Author: "Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )" / Search Limiters: Full Text - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )"' showing total 77 results

Start Over Author "Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )" Search Limiters Full Text

77 results on '"Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )"'

1. 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
Full Text: View/download PDF

2. Modelling time variations of root diameter and elongation rate as related to assimilate supply and demand

Author: Loïc Pagès, Marie Bernert, Guillaume Pagès, Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), BrainTech Laboratory [CHU Grenoble Alpes - Inserm U1205] (Brain Tech Lab ), CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Lucas, Nelly, 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)-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 ), and Université Grenoble Alpes (UGA)
Subjects: 0106 biological sciences, 0301 basic medicine, phenotyping, root growth, Physiology, Meristem, Root (chord), Plant Science, Root system, Root tip, Plasticity, Plant Roots, 01 natural sciences, Growth model, 03 medical and health sciences, MESH: Growth model, photo-assimilates, root meristem, root traits, structure-function model, vascular resistance, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, Mathematics, AcademicSubjects/SCI01210, Maximal diameter, 15. Life on land, Research Papers, 030104 developmental biology, Homogeneous, structure–function model, Growth and Development, Time variations, Elongation, Biological system, human activities, 010606 plant biology & botany
Abstract: We present a simple and generic model, based on mechanistic hypotheses, to represent the longitudinal variations of the diameter of individual roots and the associated diversity of growth patterns., In a given root system, individual roots usually exhibit a rather homogeneous tip structure although highly different diameters and growth patterns, and this diversity is of prime importance in the definition of the whole root system architecture and foraging characteristics. In order to represent and predict this diversity, we built a simple and generic model at root tip level combining structural and functional knowledge on root elongation. The tip diameter, reflecting meristem size, is used as a driving variable of elongation. It varies, in response to the fluctuations of photo-assimilate availability, between two limits (minimal and maximal diameter). The elongation rate is assumed to be dependent on the transient value of the diameter. Elongation stops when the tip reaches the minimal diameter. The model could satisfactorily reproduce patterns of root elongation and tip diameter changes observed in various species at different scales. Although continuous, the model could generate divergent root classes as classically observed within populations of lateral roots. This model should help interpret the large plasticity of root elongation patterns which can be obtained in response to different combinations of endogenous and exogenous factors. The parameters could be used in phenotyping the root system.
Published: 2020
Full Text: View/download PDF

3. Predicting Protein Functional Motions: an Old Recipe with a New Twist

Author: Sergei Grudinin, Alexandre Hoffmann, Elodie Laine, 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), 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), 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), and ANR-17-CE12-0009,MASSIV,Modèle(s) d'Evolution de l'Epissage Alternatif et de son Impact Structural(2017)
Subjects: Models, Molecular, Rigid block, Work (thermodynamics), Protein Conformation, Structure (category theory), Biophysics, Motion (geometry), Structural heterogeneity, Crystallography, X-Ray, 01 natural sciences, Structural transitions, Article, 03 medical and health sciences, 0302 clinical medicine, Protein structure, Collective motions, Normal mode, 0103 physical sciences, Cryo-EM fitting, Statistical physics, Twist, Nonlinear normal modes, Flexible docking, NMA, 030304 developmental biology, Physics, Quantitative Biology::Biomolecules, 0303 health sciences, 010304 chemical physics, Series (mathematics), Cryoelectron Microscopy, Proteins, Nonlinear system, Range (mathematics), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery
Abstract: Large macromolecules, including proteins and their complexes, very often adopt multiple conformations. Some of them can be seen experimentally, for example with X-ray crystallography or cryo-electron microscopy. This structural heterogeneity is not occasional and is frequently linked with specific biological function. Thus, the accurate description of macromolecular conformational transitions is crucial for understanding fundamental mechanisms of life’s machinery. We report on a real-time method to predict such transitions by extrapolating from instantaneous eigen-motions, computed using the normal mode analysis, to a series of twists. We demonstrate the applicability of our approach to the prediction of a wide range of motions, including large collective opening-closing transitions and conformational changes induced by partner binding. We also highlight particularly difficult cases of very small transitions between crystal and solution structures. Our method guaranties preservation of the protein structure during the transition and allows to access conformations that are unreachable with classical normal mode analysis. We provide practical solutions to describe localized motions with a few low-frequency modes and to relax some geometrical constraints along the predicted transitions. This work opens the way to the systematic description of protein motions, whatever their degree of collectivity. Our method is available as a part of the NOn-Linear rigid Block (NOLB) package athttps://team.inria.fr/nano-d/software/nolb-normal-modes/.Significance StatementProteins perform their biological functions by changing their shapes and interacting with each other. Getting access to these motions is challenging. In this work, we present a method that generatesplausiblephysics-based protein motions and conformations. We model a protein as a network of atoms connected by springs and deform it along the least-energy directions. Our main contribution is to perform the deformations in a nonlinear way, through a series of twists. This allows us to produce a wide range of motions, some of them previously inaccessible, and to preserve the structure of the protein during the motion. We are able to simulate the opening or closing of a protein and the changes it undergoes to adapt to a partner.
Published: 2020
Full Text: View/download PDF

4. 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
Full Text: View/download PDF

5. Modeling SARS‐CoV‐2 proteins in the CASP‐commons experiment

Author: Kryshtafovych, Andriy, Moult, John, Billings, Wendy, Della Corte, Dennis, Fidelis, Krzysztof, Kwon, Sohee, Olechnovič, Kliment, Seok, Chaok, Venclovas, Česlovas, Won, Jonghun, Adhikari, Badri, Adiyaman, Recep, Aguirre-Plans, Joaquim, Anishchenko, Ivan, Baek, Minkyung, Baker, David, Baldassarre, Frederico, Barger, Jacob, Bhattacharya, Sutanu, Bhattacharya, Debswapna, Bitton, Mor, Cao, Renzhi, Cheng, Jianlin, Christoffer, Charles, Czaplewski, Cezary, Elofsson, Arne, Faraggi, Eshel, Feig, Michael, Fernandez-Fuentes, Narcis, Grishin, Nick, Grudinin, Sergei, Guo, Zhiye, Hanazono, Yuya, Hassabis, Demis, Hedelius, Bryce, Heo, Lim, Hiranuma, Naozumi, Hunt, Cassandra, Igashov, Ilia, Ishida, Takashi, Jernigan, Robert, Jones, David, Jumper, John, Kadukova, Maria, Kandathil, Shaun, Keasar, Chen, Kihara, Daisuke, Kinch, Lisa, Kiyota, Yasuomi, Kloczkowski, Andrzje, Kohli, Pushmeet, Kogut, Mateusz, Laine, Elodie, Lilley, Cade, Liu, Jian, Liwo, Adam, Lubecka, Emilia, Mondal, Arup, Morris, Connor, Mcguffin, Liam, Molina, Alexis, Nakamura, Tsukasa, Oliva, Baldo, Perez, Alberto, Pozzati, Gabriele, Sarkar, Daipayan, Sato, Rin, Schwede, Torsten, Shrestha, Bikash, Sidi, Tomer, Studer, Gabriel, Shuvo, Md Hossain, Takeda-Shitaka, Mayuko, Takei, Yuma, Terashi, Genki, Tomii, Kentaro, Tsuchiya, Yuko, Tunyasuvunakool, Kathryn, Waliner, Björn, Wu, Tianqi, Xu, Jinbo, Yamamori, Yu, Zhang, Chengxin, Zhang, Yang, Zheng, Wei, University of California [Davis] (UC Davis), University of California (UC), Institute for Bioscience and Biotechnology Research [Rockville, MD, États-Unis] (IBBR), University of Maryland [College Park], University of Maryland System-University of Maryland System, Brigham Young University (BYU), Seoul National University [Seoul] (SNU), Vilnius University [Vilnius], University of Missouri [St. Louis], University of Missouri System, University of Reading (UOR), Universitat Pompeu Fabra [Barcelona] (UPF), Biorobotics Lab (University of Washington), University of Washington [Seattle], Royal Institute of Technology [Stockholm] (KTH ), Auburn University (AU), Ben-Gurion University of the Negev (BGU), Pacific Lutheran University [Tacoma] (PLU), University of Missouri [Columbia] (Mizzou), Purdue University [West Lafayette], University of Gdańsk (UG), Stockholm University, Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Michigan State University [East Lansing], Michigan State University System, Aberystwyth University, University of Texas Southwestern Medical Center [Dallas], Données, Apprentissage et Optimisation (DAO), 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)-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), National Institutes for Quantum and Radiological Science and Technology (QST), DeepMind [London], DeepMind Technologies, Moscow Institute of Physics and Technology [Moscow] (MIPT), Tokyo Institute of Technology [Tokyo] (TITECH), Iowa State University (ISU), University College of London [London] (UCL), Kitasato University, Ohio State University [Columbus] (OSU), 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), 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), University of Florida [Gainesville] (UF), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Tohoku University [Sendai], University of Basel (Unibas), National Institute of Advanced Industrial Science and Technology (AIST), Linköping University (LIU), Toyota Technological Institute at Chicago [Chicago] (TTIC), University of Michigan [Ann Arbor], University of Michigan System, University of California, University of Maryland [Baltimore], 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), and 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 )
Subjects: Models, Molecular, 2019-20 coronavirus outbreak, Research groups, Protein Conformation, Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Model accuracy, Genome, Viral, computer.software_genre, Biochemistry, SARS‐CoV‐2, Viroporin Proteins, Domain (software engineering), Viral Proteins, 03 medical and health sciences, Protein Domains, Structural Biology, EMA, Humans, CASP, Molecular Biology, Research Articles, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, COVID, 0303 health sciences, SARS-CoV-2, 030302 biochemistry & molecular biology, COVID-19, Protein structure prediction, Model quality, Critical assessment, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Research Article
Abstract: International audience; Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).
Published: 2021
Full Text: View/download PDF

