a unique source of data, the EM density map. In this chapter, we describe a protocol where an EM map is used as restraint in HADDOCK to guide the modeling process. In the first step, rigid-body fitting is performed with PowerFit in order to identify the most likely locations of the molecules into the map. These are then used as centroids to which distance restraints are defined from the center of mass of the components of the complex for the initial rigid-body docking. The EM density is then directly used as an additional restraint energy term, which can be combined with all the other types of data supported by HADDOCK. This protocol relies on the new version 2.4 of both the HADDOCK webserver and software. Preparation steps consisting of cropping the EM map and rigid-body fitting of the atomic structure are explained. Then, the EM-driven docking protocol using HADDOCK is illustrated.

Published
2020

4. Protein–Protein Modeling Using Cryo-EM Restraints

Author

Trellet, Mikael, van Zundert, Gydo, Bonvin, Alexandre M.J.J., Gáspári, Zoltán, Sub NMR Spectroscopy, and NMR Spectroscopy

Subjects
Molecular model, Computer science, Cryo-electron microscopy, Biophysics, Cryo-EM data, Molecular modeling, Protein protein, 03 medical and health sciences, Molecular recognition, Taverne, Genetics, Molecule, Molecular Biology, 030304 developmental biology, 0303 health sciences, 030302 biochemistry & molecular biology, Information-driven docking, HADDOCK, computer.file_format, Protein Data Bank, Chemistry, Docking (molecular), Biomolecular interactions, Flexibility, Biological system, computer
Abstract

Comment: 28 pages including 7 figures, The recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low to medium resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modelling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore key aspects of molecular recognition, the energetics and flexibility of the interfaces. Moreover, these often restricts the modelling to a unique source of data, the EM density map. In this chapter, we describe a protocol where an EM map is used as restraint in HADDOCK to guide the modelling process.