6. Spherical convolutions on molecular graphs for protein model quality assessment

Author: Ilia Igashov, Nikita Pavlichenko, Sergei Grudinin, Moscow Institute of Physics and Technology [Moscow] (MIPT), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), This work was partially supported by the Inria International Partnership program BIOTOOLS., 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)-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 ), and Université Grenoble Alpes (UGA)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Coordinate system, Image processing, Topology (electrical circuits), Quantitative Biology - Quantitative Methods, Convolutional neural network, Convolution, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, CASP, Quantitative Methods (q-bio.QM), 030304 developmental biology, Structure (mathematical logic), 0303 health sciences, rotational equivariance, Spherical harmonics, spherical harmonics, structural bioinformatics, Graph Convolutional Network, Human-Computer Interaction, 030220 oncology & carcinogenesis, FOS: Biological sciences, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithm, Software
Abstract: Processing information on three-dimensional (3D) objects requires methods stable to rigid-body transformations, in particular rotations, of the input data. In image processing tasks, convolutional neural networks achieve this property using rotation-equivariant operations. However, contrary to images, graphs generally have irregular topology. This makes it challenging to define a rotation-equivariant convolution operation on these structures. In this work, we propose spherical graph convolutional network that processes 3D models of proteins represented as molecular graphs. In a protein molecule, individual amino acids have common topological elements. This allows us to unambiguously associate each amino acid with a local coordinate system and construct rotation-equivariant spherical filters that operate on angular information between graph nodes. Within the framework of the protein model quality assessment problem, we demonstrate that the proposed spherical convolution method significantly improves the quality of model assessment compared to the standard message-passing approach. It is also comparable to state-of-the-art methods, as we demonstrate on critical assessment of structure prediction benchmarks. The proposed technique operates only on geometric features of protein 3D models. This makes it universal and applicable to any other geometric-learning task where the graph structure allows constructing local coordinate systems. The method is available at https://team.inria.fr/nano-d/software/s-gcn/.
Published: 2021
Full Text: View/download PDF

7. HOPMA: Boosting protein functional dynamics with colored contact maps

Author: Sergei Grudinin, Elodie Laine, 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), 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), Données, Apprentissage et Optimisation (DAO), 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)-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), Grudinin, Sergei, 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), and 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 )
Subjects: Models, Molecular, Boosting (machine learning), Macromolecular Substances, Protein Conformation, Computer science, Structure (category theory), Crystallography, X-Ray, 010402 general chemistry, Topology, 01 natural sciences, Set (abstract data type), 03 medical and health sciences, Protein structure, Normal mode, 0103 physical sciences, Materials Chemistry, Physical and Theoretical Chemistry, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 030304 developmental biology, 0303 health sciences, 010304 chemical physics, Functional protein, Proteins, A protein, Function (mathematics), Protein structure prediction, 0104 chemical sciences, Surfaces, Coatings and Films, Nonlinear system, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Macromolecule
Abstract: In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches.We present HOPMA, a novel method to predict protein functional states and transitions using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures.The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.
Published: 2021
Full Text: View/download PDF

8. VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

Author: Česlovas Venclovas, Kliment Olechnovič, Sergei Grudinin, Maria Kadukova, Ilia Igashov, Moscow Institute of Physics and Technology [Moscow] (MIPT), Données, Apprentissage et Optimisation (DAO), 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)-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), Vilnius University [Vilnius], 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), and 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 )
Subjects: Statistics and Probability, 0303 health sciences, Computational model, 010304 chemical physics, Computer science, 030302 biochemistry & molecular biology, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 01 natural sciences, Biochemistry, Convolutional neural network, Computer Science Applications, 03 medical and health sciences, Computational Mathematics, Protein structure, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computational Theory and Mathematics, 0103 physical sciences, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Voronoi diagram, Molecular Biology, Algorithm, 030304 developmental biology
Abstract: Motivation Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. Results For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows us to efficiently introduce both convolution and pooling operations and train the network in an end-to-end fashion without precomputed descriptors. The resultant model, VoroCNN, predicts local qualities of 3D protein folds. The prediction results are competitive to state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in recognition of protein binding interfaces. Availability and implementation The model, data and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2020
Full Text: View/download PDF

9. Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

Author: Sergei Grudinin, Vladimir Chupin, Maria Kadukova, 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), Moscow Institute of Physics and Technology [Moscow] (MIPT), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Macrocyclic Compounds, Computer science, Protein Conformation, Machine learning, computer.software_genre, Ligands, 01 natural sciences, Autodock vina, 0103 physical sciences, Drug Discovery, Humans, Physical and Theoretical Chemistry, Databases, Protein, Binding Sites, 010304 chemical physics, business.industry, Proteins, 0104 chemical sciences, Computer Science Applications, Molecular Docking Simulation, 010404 medicinal & biomolecular chemistry, Protein–ligand docking, 13. Climate action, Docking (molecular), Drug Design, Pose prediction, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Software, Protein Binding
Abstract: International audience; The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.
Published: 2020
Full Text: View/download PDF

10. Analytical symmetry detection in protein assemblies. II. Dihedral and cubic symmetries

Author: Guillaume Pagès, Sergei Grudinin, 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 ), and 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])
Subjects: 0301 basic medicine, Protein Structure, Protein Conformation, Icosahedral symmetry, Symmetry group, Dihedral angle, Crystallography, X-Ray, Topology, 03 medical and health sciences, Discrete Optimization, Structural Biology, Molecular symmetry, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Databases, Protein, Continuous Optimization, Physics, Quantitative Biology::Biomolecules, Point-Group Symmetry, Proteins, computer.file_format, Protein Data Bank, Cubic Groups, 030104 developmental biology, Group Theory, Tetrahedron, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Symmetry (geometry), computer, Algorithms, Software, Group theory
Abstract: International audience; Protein assemblies are often symmetric, as this organization has many advantages compared to individual proteins. Complex protein structures thus very often possess high-order symmetries. Detection and analysis of these symmetries has been a challenging problem and no efficient algorithms have been developed so far. This paper presents the extension of our cyclic symmetry detection method for higher-order symmetries with multiple symmetry axes. These include dihedral and cubic, i.e., tetrahedral, octahedral, and icosa-hedral, groups. Our method assesses the quality of a particular symmetry group and also determines all of its symmetry axes with a machine precision. The method comprises discrete and continuous optimization steps and is applicable to assemblies with multiple chains in the asymmetric subunits or to those with pseudo-symmetry. We implemented the method in C++ and exhaustively tested it on all 51,358 symmetrical assemblies from the Protein Data Bank (PDB). It allowed us to study structural organization of symmetrical assemblies solved by X-ray crystallography, and also to assess the symmetry annotation in the PDB. For example, in 1.6% of the cases we detected a higher symmetry group compared to the PDB annotation, and we also detected several cases with incorrect annotation. The method is available at http://team.inria.fr/nano-d/software/ananas. The graphical user interface of the method built for the SAMSON platform is available at http://samson-connect.net.
Published: 2018
Full Text: View/download PDF

11. Analytical symmetry detection in protein assemblies. I. Cyclic symmetries

Author: Guillaume Pagès, Sergei Grudinin, Elvira Kinzina, 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]), and Moscow Institute of Physics and Technology [Moscow] (MIPT)
Subjects: 0301 basic medicine, Cyclic symmetry in three dimensions, Protein Structure, Computer science, Protein Assemblies, Topology, Machine epsilon, 03 medical and health sciences, Structural Biology, Position (vector), Euclidean geometry, Molecular symmetry, Databases, Protein, Continuous Optimization, Point-Group Symmetry, Proteins, computer.file_format, Function (mathematics), Protein Data Bank, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 030104 developmental biology, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Symmetry (geometry), computer, Algorithms, Software
Abstract: International audience; Symmetry in protein, and, more generally, in macromolecular assemblies is a key point to understand their structure, stability and function. Many symmetrical assemblies are currently present in the Protein Data Bank (PDB) and some of them are among the largest solved structures, thus an efficient computational method is needed for the exhaustive analysis of these. The cyclic symmetry groups represent the most common assemblies in the PDB. These are also the building blocks for higher-order symmetries. This paper presents a mathematical formulation to find the position and the orientation of the symmetry axis in a cyclic symmetrical protein assembly, and also to assess the quality of this symmetry. Our method can also detect symmetries in partial assemblies. We provide an efficient C++ implementation of the method and demonstrate its efficiency on several examples including partial assemblies and pseudo symmetries. We also compare the method with two other published techniques and show that it is significantly faster on all the tested examples. Our method produces results with a machine precision, its cost function is solely based on 3D Euclidean geometry, and most of the operations are performed analytically. The method is available at http://team.inria.fr/nano-d/software/ananas. The graphical user interface of the method built for the SAMSON platform is available at http://samson-connect.net.
Published: 2018
Full Text: View/download PDF

12. IM-UFF: Extending the universal force field for interactive molecular modeling

Author: Léonard Jaillet, Stephane Redon, Svetlana Artemova, 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 ), and 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])
Subjects: Models, Molecular, Theoretical computer science, Molecular perception, Molecular model, Computer science, Molecular Dynamics Simulation, Time step, Molecular systems, 010402 general chemistry, Network topology, 01 natural sciences, Force field (chemistry), Computational science, Software, 0103 physical sciences, Materials Chemistry, Physical and Theoretical Chemistry, Interactive simulation, Spectroscopy, Internet, Molecular Structure, 010304 chemical physics, business.industry, Physics based, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Graphics and Computer-Aided Design, 0104 chemical sciences, Universality (dynamical systems), Empirical force fields, Universal Force Field, Interactive modeling, business
Abstract: International audience; The universal force field (UFF) is a broadly applicable classical force field that contains parameters for almost every atom type of the periodic table. This force field is non-reactive, i.e. the topology of the system under study is considered as fixed and no creation or breaking of covalent bonds is possible. This paper introduces interactive modeling-UFF (IM-UFF), an extension of UFF that combines the possibility to significantly modify molecular structures (as with reactive force fields) with a broad diversity of supported systems thanks to the universality of UFF. Such an extension lets the user easily build and edit molecular systems interactively while being guided by physics based inter-atomic forces. This approach introduces weighted atom types and weighted bonds, used to update topologies and atom parameterizations at every time step of a simulation. IM-UFF has been evaluated on a large set of benchmarks and is proposed as a self-contained implementation integrated in a new module for the SAMSON software platform for computational nanoscience available at http://www.samson-connect.net.
Published: 2017
Full Text: View/download PDF

13. Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization

Author: Sergei Grudinin, Maria Kadukova, 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]), and Moscow Institute of Physics and Technology [Moscow] (MIPT)
Subjects: 0301 basic medicine, Optimization problem, Protein Conformation, Computer science, Knowledge Bases, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Protein-ligand interactions, Ligands, computer.software_genre, Structure-Activity Relationship, 03 medical and health sciences, Polynomial basis, Knowledge-based potential, Scoring functions for docking, Software, Machine learning, Drug Discovery, Humans, Physical and Theoretical Chemistry, Databases, Protein, Binding Sites, Molecular Structure, business.industry, Regular polygon, Proteins, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Molecular Docking Simulation, 030104 developmental biology, Docking (molecular), Drug Design, Molecular docking, Convex optimization, Data mining, Scoring function, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Algorithm, computer, Algorithms, [CHIM.CHEM]Chemical Sciences/Cheminformatics, Protein Binding, Protein ligand
Abstract: We present a novel optimization approach to train a free-shape distance-dependent protein-ligand scoring function called Convex-PL. We do not impose any functional form of the scoring function. Instead, we decompose it into a polynomial basis and deduce the expansion coefficients from the structural knowledge base using a convex formulation of the optimization problem. Also, for the training set we do not generate false poses with molecular docking packages, but use constant RMSD rigid-body deformations of the ligands inside the binding pockets. This allows the obtained scoring function to be generally applicable to scoring of structural ensembles generated with different docking methods. We assess the Convex-PL scoring function using data from D3R Grand Challenge 2 submissions and the docking test of the CASF 2013 study. We demonstrate that our results outperform the other 20 methods previously assessed in CASF 2013. The method is available at http://team.inria.fr/nano-d/software/Convex-PL/ .
Published: 2017
Full Text: View/download PDF

14. As-Rigid-As-Possible molecular interpolation paths

Author: Minh Khoa Nguyen, Stephane Redon, Léonard Jaillet, 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 ), and 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])
Subjects: Models, Molecular, 0301 basic medicine, Theoretical computer science, Protein Conformation, Computer science, 02 engineering and technology, Molecular systems, Molecular conformation, Interpolation path, Computer graphics, 03 medical and health sciences, As-rigid-as-possible, Drug Discovery, Computer Graphics, 0202 electrical engineering, electronic engineering, information engineering, Animals, Humans, Computer Simulation, Polygon mesh, Physical and Theoretical Chemistry, Adaptation (computer science), Morphing, Molecular structures, Proteins, 020207 software engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, 030104 developmental biology, Large set (Ramsey theory), Algorithm, Algorithms, Software, Interpolation
Abstract: International audience; This paper proposes a new method to generate interpolation paths between two given molecular conformations. It relies on the As-Rigid-As-Possible (ARAP) paradigm used in Computer Graphics to manipulate complex meshes while preserving their essential structural characteristics. The adaptation of ARAP approaches to the case of molecular systems is presented in this contribution. Experiments conducted on a large set of benchmarks show how such a strategy can efficiently compute relevant interpolation paths with large conformational rearrangements.
Published: 2017
Full Text: View/download PDF

15. Interdomain Flexibility within NADPH Oxidase Suggested by SANS Using LMNG Stealth Carrier

Author: Annelise Vermot, Anne L. Martel, Corinne Vivès, Sergei Grudinin, Michael Härtlein, Aline Le Roy, Michel Thépaut, Isabelle Petit-Hartlein, Christine Ebel, Martine Moulin, Susan M.E. Smith, Cécile Breyton, Franck Fieschi, Institut de biologie structurale (IBS - UMR 5075), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Sciences et Société, Historicité, Éducation et Pratiques (EA S2HEP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institut Laue-Langevin (ILL), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Molecular and Cellular Biology [Kennesaw], Kennesaw State University (KSU), ANR-17-CE11-0013,BANDIT,d'une Nox bactérienne à la conception d'outil de criblage(2017), ANR-16-CE92-0001,FLUOR,Tensioactifs Fluorés pour l'Étude de Protéines Membranaires(2016), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), ILL, 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)-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 ), and Université Grenoble Alpes (UGA)
Subjects: 0303 health sciences, Molecular model, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], Biophysics, Membrane Proteins, NADPH Oxidases, Dehydrogenase, Articles, Neopentyl glycol, Small-angle neutron scattering, Transmembrane protein, 03 medical and health sciences, chemistry.chemical_compound, Crystallography, Neutron Diffraction, 0302 clinical medicine, chemistry, Docking (molecular), Scattering, Small Angle, Linker, Integral membrane protein, Oxidation-Reduction, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: International audience; Small angle neutron scattering (SANS) provides a method to obtain important low-resolution information for integral membrane proteins (IMPs), challenging targets for structural determination. Specific deuteration furnishes a "stealth" carrier for the solubilized IMP. We used SANS to determine a structural envelope of SpNOX, the Streptococcus pneumoniae NADPH oxidase (NOX), a prokaryotic model system for exploring structure and function of eukaryotic NOXes. SpNOX was solubilized in the detergent lauryl maltose neopentyl glycol, which provides optimal SpNOX stability and activity. Using deuterated solvent and protein, the lauryl maltose neopentyl glycol was experimentally undetected in SANS. This affords a cost-effective SANS approach for obtaining novel structural information on IMPs. Combining SANS data with molecular modeling provided a first, to our knowledge, structural characterization of an entire NOX enzyme. It revealed a distinctly less compact structure than that predicted from the docking of homologous crystal structures of the separate transmembrane and dehydrogenase domains, consistent with a flexible linker connecting the two domains.
Published: 2020
Full Text: View/download PDF

16. AnAnaS: Software for Analytical Analysis of Symmetries in Protein Structures

Author: Sergei Grudinin, Guillaume Pagès, Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), 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)-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 ), and Université Grenoble Alpes (UGA)
Subjects: Quaternion arithmetic, Physics, Quantitative Biology::Biomolecules, 0303 health sciences, Pure mathematics, Icosahedral symmetry, Dihedral angle, Symmetry group, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 010402 general chemistry, 01 natural sciences, Protein Assembly, Symmetry axes detection, Protein Symmetry, 0104 chemical sciences, 03 medical and health sciences, Protein structure, Octahedron, Continuous optimization, Homogeneous space, Tetrahedron, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Symmetry (geometry), 030304 developmental biology
Abstract: International audience; Symmetry is very common among proteins found in structural databases such as the PDB. We present novel software, called AnAnaS, that finds positions and orientations of the symmetry axes in all types of symmetrical protein assemblies. It deals with five symmetry groups, cyclic, dihedral, tetrahedral, octahedral, and icosahedral. The software also assesses the quality of symmetry and can detect symmetries in incomplete cyclic assemblies. Internally, AnAnaS comprises discrete and continuous optimization steps and is applicable to assemblies with multiple chains in the asymmetric subunits or to those with pseudo-symmetry. The method is very fast, as most of the steps are performed analytically.
Published: 2020
Full Text: View/download PDF

17. Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Author: Larsen, Andreas Haahr, Wang, Yong, Bottaro, Sandro, Grudinin, Sergei, Arleth, Lise, Lindorff-Larsen, Kresten, Niels Bohr Institute [Copenhagen] (NBI), Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Structural Bioinformatics and Computational Biochemistry [Oxford] (SBCB), University of Oxford, Linderstrøm-Lang Centre for Protein Science [Copenhagen], IT University of Copenhagen (ITU), Università degli studi di Genova = University of Genoa (UniGe), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), University of Oxford [Oxford], IT University of Copenhagen, Universita degli studi di Genova, 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)-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 ), and Université Grenoble Alpes (UGA)
Subjects: 0301 basic medicine, Protein Conformation, Entropy, Neutron scattering, Biochemistry, 01 natural sciences, Small-Angle Scattering, Scattering, Molecular dynamics, 0302 clinical medicine, X-Ray Diffraction, Biochemical Simulations, Macromolecular Structure Analysis, Statistical physics, Biology (General), Physics, Quantitative Biology::Biomolecules, 0303 health sciences, Ecology, 010304 chemical physics, Small-angle X-ray scattering, Simulation and Modeling, Principle of maximum entropy, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Computational Theory and Mathematics, Modeling and Simulation, Physical Sciences, Engineering and Technology, Thermodynamics, Small-angle scattering, Algorithms, Research Article, Protein Structure, Biophysical Simulations, QH301-705.5, Biophysics, Molecular Dynamics Simulation, Research and Analysis Methods, Cellular and Molecular Neuroscience, 03 medical and health sciences, Protein Domains, Robustness (computer science), Scattering, Small Angle, 0103 physical sciences, Genetics, Neutron, Protein Interactions, Signal to Noise Ratio, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Neutrons, Biology and Life Sciences, Computational Biology, Proteins, Function (mathematics), 030104 developmental biology, Structural biology, Signal Processing, 030217 neurology & neurosurgery
Abstract: Many proteins contain multiple folded domains separated by flexible linkers, and the ability to describe the structure and conformational heterogeneity of such flexible systems pushes the limits of structural biology. Using the three-domain protein TIA-1 as an example, we here combine coarse-grained molecular dynamics simulations with previously measured small-angle scattering data to study the conformation of TIA-1 in solution. We show that while the coarse-grained potential (Martini) in itself leads to too compact conformations, increasing the strength of protein-water interactions results in ensembles that are in very good agreement with experiments. We show how these ensembles can be refined further using a Bayesian/Maximum Entropy approach, and examine the robustness to errors in the energy function. In particular we find that as long as the initial simulation is relatively good, reweighting against experiments is very robust. We also study the relative information in X-ray and neutron scattering experiments and find that refining against the SAXS experiments leads to improvement in the SANS data. Our results suggest a general strategy for studying the conformation of multi-domain proteins in solution that combines coarse-grained simulations with small-angle X-ray scattering data that are generally most easy to obtain. These results may in turn be used to design further small-angle neutron scattering experiments that exploit contrast variation through 1H/2H isotope substitutions., Author summary Many proteins contain multiple folded domains separated by flexible linkers, and in order to understand how such multi-domain proteins function, we need to be able to describe how these domains are oriented in space. We have used the three-domain protein TIA-1 as an example to combine molecular simulations with biophysical experiments to describe the structural and dynamical properties of a multi-domain protein. We show that while standard simulations do not lead to good agreement with the experimental data, we can improve the agreement substantially by tuning a single parameter in the model that describes the interaction between protein and water. We can gain further information about the system by a more direct integration of the data, and we find that we can provide a detailed and robust description of the relative location of the different domains in TIA-1. The method is general and will be useful to study the relationship between structure, dynamics and function in multi-domain proteins in other systems.
Published: 2019
Full Text: View/download PDF

18. DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures

Author: Sergei Grudinin, Guillaume Pagès, Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), 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 ), and 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])
Subjects: FOS: Computer and information sciences, Statistics and Probability, Computer Science - Machine Learning, Computer science, Protein Data Bank (RCSB PDB), FOS: Physical sciences, Biochemistry, Quantitative Biology - Quantitative Methods, Machine Learning (cs.LG), 03 medical and health sciences, Protein structure, Tandem repeat, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Physics - Biological Physics, Molecular Biology, Quantitative Methods (q-bio.QM), 030304 developmental biology, 0303 health sciences, Basis (linear algebra), 030302 biochemistry & molecular biology, Proteins, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Structural biology, Biological Physics (physics.bio-ph), Tandem Repeat Sequences, FOS: Biological sciences, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Symmetry (geometry), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithm, Algorithms, Software
Abstract: Motivation Thanks to the recent advances in structural biology, nowadays 3D structures of various proteins are solved on a routine basis. A large portion of these structures contain structural repetitions or internal symmetries. To understand the evolution mechanisms of these proteins and how structural repetitions affect the protein function, we need to be able to detect such proteins very robustly. As deep learning is particularly suited to deal with spatially organized data, we applied it to the detection of proteins with structural repetitions. Results We present DeepSymmetry, a versatile method based on 3D convolutional networks that detects structural repetitions in proteins and their density maps. Our method is designed to identify tandem repeat proteins, proteins with internal symmetries, symmetries in the raw density maps, their symmetry order and also the corresponding symmetry axes. Detection of symmetry axes is based on learning 6D Veronese mappings of 3D vectors, and the median angular error of axis determination is less than one degree. We demonstrate the capabilities of our method on benchmarks with tandem-repeated proteins and also with symmetrical assemblies. For example, we have discovered about 7800 putative tandem repeat proteins in the PDB. Availability and implementation The method is available at https://team.inria.fr/nano-d/software/deepsymmetry. It consists of a C++ executable that transforms molecular structures into volumetric density maps, and a Python code based on the TensorFlow framework for applying the DeepSymmetry model to these maps. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2019
Full Text: View/download PDF

19. Small angle X‐ray scattering‐assisted protein structure prediction in CASP13 and emergence of solution structure differences

Author: Sergei Grudinin, Jose M. Duarte, Curtis D. Hodge, Dmytro Guzenko, John A. Tainer, Bohdan Monastyrskyy, Daniel J. Rosenberg, Susan E. Tsutakawa, Greg L. Hura, Krzysztof Fidelis, Andriy Kryshtafovych, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Department of Chemistry and Biochemistry [Santa Cruz], University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC), San Diego Supercomputer Center [San Diego], Genome Center [UC Davis], University of California [Davis] (UC Davis), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of California [Santa Cruz] (UCSC), University of California-University of California, 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 ), and 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])
Subjects: Models, Molecular, Small Angle, Protein Folding, experimental restraints, Domain level, Protein Conformation, Bioinformatics, Bioengineering, Biochemistry, Article, Mathematical Sciences, Scattering, [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], 03 medical and health sciences, X-Ray Diffraction, Structural Biology, Models, Information and Computing Sciences, Scattering, Small Angle, [CHIM.CRIS]Chemical Sciences/Cristallography, unstructured regions, CASP, Molecular Biology, 030304 developmental biology, 0303 health sciences, complexes, Small-angle X-ray scattering, 030302 biochemistry & molecular biology, Proteins, Computational Biology, Molecular, modeling, SAXS, disorder, Protein structure prediction, Biological Sciences, solution scattering, Solution structure, structure prediction, Solutions, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Prediction algorithms, flexibility, Data quality, SAS, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithm, Predictive modelling, Algorithms
Abstract: International audience; Small angle X‐ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS‐assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography‐SAXS (SEC‐SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS‐assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology.
Published: 2019
Full Text: View/download PDF

20. KAP1 is an antiparallel dimer with a functional asymmetry

Author: Louise C. Bryan, Pierre-Yves Helleboid, Matteo Dal Peraro, Giulia Fonti, Davide Demurtas, Didier Trono, Beat Fierz, Mark D. Tully, Alexandra Styliani Kalantzi, Sergei Grudinin, Sylvain Träger, Maria J. Marcaida, Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), European Synchrotron Radiation Facility (ESRF), 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]), School of Life Sciences [Lausanne], Université de Lausanne = University of Lausanne (UNIL), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Transcription, Genetic, Health, Toxicology and Mutagenesis, SUMO protein, Plant Science, Tripartite Motif-Containing Protein 28, small-angle scattering, 0302 clinical medicine, Heterochromatin, phd finger, Promoter Regions, Genetic, hp1 proteins, Research Articles, Coiled coil, 0303 health sciences, Ecology, biology, Chemistry, Chromatin, 3. Good health, Ubiquitin ligase, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, transcriptional corepressor tif1-beta, Research Article, zinc-finger proteins, Antiparallel (biochemistry), Biochemistry, Genetics and Molecular Biology (miscellaneous), tripartite motif, structural insights, Cell Line, 03 medical and health sciences, coiled-coil, Humans, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], Transcription factor, 030304 developmental biology, Binding Sites, trim family, Sumoylation, e3 ligase, PHD finger, biology.protein, Biophysics, Heterochromatin protein 1, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery, Transcription Factors
Abstract: This study reveals the architecture of human KAP1 by integrating molecular modeling with small-angle X-ray scattering and single-molecule experiments. KAP1 dimers feature a structural asymmetry at the C-terminal domains that has functional implications for recruitment of HP1., KAP1 (KRAB domain–associated protein 1) plays a fundamental role in regulating gene expression in mammalian cells by recruiting different transcription factors and altering the chromatin state. In doing so, KAP1 acts both as a platform for macromolecular interactions and as an E3 small ubiquitin modifier ligase. This work sheds light on the overall organization of the full-length protein combining solution scattering data, integrative modeling, and single-molecule experiments. We show that KAP1 is an elongated antiparallel dimer with an asymmetry at the C-terminal domains. This conformation is consistent with the finding that the Really Interesting New Gene (RING) domain contributes to KAP1 auto-SUMOylation. Importantly, this intrinsic asymmetry has key functional implications for the KAP1 network of interactions, as the heterochromatin protein 1 (HP1) occupies only one of the two putative HP1 binding sites on the KAP1 dimer, resulting in an unexpected stoichiometry, even in the context of chromatin fibers.
Published: 2019
Full Text: View/download PDF

21. Assessment of chemical-crosslink-assisted protein structure modeling in CASP13

Author: Bohdan Monastyrskyy, Juri Rappsilber, Krzysztof Fidelis, J. Eduardo Fajardo, Mikhail Karasikov, Alexander Leitner, Agnieszka S. Karczyńska, Andras Fiser, Adam Belsom, Silvia Crivelli, Emilia A. Lubecka, Celina Sikorska, Andriy Kryshtafovych, Nelson Gil, Cezary Czaplewski, Rojan Shrestha, Esben Trabjerg, Sergei Grudinin, Guillaume Pagès, Adam Liwo, Adam K. Sieradzan, Department of Systems & Computational Biology [New York], Albert Einstein College of Medicine [New York], Department of Biochemistry [New York], Institut für Biotechnologie [berlin], Technische Universität Berlin (TU), Department of Computer Science [Davis] (UC Davis), University of California [Davis] (UC Davis), University of California-University of California, Department of Environmental Analytics [Univ Gdańsk], Faculty of Chemistry [Univ Gdańsk], University of Gdańsk (UG)-University of Gdańsk (UG), Genome Center [UC Davis], 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]), Moscow Institute of Physics and Technology [Moscow] (MIPT), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Department of Computer Science [ETH Zürich] (D-INFK), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Institute of Molecular Systems Biology [Zurich], Korea Institute for Advanced Study (KIAS), Institute of Mathematics [Univ Gdańsk], University of Gdańsk (UG), Wellcome Trust Centre for Cell Biology, University of Edinburgh, Technical University of Berlin / Technische Universität Berlin (TU), Department of Computer Science [Univ California Davis] (CS - UC Davis), University of California (UC)-University of California (UC), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: CASP13, Models, Molecular, Computer science, Protein Conformation, Context (language use), Computational biology, Routine practice, 010402 general chemistry, Mass spectrometry, Machine learning, computer.software_genre, 01 natural sciences, Biochemistry, Article, [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], 03 medical and health sciences, Protein structure, chemical-crosslink-assisted protein structure modeling, Structural Biology, Tandem Mass Spectrometry, Molecular Biology, 030304 developmental biology, 0303 health sciences, Chemistry, business.industry, 030302 biochemistry & molecular biology, Model protein, Computational Biology, Proteins, Reproducibility of Results, 0104 chemical sciences, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Workflow, Cross-Linking Reagents, Models, Chemical, chemical crosslinking/mass spectrometry, Critical assessment, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Protein structure modeling, computer, Function (biology), Algorithms, Chromatography, Liquid
Abstract: International audience; With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest‐to‐date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.
Published: 2019
Full Text: View/download PDF

22. PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation

Author: Petr Popov, David W. Ritchie, Emilie Neveu, Sergei Grudinin, 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]), 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), and Moscow Institute of Physics and Technology [Moscow] (MIPT)
Subjects: 0301 basic medicine, Statistics and Probability, Binding free energy, Protein Conformation, Computer science, Molecular Conformation, Docking proteines, Machine learning, computer.software_genre, Biochemistry, Molecular Docking Simulation, Molecular conformation, Protein–protein interaction, Machine Learning, 03 medical and health sciences, Protein structure, DOCK, Native state, Macromolecular docking, Protein Interactions, Molecular Biology, business.industry, Proteins, Models, Theoretical, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Docking (molecular), Artificial intelligence, business, computer, Algorithm, Algorithms, Protein Binding
Abstract: Motivation Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. Results First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein–protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5–15 min on a modern laptop and can easily be extended to other types of interactions. Availability and Implementation https://team.inria.fr/nano-d/software/PEPSI-Dock. Contact sergei.grudinin@inria.fr
Published: 2016
Full Text: View/download PDF

23. Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates

Author: Sergei Grudinin, Maria Kadukova, 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]), and Moscow Institute of Physics and Technology [Moscow] (MIPT)
Subjects: 0301 basic medicine, Informatics, Support Vector Machine, Computer science, protein ligand docking, SVM, General Chemical Engineering, Library and Information Sciences, Ligands, computer.software_genre, 01 natural sciences, Set (abstract data type), Automation, 03 medical and health sciences, Software, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 3D structure perception, 0103 physical sciences, Organic Chemicals, 010304 chemical physics, business.industry, Atom (order theory), General Chemistry, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, Molecular Weight, Data set, Support vector machine, machine learning, 030104 developmental biology, Protein–ligand docking, Benchmark (computing), Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Algorithm, computer, Algorithms
Abstract: International audience; Here we address the problem of the assignment of atom types and bond orders in low molecular weight compounds. For this purpose, we have developed a prediction model based on nonlinear Support Vector Machines (SVM), implemented in a KNOwledge-Driven Ligand Extractor called Knodle, a software library for the recognition of atomic types, hybridization states, and bond orders in the structures of small molecules. We trained the model using an excessive amount of structural data collected from the PDBbindCN database. Accuracy of the results and the running time of our method is comparable with other popular methods, such as NAOMI, fconv, and I-interpret. On the popular Labute’s benchmark set consisting of 179 protein–ligand complexes, Knodle makes five to six perception errors, NAOMI makes seven errors, I-interpret makes nine errors, and fconv makes 13 errors. On a larger set of 3,000 protein–ligand structures collected from the PDBBindCN general data set (v2014), Knodle and NAOMI have a comparable accuracy of approximately 3.9% and 4.7% of errors, I-interpret made 6.0% of errors, while fconv produced approximately 12.8% of errors. On a more general set of 332,974 entries collected from the Ligand Expo database, Knodle made 4.5% of errors. Overall, our study demonstrates the efficiency and robustness of nonlinear SVM in structure perception tasks. Knodle is available at https://team.inria.fr/nano-d/software/Knodle.
Published: 2016
Full Text: View/download PDF

24. Error Analysis of Modified Langevin Dynamics

Author: Stephane Redon, Gabriel Stoltz, Zofia Trstanova, 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]), Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), École des Ponts ParisTech (ENPC), MATHematics for MatERIALS (MATHERIALS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC), ANR-14-CE23-0012,COSMOS,Statistique numérique et simulation moléculaire(2014), École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC)-Inria de Paris, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Physics, Work (thermodynamics), Statistical Mechanics (cond-mat.stat-mech), Monte Carlo method, Ergodicity, FOS: Physical sciences, Statistical and Nonlinear Physics, Numerical Analysis (math.NA), 010103 numerical & computational mathematics, 01 natural sciences, Hypoelliptic operator, 0103 physical sciences, FOS: Mathematics, Ergodic theory, Variance reduction, Mathematics - Numerical Analysis, Statistical physics, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], 0101 mathematics, 010306 general physics, Galerkin method, Langevin dynamics, Condensed Matter - Statistical Mechanics, Mathematical Physics
Abstract: International audience; We consider Langevin dynamics associated with a modified kinetic energy vanishing for small momenta. This allows us to freeze slow particles, and hence avoid the re-computation of inter-particle forces, which leads to computational gains. On the other hand, the statistical error may increase since there are a priori more correlations in time. The aim of this work is first to prove the ergodicity of the modified Langevin dynamics (which fails to be hypoelliptic), and next to analyze how the asymptotic variance on ergodic averages depends on the parameters of the modified kinetic energy. Numerical results illustrate the approach, both for low-dimensional systems where we resort to a Galerkin approximation of the generator, and for more realistic systems using Monte Carlo simulations.
Published: 2016
Full Text: View/download PDF

25. Incremental solver for orbital-free density functional theory

Author: François Rousse, Stephane Redon, Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), 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 ), and 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])
Subjects: Particle system, 010304 chemical physics, Computer science, Orbital-free density functional theory, Adaptively Restrained Particle Simulations, Context (language use), General Chemistry, Solver, 010402 general chemistry, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 01 natural sciences, Incremental algorithm, 0104 chemical sciences, Orbital-Free DFT, Computational Mathematics, 0103 physical sciences, Particle, Density functional theory, Ab-initio molecular dynamic, Statistical physics, Real-Space Finite-Differences
Abstract: First-principle calculations are still a challenge since they require a great amount of computational time. In this article, we introduce a new algorithm to perform orbital-free density functional theory (OF-DFT) calculations. Our new algorithm focuses computational efforts on important parts of the particle system, which, in the context of adaptively restrained particle simulations (ARPS) allows us to accelerate particle simulations. © 2019 Wiley Periodicals, Inc.
Published: 2019
Full Text: View/download PDF

26. Deep convolutional networks for quality assessment of protein folds

Author: Sergei Grudinin, Yoshua Bengio, Guillaume Lamoureux, Georgy Derevyanko, Concordia University [Montreal], 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]), Département d'Informatique et de Recherche Opérationnelle [Montreal] (DIRO), Université de Montréal (UdeM), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0301 basic medicine, Statistics and Probability, Protein Folding, Computer science, Machine learning, computer.software_genre, Biochemistry, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, Protein structure, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Code (cryptography), Feature (machine learning), Molecular Biology, Native structure, Structure (mathematical logic), Sequence, 030102 biochemistry & molecular biology, Artificial neural network, Basis (linear algebra), business.industry, Rank (computer programming), A protein, Computational Biology, Proteins, Biomolecules (q-bio.BM), Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Quantitative Biology - Biomolecules, FOS: Biological sciences, Artificial intelligence, Neural Networks, Computer, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Algorithms
Abstract: The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models. Most assessment methods rank candidate models using heavily engineered structural features, defined as complex functions of the atomic coordinates. However, very few methods have attempted to learn these features directly from the data. We show that deep convolutional networks can be used to predict the ranking of model structures solely on the basis of their raw three-dimensional atomic densities, without any feature tuning. We develop a deep neural network that performs on par with state-of-the-art algorithms from the literature. The network is trained on decoys from the CASP7 to CASP10 datasets and its performance is tested on the CASP11 dataset. On the CASP11 stage 2 dataset, it achieves a loss of 0.064, whereas the best performing method achieves a loss of 0.063. Additional testing on decoys from the CASP12, CAMEO, and 3DRobot datasets confirms that the network performs consistently well across a variety of protein structures. While the network learns to assess structural decoys globally and does not rely on any predefined features, it can be analyzed to show that it implicitly identifies regions that deviate from the native structure., 8 pages
Published: 2018
Full Text: View/download PDF

27. Protein model quality assessment using 3D oriented convolutional neural networks

Author: Benoit Charmettant, Guillaume Pagès, Sergei Grudinin, 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]), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Statistics and Probability, Computer science, 0206 medical engineering, 02 engineering and technology, Machine learning, computer.software_genre, Biochemistry, Convolutional neural network, Machine Learning, 03 medical and health sciences, Structural bioinformatics, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, CASP, Molecular Biology, 030304 developmental biology, 0303 health sciences, Artificial neural network, business.industry, Quality assessment, 030302 biochemistry & molecular biology, Proteins, Pattern recognition, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Protein model, Artificial intelligence, Neural Networks, Computer, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 020602 bioinformatics
Abstract: Motivation Protein model quality assessment (QA) is a crucial and yet open problem in structural bioinformatics. The current best methods for single-model QA typically combine results from different approaches, each based on different input features constructed by experts in the field. Then, the prediction model is trained using a machine-learning algorithm. Recently, with the development of convolutional neural networks (CNN), the training paradigm has changed. In computer vision, the expert-developed features have been significantly overpassed by automatically trained convolutional filters. This motivated us to apply a three-dimensional (3D) CNN to the problem of protein model QA. Results We developed Ornate (Oriented Routed Neural network with Automatic Typing)—a novel method for single-model QA. Ornate is a residue-wise scoring function that takes as input 3D density maps. It predicts the local (residue-wise) and the global model quality through a deep 3D CNN. Specifically, Ornate aligns the input density map, corresponding to each residue and its neighborhood, with the backbone topology of this residue. This circumvents the problem of ambiguous orientations of the initial models. Also, Ornate includes automatic identification of atom types and dynamic routing of the data in the network. Established benchmarks (CASP 11 and CASP 12) demonstrate the state-of-the-art performance of our approach among single-model QA methods. Availability and implementation The method is available at https://team.inria.fr/nano-d/software/Ornate/. It consists of a C++ executable that transforms molecular structures into volumetric density maps, and a Python code based on the TensorFlow framework for applying the Ornate model to these maps. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2018
Full Text: View/download PDF

28. Generating conformational transition paths with low potential-energy barriers for proteins

Author: Léonard Jaillet, Stephane Redon, Minh Khoa Nguyen, 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 ), and 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])
Subjects: 0301 basic medicine, Physics, Quantitative Biology::Biomolecules, Protein Conformation, Proteins, Rigidity (psychology), Topology, Collision, Potential energy, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, 03 medical and health sciences, 030104 developmental biology, Models, Chemical, Drug Discovery, Path (graph theory), Thermodynamics, Physical and Theoretical Chemistry, Energy (signal processing), Algorithms, Interpolation
Abstract: International audience; The knowledge of conformational transition paths in proteins can be useful for understanding protein mechanisms. Recently, we have introduced the As-Rigid-As-Possible (ARAP) interpolation method, for generating interpolation paths between two protein conformations. The method was shown to preserve well the rigidity of the initial conformation along the path. However, because the method is totally geometry-based, the generated paths may be inconsistent because the atom interactions are ignored. Therefore, in this article, we would like to introduce a new method to generate conformational transition paths with low potential-energy barriers for proteins. The method is composed of three processing stages. First, ARAP interpolation is used for generating an initial path. Then, the path conformations are enhanced by a clash remover. Finally, Nudged Elastic Band, a path-optimization method, is used to produce a low-energy path. Large energy reductions are found in the paths obtained from the method than in those obtained from the ARAP interpolation method alone. The results also show that ARAP interpolation is a good candidate for generating an initial path because it leads to lower potential-energy paths than two other common methods for path interpolation.
Published: 2018
Full Text: View/download PDF

29. Atomistic Modelling and Simulation of Transmission Electron Microscopy Images: Application to Intrinsic Defects of Graphene