Published
2020

5. Blind prediction of homo- and hetero- protein complexes: The CASP13-CAPRI experiment

Author

Lensink, Marc F., Brysbaert, Guillaume, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Gerguri, Tereza, Bates, Paul A., Laine, Elodie, Carbone, Alessandra, Grudinin, Sergei, Kong, Ren, Liu, Ran-Ran, Xu, Xi-Ming, Shi, Hang, Chang, Shan, Eisenstein, Miriam, Karczynska, Agnieszka, Czaplewski, Cezary, Lubecka, Emilia, Lipska, Agnieszka, Krupa, Paweł, Mozolewska, Magdalena, Golon, Łukasz, Samsonov, Sergey, Liwo, Adam, Crivelli, Silvia, Pagès, Guillaume, Karasikov, Mikhail, Kadukova, Maria, Yan, Yumeng, Huang, Sheng-You, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Romero-Durana, Miguel, Díaz-Bueno, Lucía, Fernandez-Recio, Juan, Christoffer, Charles, Terashi, Genki, Shin, Woong-Hee, Aderinwale, Tunde, Maddhuri Venkata Subraman, Sai Raghavendra, Kihara, Daisuke, Kozakov, Dima, Vajda, Sandor, Porter, Kathryn, Padhorny, Dzmitry, Desta, Israel, Beglov, Dmitri, Ignatov, Mikhail, Kotelnikov, Sergey, Moal, Iain H., Ritchie, David W., Chauvot de Beauchêne, Isaure, Maigret, Bernard, Devignes, Marie-Dominique, Ruiz Echartea, Maria E., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Cao, Yue, Shen, Yang, Baek, Minkyung, Park, Taeyong, Woo, Hyeonuk, Seok, Chaok, Braitbard, Merav, Bitton, Lirane, Scheidman-Duhovny, Dina, Dapkūnas, Justas, Olechnovič, Kliment, Venclovas, Česlovas, Kundrotas, Petras J., Belkin, Saveliy, Chakravarty, Devlina, Badal, Varsha D., Vakser, Ilya A., Vreven, Thom, Vangaveti, Sweta, Borrman, Tyler, Weng, Zhiping, Guest, Johnathan D., Gowthaman, Ragul, Pierce, Brian G., Xu, Xianjin, Duan, Rui, Qiu, Liming, Hou, Jie, Ryan Merideth, Benjamin, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jörg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li, Jiménez-García, Brian, van Noort, Charlotte W., Honorato, Rodrigo V., Bonvin, Alexandre M. J. J., Wodak, Shoshana J., 0000-0003-3957-9470, 0000-0003-2759-2088, 0000-0003-0621-0925, 0000-0003-2098-5743, 0000-0001-7169-9398, 0000-0002-0294-3403, 0000-0002-4209-4565, 0000-0002-7787-8896, 0000-0002-3986-7686, 0000-0002-6163-6323, 0000-0003-4091-6614, 0000-0003-0464-4500, 0000-0002-4960-5487, 0000-0002-7035-3042, 0000-0002-1703-7796, 0000-0002-1419-9888, 0000-0002-0496-6107, 0000-0002-4215-0213, 0000-0002-5743-2934, 0000-0003-3413-806X, 0000-0002-3032-7966, 0000-0002-1409-8358, 0000-0001-7369-1322, 0000-0002-0701-6545, Unité de Glycobiologie Structurale et Fonctionnelle UMR 8576 (UGSF), Institut National de la Recherche Agronomique (INRA)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, The Francis Crick Institute [London], School of Geographical Sciences [Bristol], University of Bristol [Bristol], Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), 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)-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]), JiangSu University, Chemical Research Support [Rehovot], Weizmann Institute of Science [Rehovot, Israël], University of Gdańsk (UG), Department of Environmental Analytics [Univ Gdańsk], Faculty of Chemistry [Univ Gdańsk], University of Gdańsk (UG)-University of Gdańsk (UG), Institute of Physics [Warsaw] (IFPAN), Polish Academy of Sciences (PAN), Department of Computer Science [Davis] (UC Davis), University of California [Davis] (UC Davis), University of California-University of California, Moscow Institute of Physics and Technology [Moscow] (MIPT), Huazhong University of Science and Technology [Wuhan] (HUST), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Purdue University [West Lafayette], Kitasato University, Stony Brook University [SUNY] (SBU), State University of New York (SUNY), Department of Biomedical Engineering [Boston], Boston University [Boston] (BU), 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), 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), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), King Abdullah University of Science and Technology (KAUST), University of Naples Federico II, Service des Recherches Métallurgiques Appliquées (SRMA), Département des Matériaux pour le Nucléaire (DMN), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Department of Chemistry, Seoul National University [Seoul] (SNU), The Hebrew University of Jerusalem (HUJ), Vilnius University [Vilnius], Department of Molecular Biosciences [Lawrence], University of Kansas [Lawrence] (KU), University of Kansas [Kansas City], University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS), Program in Bioinformatics and Integrative Biology [Worcester], University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS), University of Maryland [Baltimore], Beijing University of Technology, University of Missouri [Columbia] (Mizzou), University of Missouri System, Bijvoet Center for Biomolecular Research [Utrecht], Utrecht University [Utrecht], Dalton Cardiovascular Research Center [Columbia], University of Missouri System-University of Missouri System, Shandong University, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), University of California [Santa Cruz] (UCSC), University of California, The Hospital for sick children [Toronto] (SickKids), Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Institut des Hautes Etudes Scientifiques (IHES), IHES, The Francis Crick Institute, Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), 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), Weizmann Institute of Science, University of Gdańsk, Department of Environmental Analytics [Gdańsk], 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), Technische Universität München [München] (TUM), CEA-Direction de l'Energie Nucléaire (CEA-DEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction de l'Energie Nucléaire (CEA-DEN), University of Missouri [Columbia], Université Paris-Sud - Paris 11 (UP11)-Université Paris-Saclay-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Université de Lille, CNRS, Unité de Glycobiologie Structurale et Fonctionnelle UMR 8576 [UGSF], Agence Nationale de la Recherche (France), Cancer Research UK, European Commission, Medical Research Council (UK), National Institutes of Health (US), National Natural Science Foundation of China, National Research Foundation of Korea, National Science Foundation (US), Ministerio de Economía y Competitividad (España), Università degli Studi di Napoli PARTHENOPE, Wellcome Trust, Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Computer Science [Univ California Davis] (CS - UC Davis), University of California (UC)-University of California (UC), University of Naples Federico II = Università degli studi di Napoli Federico II, University of California [Santa Cruz] (UC Santa Cruz), University of California (UC), and Grudinin, Sergei