Author: François Rousse, Cyril Guedj, Stephane Redon, Léonard Jaillet, Département Plate-Forme Technologique (DPFT), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), 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 ), and 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])
Subjects: Materials science, business.industry, Graphene, 02 engineering and technology, [CHIM.MATE]Chemical Sciences/Material chemistry, 021001 nanoscience & nanotechnology, Microstructure, Radial distribution function, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Characterization (materials science), law.invention, Software, law, 0103 physical sciences, Scanning transmission electron microscopy, Range (statistics), [PHYS.COND.CM-MS]Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci], Optoelectronics, 010306 general physics, 0210 nano-technology, business, Energy (signal processing), ComputingMilieux_MISCELLANEOUS
Abstract: The characterization of advanced materials and devices in the nanometer range requires complex tools to understand the precise links between structure and properties. This paper demonstrates that the modelling of graphene-based defects can be obtained efficiently for various atomic arrangements using the Brenner module of the SAMSON software platform. The signatures of all kinds of defects are computed in terms of energy and simulated scanning transmission electron microscopy images. The results are in good agreement with the majority of the available theoretical and experimental data. This original methodology is an excellent compromise between the speed and the precision required by the semiconductor industry and opens the possibility of realistic in-silico research conjugated to the experimental nanocharacterization of these promising materials. We propose a novel approach to compare the agreement between experiment and simulation by using the projected radial distribution function. The maximum projected Euclidian distance between the model and the experiment is always better than 100 pm.
Published: 2018
Full Text: View/download PDF

30. Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

Author: Sergei Grudinin, Guillaume Pagès, Mikhail Karasikov, Moscow Institute of Physics and Technology [Moscow] (MIPT), 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]), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Statistics and Probability, Models, Molecular, 0303 health sciences, Computer science, Hydrogen bond, Protein Conformation, 030302 biochemistry & molecular biology, Protein design, Atomic coordinates, Function (mathematics), Protein structure prediction, Biochemistry, Computer Science Applications, 03 medical and health sciences, Computational Mathematics, Structural bioinformatics, Protein structure, Computational Theory and Mathematics, Grain Proteins, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Molecular Biology, Algorithm, 030304 developmental biology
Abstract: Motivation Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models. Results We present a novel single-model QA method called SBROD. The SBROD (Smooth Backbone-Reliant Orientation-Dependent) method uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models. The proposed method deduces its scoring function from a training set of protein models. The SBROD scoring function is composed of four terms related to different structural features: residue–residue orientations, contacts between backbone atoms, hydrogen bonding and solvent–solute interactions. It is smooth with respect to atomic coordinates and thus is potentially applicable to continuous gradient-based optimization of protein conformations. Furthermore, it can also be used for coarse-grained protein modeling and computational protein design. SBROD proved to achieve similar performance to state-of-the-art single-model QA methods on diverse datasets (CASP11, CASP12 and MOULDER). Availability and implementation The standalone application implemented in C++ and Python is freely available at https://gitlab.inria.fr/grudinin/sbrod and supported on Linux, MacOS and Windows. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2018
Full Text: View/download PDF

31. ART-RRT: As-Rigid-As-Possible exploration of ligand unbinding pathways

Author: Léonard Jaillet, Stephane Redon, Minh Khoa Nguyen, 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 ), and 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])
Subjects: 0301 basic medicine, Computer science, dimension reduction, Dimensionality reduction, ART-RRT, General Chemistry, motion planning, morphing, 010402 general chemistry, Ligand (biochemistry), 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 0104 chemical sciences, Reaction coordinate, Computer graphics, 03 medical and health sciences, Computational Mathematics, Tree (data structure), Morphing, 030104 developmental biology, Motion planning, Algorithm, ligand unbinding pathway
Abstract: This article proposes a method to efficiently generate approximate ligand unbinding pathways. It combines an efficient tree-based exploration method with a morphing technique from Computer Graphics for dimensionality reduction. This method is computationally cheap and, unlike many existing approaches, does not require a reaction coordinate to guide the search. It can be used for finding pathways with known or unknown directions beforehand. The approach is evaluated on several benchmarks and the obtained solutions are compared with the results from other state-of-the-art approaches. We show that the method is time-efficient and produces pathways in good agreement with other state-of-the-art solutions. These paths can serve as first approximations that can be used, analyzed, or improved with more specialized methods. © 2018 Wiley Periodicals, Inc.
Published: 2018
Full Text: View/download PDF

32. Eurecon: Equidistant Uniform Rigid-body Ensemble Constructor

Author: Petr Popov, Sergei Grudinin, Moscow Institute of Physics and Technology [Moscow] (MIPT), 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 ), and 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])
Subjects: 0301 basic medicine, Computation, Molecular Conformation, Molecular Dynamics Simulation, 010402 general chemistry, Translation (geometry), [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 01 natural sciences, 03 medical and health sciences, Materials Chemistry, Partition (number theory), Equidistant, Statistical physics, Physical and Theoretical Chemistry, Root-mean-square deviation, Conformational ensembles, Spectroscopy, Physics, Quantitative Biology::Biomolecules, Proteins, DNA, Rigid body, Computer Graphics and Computer-Aided Design, 0104 chemical sciences, 030104 developmental biology, Protein Multimerization, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Rotation (mathematics), Algorithms
Abstract: International audience; Conformational ensembles comprise one of the fundamental concepts in statistical bioinformatics and appear in a variety of applications, e.g. molecular docking, virtual screening, searching for pharmacophores, etc. High-throughput applications require billions of conformations to be considered, thus, one often uses the rigid-body representation of molecules or its fragments to cope with the computational cost. Of particular interest is generation of the near-native conformational ensembles, which consist of conformations structurally close to the biologically relevant ones. One possible way to compose such ensembles is to control the root mean square deviation (RMSD) between the original and the generated conformations. To the best of our knowledge there is no computational approach that guarantees that all the generated conformations have the desired RMSD with respect to the reference structure. In this study we presented a fast algorithm for the construction of rigid-body conformational ensembles, which possess two main properties: (i) each generated conformation has a fixed RMSD with respect to the original conformation, (ii) generated conformations are distributed uniformly over the sphere of axes corresponding to the rigid-body motions. The algorithm is very efficient, it does not require any standard RMSD computation between the conformations and has the O(N + M) complexity to generate the required rigid-body transforms, where N is the number of atoms in the system, and M is the size of the conformational ensemble. Eurecon is applicable to an arbitrary atomic system, thus, it could be used for molecular systems of various size and type. We demonstrated Eurecon application by generating near-native conformational ensembles for a ligand placed inside a binding site, a protein dimer embedded into a membrane, and a ribosomal complex. We implemented the developed algorithm in C++ and called it Eurecon, which stands for Equidistant Uniform Rigid-body Ensemble CONstructor. A user-friendly interface allows to define the desired RMSD value, the relative amplitudes for rotation and translation motions by means of the partition parameter, and the set of axes corresponding to the rigid-body motions. Eurecon is available as the SAMSON Element (https://samson-connect.net).
Published: 2018
Full Text: View/download PDF

33. Incremental update of electrostatic interactions in adaptively restrained particle simulations

Author: Stephane Redon, Semeho Prince A. Edorh, 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 ), and 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])
Subjects: Molecular Simulations, 010304 chemical physics, Computational complexity theory, Computer science, Computation, General Chemistry, Multigrid, 01 natural sciences, P3M, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computational science, Computational Mathematics, Molecular dynamics, Multigrid method, Electrostatics, Particle Mesh, 0103 physical sciences, Particle, Central processing unit, Adaptively Restrained Molecular Dynamics, 010306 general physics, LAMMPS
Abstract: International audience; The computation of long-range potentials is one of the demanding tasks in Molecular Dynamics. During the last decades, an inventive panoply of methods was developed in order to reduce the CPU time of this task. In this work, we propose a fast method dedicated to the computation of the elec-trostatic potential in adaptively restrained systems. We exploit the fact that, in such systems, only some particles are allowed to move at each timestep. We developed an incremental algorithm derived from a multigrid-based alternative to traditional Fourier-based methods. Our algorithm was implemented inside LAMMPS, a popular molecular dynamics simulation package. We evaluated the method on different systems. We showed that the new algorithm's computational complexity scales with the number of active particles in the simulated system, and is able to outperform the well-established Particle Particle Particle Mesh (P3M) for adaptively restrained simulations.
Published: 2017
Full Text: View/download PDF

34. RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules

Author: Grégoire Danoy, Petr Popov, Angelo Migliosi, Emilie Neveu, Pascal Bouvry, Xavier Besseron, Sergei Grudinin, Alexandre Hoffmann, 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]), Université de Lausanne = University of Lausanne (UNIL), Moscow Institute of Physics and Technology [Moscow] (MIPT), Computer Science and Communications Research Unit [Luxembourg] (CSC), Laboratory of Advanced Software SYstems [Luxembourg] (LASSY), Université du Luxembourg (Uni.lu)-Université du Luxembourg (Uni.lu), and Université de Lausanne (UNIL)
Subjects: 0301 basic medicine, Statistics and Probability, Computer science, Protein Conformation, Initialization, Molecular Dynamics Simulation, 01 natural sciences, Biochemistry, Molecular conformation, 03 medical and health sciences, Molecular dynamics, Motion, Protein structure, 0103 physical sciences, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Cluster analysis, Pliability, Molecular Biology, Quantitative Biology::Biomolecules, Principal Component Analysis, 010304 chemical physics, Computational Biology, Proteins, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Structural biology, Docking (molecular), Principal component analysis, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithm, Algorithms, Software
Abstract: Motivation The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis. Results The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking. Availability and implementation We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2017
Full Text: View/download PDF

35. A novel fast Fourier transform accelerated off-grid exhaustive search method for cryo-electron microscopy fitting

Author: Sergei Grudinin, Alexandre Hoffmann, Valérie Perrier, 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]), Calcul des Variations, Géométrie, Image (CVGI ), Laboratoire Jean Kuntzmann (LJK ), and 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])-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])
Subjects: 0301 basic medicine, exhaustive search, Computer science, Computation, Fast Fourier transform, Prime-factor FFT algorithm, Brute-force search, Grid, fast Fourier transform, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, General Biochemistry, Genetics and Molecular Biology, FFT, 03 medical and health sciences, 030104 developmental biology, Quadratic equation, Split-radix FFT algorithm, Rader's FFT algorithm, trust-region problem, [CHIM.CRIS]Chemical Sciences/Cristallography, cryo-EM fitting, off-grid search, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], Algorithm
Abstract: This paper presents a novel fast Fourier transform (FFT)-based exhaustive search method extended to off-grid translational and rotational degrees of freedom. The method combines the advantages of the FFT-based exhaustive search, which samples all the conformations of a system under study on a grid, with a local optimization technique that guarantees to find the nearest optimal off-grid conformation. The method is demonstrated on a fitting problem and can be readily applied to a docking problem. The algorithm first samples a scoring function on a six-dimensional grid of sizeN6using the FFT. This operation has an asymptotic complexity ofO(N6logN). Then, the method performs the off-grid search using a local quadratic approximation of the cost function and the trust-region optimization algorithm. The computation of the quadratic approximation is also accelerated by FFT at the same additional asymptotic cost ofO(N6logN). The method is demonstrated by fitting atomic protein models into several simulated and experimental maps from cryo-electron microscopy. The method is available at https://team.inria.fr/nano-d/software/offgridfit.
Published: 2017
Full Text: View/download PDF

36. Single-pass incremental force updates for adaptively restrained molecular dynamics

Author: Stephane Redon, Krishna Kant Singh, Krishna kant Singh, 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 ), and 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])
Subjects: 0301 basic medicine, Single pass, Speedup, 010304 chemical physics, Computer science, Observable, Incremental Force Update, General Chemistry, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 03 medical and health sciences, Computational Mathematics, Molecular dynamics, 030104 developmental biology, Neighbor Lists, 0103 physical sciences, Convergence (routing), Adaptively Restrained Molecular Dynamics, Algorithm
Abstract: Adaptively restrained molecular dynamics (ARMD) allows users to perform more integration steps in wall-clock time by switching on and off positional degrees of freedoms. This article presents new, single-pass incremental force updates algorithms to efficiently simulate a system using ARMD. We assessed different algorithms for speedup measurements and implemented them in the LAMMPS MD package. We validated the single-pass incremental force update algorithm on four different benchmarks using diverse pair potentials. The proposed algorithm allows us to perform simulation of a system faster than traditional MD in both NVE and NVT ensembles. Moreover, ARMD using the new single-pass algorithm speeds up the convergence of observables in wall-clock time. © 2017 Wiley Periodicals, Inc.
Published: 2017
Full Text: View/download PDF

37. Parallel Adaptively Restrained Molecular Dynamics

Author: Stephane Redon, Krishna Kant Singh, Dmitriy F. Marin, 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 ), and 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])
Subjects: ARMD, 010304 chemical physics, Computer science, Interface (computing), Computation, Parallel algorithm, Molecular Dynamics, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computational science, Molecular dynamics, 0103 physical sciences, Scalability, MPI, 010306 general physics, Single-pass Incremental Force Update, LAMMPS
Abstract: International audience; Force computations are one of the most time consuming part in performing Molecular Dynamics (MD) simulations. Adaptively Restrained Molecular Dynamics (ARMD) makes it possible to perform fewer force calculations by adap-tively restraining particles positions. This paper introduces parallel algorithms for single-pass incremental force computations to take advantage of adaptive restraints using the Message Passage Interface (MPI) standard. The proposed algorithms are implemented and validated in LAMMPS, however, these algorithms can be applied to other MD simulators. We compared our algorithms with LAMMPS for performance and scalability measurements.
Published: 2017
Full Text: View/download PDF

38. Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2

Author: Sergei Grudinin, Maria Kadukova, 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]), and Moscow Institute of Physics and Technology [Moscow] (MIPT)
Subjects: 0301 basic medicine, Computer science, Protein Conformation, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Receptors, Cytoplasmic and Nuclear, Computational biology, Crystallography, X-Ray, Ligands, 01 natural sciences, ensemble docking, Autodock vina, Small Molecule Libraries, 03 medical and health sciences, Computational chemistry, Drug Discovery, scoring function, Humans, Physical and Theoretical Chemistry, Receptor, Databases, Protein, Lead Finder, Binding Sites, protein-ligand docking, Small molecule, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 0104 chemical sciences, Computer Science Applications, Molecular Docking Simulation, 010404 medicinal & biomolecular chemistry, 030104 developmental biology, Protein–ligand docking, Searching the conformational space for docking, Docking (molecular), Drug Design, Computer-Aided Design, Farnesoid X receptor, flexible docking, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], D3R, Software, Protein Binding
Abstract: International audience; The 2016 D3R Grand Challenge 2 provided an opportunity to test multiple protein-ligand docking protocols on a set of ligands bound to farnesoid X receptor that has many available experimental structures. We participated in the Stage 1 of the Challenge devoted to the docking pose predictions, with the mean RMSD value of our submission poses of 2.9 Å. Here we present a thorough analysis of our docking predictions made with AutoDock Vina and the Convex-PL rescoring potential by reproducing our submission protocol and running a series of additional molecular docking experiments. We conclude that a correct receptor structure, or more precisely, the structure of the binding pocket, plays the crucial role in the success of our docking studies. We have also noticed the important role of a local ligand geometry, which seems to be not well discussed in literature. We succeed to improve our results up to the mean RMSD value of 2.15 – 2.33 Å dependent on the models of the ligands, if docking these to all available homologous receptors. Overall, for docking of ligands of diverse chemical series we suggest to perform docking of each of the ligands to a set of multiple receptors that are homologous to the target.
Published: 2017
Full Text: View/download PDF

39. Pepsi-SAXS : an adaptive method for rapid and accurate computation of small-angle X-ray scattering profiles

Author: Maria Garkavenko, Andrei Kazennov, Sergei Grudinin, 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]), and Moscow Institute of Physics and Technology [Moscow] (MIPT)
Subjects: Models, Molecular, 0301 basic medicine, Time Factors, Protein Conformation, Zernike polynomials, Computation, small-angle scattering, 03 medical and health sciences, symbols.namesake, Optics, Quadratic equation, X-Ray Diffraction, Structural Biology, [MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph], Scattering, Small Angle, Animals, Humans, Nyquist–Shannon sampling theorem, Mathematics, Debye, Pepsi-SAXS, Scattering, business.industry, Proteins, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computational physics, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, 030104 developmental biology, symbols, Small-angle scattering, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Multipole expansion, multipole expansion, Algorithms, Software
Abstract: A new method calledPepsi-SAXSis presented that calculates small-angle X-ray scattering profiles from atomistic models. The method is based on the multipole expansion scheme and is significantly faster compared with other tested methods. In particular, using the Nyquist–Shannon–Kotelnikov sampling theorem, the multipole expansion order is adapted to the size of the model and the resolution of the experimental data. It is argued that by using the adaptive expansion order, this method has the same quadratic dependence on the number of atoms in the model as the Debye-based approach, but with a much smaller prefactor in the computational complexity. The method has been systematically validated on a large set of over 50 models collected from the BioIsis and SASBDB databases. Using a laptop, it was demonstrated thatPepsi-SAXSis about seven, 29 and 36 times faster compared withCRYSOL,FoXSand the three-dimensional Zernike method inSAStbx, respectively, when tested on data from the BioIsis database, and is about five, 21 and 25 times faster compared withCRYSOL,FoXSandSAStbx, respectively, when tested on data from SASBDB. On average,Pepsi-SAXSdemonstrates comparable accuracy in terms of χ2toCRYSOLandFoXSwhen tested on BioIsis and SASBDB profiles. Together with a small allowed variation of adjustable parameters, this demonstrates the effectiveness of the method.Pepsi-SAXSis available at http://team.inria.fr/nano-d/software/pepsi-saxs.
Published: 2017
Full Text: View/download PDF

40. NOLB: Nonlinear Rigid Block Normal Mode Analysis Method

Author: Sergei Grudinin, Alexandre Hoffmann, 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 ), and 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])
Subjects: 0301 basic medicine, Physics, Basis (linear algebra), Extrapolation, Motion (geometry), Angular velocity, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, 03 medical and health sciences, Nonlinear system, 030104 developmental biology, Classical mechanics, Normal mode, Physical and Theoretical Chemistry, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Rotation (mathematics), Eigenvalues and eigenvectors
Abstract: We present a new conceptually simple and computationally efficient method for nonlinear normal-mode analysis called NOLB. It relies on the rotations-translations of blocks (RTB) theoretical basis developed by Y.-H. Sanejouand and colleagues [ Durand et al. Biopolymers 1994 , 34 , 759 - 771 . Tama et al. Proteins: Struct., Funct., Bioinf . 2000 , 41 , 1 - 7 ]. We demonstrate how to physically interpret the eigenvalues computed in the RTB basis in terms of angular and linear velocities applied to the rigid blocks and how to construct a nonlinear extrapolation of motion out of these velocities. The key observation of our method is that the angular velocity of a rigid block can be interpreted as the result of an implicit force, such that the motion of the rigid block can be considered as a pure rotation about a certain center. We demonstrate the motions produced with the NOLB method on three different molecular systems and show that some of the lowest frequency normal modes correspond to the biologically relevant motions. For example, NOLB detects the spiral sliding motion of the TALE protein, which is capable of rapid diffusion along its target DNA. Overall, our method produces better structures compared to the standard approach, especially at large deformation amplitudes, as we demonstrate by visual inspection, energy, and topology analyses and also by the MolProbity service validation. Finally, our method is scalable and can be applied to very large molecular systems, such as ribosomes. Standalone executables of the NOLB normal-mode analysis method are available at https://team.inria.fr/nano-d/software/nolb-normal-modes/ . A graphical user interface created for the SAMSON software platform will be made available at https://www.samson-connect.net .
Published: 2017
Full Text: View/download PDF

41. New Insights on Signal Propagation by Sensory Rhodopsin II/Transducer Complex

Author: Johann P. Klare, Petr Utrobin, Ivan Gushchin, Vitaly Polovinkin, Alina Remeeva, Valentin Borshchevskiy, Valentin Gordeliy, Martin Engelhard, Sergei Grudinin, Georg Büldt, Taras Balandin, Andrii Ishchenko, Ekaterina Round, Institute of Complex Systems (ICS), ICS-6: Structural Biochemistry ( ICS-6), Institute of Crystallography [Aachen], Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Institut de biologie structurale (IBS - UMR 5075 ), Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Moscow Institute of Physics and Technology [Moscow] (MIPT), 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]), Max Planck Institute of Molecular Physiology, Max-Planck-Gesellschaft, Department of Physics, University of Osnabrück, Osnabrück University, Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Universität Osnabrück - Osnabrück University
Subjects: Models, Molecular, 0301 basic medicine, Magnetic Resonance Spectroscopy, Protein Conformation, Archaeal Proteins, Static Electricity, Plasma protein binding, Biology, Article, HAMP domain, Structure-Activity Relationship, 03 medical and health sciences, Sensory Rhodopsins, Protein structure, Membrane proteins, Halobacteriaceae, Protein Interaction Domains and Motifs, Negative phototaxis, Binding Sites, Multidisciplinary, 030102 biochemistry & molecular biology, Nanocrystallography, Sensory rhodopsin II, biology.organism_classification, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 3. Good health, 030104 developmental biology, Biochemistry, Domain (ring theory), Biophysics, ddc:000, Protein Multimerization, Protein Binding, Signal Transduction
Abstract: The complex of two membrane proteins, sensory rhodopsin II (NpSRII) with its cognate transducer (NpHtrII), mediates negative phototaxis in halobacteria N. pharaonis. Upon light activation NpSRII triggers a signal transduction chain homologous to the two-component system in eubacterial chemotaxis. Here we report on crystal structures of the ground and active M-state of the complex in the space group I212121. We demonstrate that the relative orientation of symmetrical parts of the dimer is parallel (“U”-shaped) contrary to the gusset-like (“V”-shaped) form of the previously reported structures of the NpSRII/NpHtrII complex in the space group P21212, although the structures of the monomers taken individually are nearly the same. Computer modeling of the HAMP domain in the obtained “V”- and “U”-shaped structures revealed that only the “U”-shaped conformation allows for tight interactions of the receptor with the HAMP domain. This is in line with existing data and supports biological relevance of the “U” shape in the ground state. We suggest that the “V”-shaped structure may correspond to the active state of the complex and transition from the “U” to the “V”-shape of the receptor-transducer complex can be involved in signal transduction from the receptor to the signaling domain of NpHtrII.
Published: 2017
Full Text: View/download PDF