Subjects
Models, Molecular, Computer science, Protein Conformation, Protein complexes, Template‐based modeling, Oligomeric state, Biochemistry, Docking, protein-protein interaction, Structural Biology, Protein Interaction Mapping, Taverne, Blind prediction, Protein assemblies, Databases, Protein, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 0303 health sciences, oligomeric state, 030302 biochemistry & molecular biology, protein assemblies, computer.file_format, 3. Good health, CASP, docking, blind prediction, Protein‐protein interaction, CAPRI, proteincomplexes, Algorithms, Protein Binding, protein complexes, template-based modeling, Computational biology, Article, Protein–protein interaction, 03 medical and health sciences, protein‐protein interaction, template‐based modeling, Molecular Biology, 030304 developmental biology, Binding Sites, Computational Biology, Proteins, Protein Data Bank, Docking (molecular), Structural Homology, Protein, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software
Abstract

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements., Agence Nationale de la Recherche, Grant/Award Number: ANR‐15‐CE11‐0029‐03; Cancer Research UK, Grant/Award Number: FC001003; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Lietuvos Mokslo Taryba, Grant/Award Number: S‐MIP‐17‐60; Medical Research Council, Grant/Award Number: FC001003; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM123055, R35GM124952; National Natural Science Foundation of China, Grant/Award Number: 31670724; National Research Foundation of Korea, Grant/Award Number: 2016M3C4A7952630; National Science Foundation, Grant/Award Number: DBI1565107; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Number: 718.015.001; Spanish >Programma Estatal I+D+i>, Grant/Award Number: BIO2016‐79930‐R; University Parthenope, Grant/Award Number: Finanziamento per il Sostegno alla Ricerca Individ; Wellcome Trust, Grant/Award Number: FC001003

Published
2019
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6. Information-driven modeling of protein-peptide complexes

Author

Trellet, Mikael, Melquiond, Adrien S J, Bonvin, Alexandre M J J, Sub NMR Spectroscopy, and NMR Spectroscopy

Subjects
Conformational changes, Taverne, Genetics, Information-driven docking, Molecular modeling, HADDOCK, Biomolecular interactions, Flexibility, Molecular Biology
Abstract

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are diffi cult to study experimentally at the structural level because of the inherent fl exibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplifi ed by the recent introduction of proteinpeptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high fl exibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to defi ne the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The fl exibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fi t molecular recognition theories. Key words Biomolecular interactions, Information-driven docking, Conformational changes, Flexibility, HADDOCK, Molecular modeling.

Published
2015

7. Information-driven modeling of protein-peptide complexes

Author

Trellet, Mikael, Melquiond, Adrien S J, Bonvin, Alexandre M J J, Sub NMR Spectroscopy, and NMR Spectroscopy

Subjects
Conformational changes, Taverne, Genetics, Information-driven docking, Molecular modeling, HADDOCK, Biomolecular interactions, Flexibility, Molecular Biology
Abstract

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are diffi cult to study experimentally at the structural level because of the inherent fl exibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplifi ed by the recent introduction of proteinpeptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high fl exibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to defi ne the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The fl exibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fi t molecular recognition theories. Key words Biomolecular interactions, Information-driven docking, Conformational changes, Flexibility, HADDOCK, Molecular modeling.

Published
2015

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