42. Hydroxylation of antitubercular drug candidate, SQ109, by mycobacterial cytochrome P450

Author: A. A. Gilep, Sergei Grudinin, Sergey Bukhdruker, Anton Kavaleuski, Polina Shabunya, Maria Kadukova, Irina Grabovec, Tatsiana Varaksa, Egor Marin, Anastasiia Gusach, Valentin Borshchevskiy, Natallia Strushkevich, Moscow Institute of Physics and Technology [Moscow] (MIPT), Institute of Bioorganic Chemistry, NASB, Minsk, National Academy of Sciences of Belarus (NASB), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute of Biomedical Chemistry [Moscou] (IBMC), MT-Medicals LLC [Moscou], Jülich Center for Structural Biology [Jülich] (JuStruct), Institute of Biological Information Processing [Jülich] (IBI-7), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), 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)-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 ), and Université Grenoble Alpes (UGA)
Subjects: Drug, crystal structure, Cytochrome, cytochrome P450, media_common.quotation_subject, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], SQ109, Antitubercular Agents, Adamantane, Hydroxylation, Article, Mycobacterium tuberculosis, lcsh:Chemistry, 03 medical and health sciences, chemistry.chemical_compound, Pharmacokinetics, Cytochrome P-450 Enzyme System, CYP124, lcsh:QH301-705.5, 030304 developmental biology, media_common, 0303 health sciences, Binding Sites, biology, 030306 microbiology, Cytochrome P450, Monooxygenase, respiratory system, biology.organism_classification, bacterial infections and mycoses, Ethylenediamines, 3. Good health, Molecular Docking Simulation, chemistry, Biochemistry, lcsh:Biology (General), lcsh:QD1-999, ddc:540, biology.protein, Protein Binding
Abstract: Spreading of the multidrug-resistant (MDR) strains of the one of the most harmful pathogen Mycobacterium tuberculosis (Mtb) generates the need for new effective drugs. SQ109 showed activity against resistant Mtb and already advanced to Phase II/III clinical trials. Fast SQ109 degradation is attributed to the human liver Cytochrome P450s (CYPs). However, no information is available about interactions of the drug with Mtb CYPs. Here, we show that Mtb CYP124, previously assigned as a methyl-branched lipid monooxygenase, binds and hydroxylates SQ109 in vitro. A 1.25 &Aring, resolution crystal structure of the CYP124&ndash, SQ109 complex unambiguously shows two conformations of the drug, both positioned for hydroxylation of the &omega, methyl group in the trans position. The hydroxylated SQ109 presumably forms stabilizing H-bonds with its target, Mycobacterial membrane protein Large 3 (MmpL3). We anticipate that Mtb CYPs could function as analogs of drug-metabolizing human CYPs affecting pharmacokinetics and pharmacodynamics of antitubercular (anti-TB) drugs.
Published: 2020
Full Text: View/download PDF

43. Interactive Chemical Reactivity Exploration

Author: Moritz P. Haag, Mael Bosson, Stephane Redon, Markus Reiher, Alain C. Vaucher, Laboratorium für Physikalische Chemie (ETH-LPC), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), 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 (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-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)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and 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])
Subjects: Chemical Physics (physics.chem-ph), Condensed Matter - Materials Science, Structure modification, potential energy surfaces, Computer science, haptic quantum chemistry, Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, Computational Physics (physics.comp-ph), interactive quantum chemistry, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Atomic and Molecular Physics, and Optics, Simultaneous perception, Human–computer interaction, Physics - Chemical Physics, Pointer (computer programming), Physical and Theoretical Chemistry, real-time quantum chemistry, Physics - Computational Physics, Quantum, chemical reactivity, Haptic technology, Intuition
Abstract: Elucidating chemical reactivity in complex molecular assemblies of a few hundred atoms is, despite the remarkable progress in quantum chemistry, still a major challenge. Black-box search methods to find intermediates and transition-state structures might fail in such situations because of the high-dimensionality of the potential energy surface. Here, we propose the concept of interactive chemical reactivity exploration to effectively introduce the chemist's intuition into the search process. We employ a haptic pointer device with force-feedback to allow the operator the direct manipulation of structures in three dimensions along with simultaneous perception of the quantum mechanical response upon structure modification as forces. We elaborate on the details of how such an interactive exploration should proceed and which technical difficulties need to be overcome. All reactivity-exploration concepts developed for this purpose have been implemented in the Samson programming environment., 36 pages, 14 figures
Published: 2014
Full Text: View/download PDF

44. HermiteFit: fast-fitting atomic structures into a low-resolution density map using three-dimensional orthogonal Hermite functions

Author: Georgy Derevyanko, Sergei Grudinin, Institute of Complex Systems (ICS), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Institut de biologie structurale (IBS - UMR 5075 ), Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Moscow Institute of Physics and Technology [Moscow] (MIPT), 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), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), ANR-11-MONU-0006,PEPSI,Expansions Polynomiales de Structures de Protéines et Interactions(2011), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), 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 ), and 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])
Subjects: Hermite spline, Protein Conformation, Fast Fourier transform, Basis function, density fitting, Cubic Hermite spline, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Structural Biology, Hermite interpolation, HermiteFit, [CHIM.CRIS]Chemical Sciences/Cristallography, Cryo-EM, [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS], Mathematics, Crystallography, Hermite polynomials, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], spherical harmonics, Spherical harmonics, Chaperonin 60, General Medicine, fast Fourier transform, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Hermite orthogonal polynomials, Fourier transform, symbols, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Conotoxins, Algorithm, Algorithms
Abstract: HermiteFit, a novel algorithm for fitting a protein structure into a low-resolution electron-density map, is presented. The algorithm accelerates the rotation of the Fourier image of the electron density by using three-dimensional orthogonal Hermite functions. As part of the new method, an algorithm for the rotation of the density in the Hermite basis and an algorithm for the conversion of the expansion coefficients into the Fourier basis are presented.HermiteFitwas implemented using the cross-correlation or the Laplacian-filtered cross-correlation as the fitting criterion. It is demonstrated that in the Hermite basis the Laplacian filter has a particularly simple form. To assess the quality of density encoding in the Hermite basis, an analytical way of computing the crystallographicRfactor is presented. Finally, the algorithm is validated using two examples and its efficiency is compared with two widely used fitting methods,ADP_EMandcoloresfrom theSituspackage.HermiteFitwill be made available at http://nano-d.inrialpes.fr/software/HermiteFit or upon request from the authors.
Published: 2014
Full Text: View/download PDF

45. 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
Full Text: View/download PDF

46. Quadratic Programming Approach to Fit Protein Complexes into Electron Density Maps

Author: Pogodin, Roman, Katrutsa, Alexander, Grudinin, Sergei, Moscow Institute of Physics and Technology [Moscow] (MIPT), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), 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 ), and 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])
Subjects: protein structure prediction, cryoEM, Optimization and Control (math.OC), FOS: Biological sciences, electron microscopy fitting, FOS: Mathematics, quadratic programming, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Mathematics - Optimization and Control, Quantitative Biology - Quantitative Methods, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Quantitative Methods (q-bio.QM)
Abstract: The paper investigates the problem of fitting protein complexes into electron density maps. They are represented by high-resolution cryoEM density maps converted into overlapping matrices and partly show a structure of a complex. The general purpose is to define positions of all proteins inside it. This problem is known to be NP-hard, since it lays in the field of combinatorial optimization over a set of discrete states of the complex. We introduce quadratic programming approaches to the problem. To find an approximate solution, we convert a density map into an overlapping matrix, which is generally indefinite. Since the matrix is indefinite, the optimization problem for the corresponding quadratic form is non-convex. To treat non-convexity of the optimization problem, we use different convex relaxations to find which set of proteins minimizes the quadratic form best., Comment: in Information Technology and Systems 2016, Sep 2016, Repino, St. Petersburg, Russia. 2016
Published: 2016

47. Inverse Protein Folding Problem via Quadratic Programming

Author: Riazanov, Andrii, Karasikov, Mikhail, Grudinin, Sergei, Moscow Institute of Physics and Technology [Moscow] (MIPT), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), 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 ), and 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])
Subjects: Quantitative Biology::Biomolecules, convex relaxation, quadratic programming, Quantitative Biology::Genomics, Quantitative Biology - Quantitative Methods, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Optimization and Control (math.OC), FOS: Biological sciences, FOS: Mathematics, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Mathematics - Optimization and Control, optimization, Quantitative Methods (q-bio.QM), inverse protein folding
Abstract: International audience; This paper presents a method of reconstruction a primary structure of a protein that folds into a given geometrical shape. This method predicts the primary structure of a protein and restores its linear sequence of amino acids in the polypeptide chain using the tertiary structure of a molecule. Unknown amino acids are determined according to the principle of energy minimization. This study represents inverse folding problem as a quadratic optimization problem and uses different relaxation techniques to reduce it to the problem of convex optimizations. Computational experiment compares the quality of these approaches on real protein structures.
Published: 2016

48. Langevin dynamics with general kinetic energies

Author: Gabriel Stoltz, Zofia Trstanova, Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), École des Ponts ParisTech (ENPC), MATHematics for MatERIALS (MATHERIALS), École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), 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]), ANR-14-CE23-0012,COSMOS,Statistique numérique et simulation moléculaire(2014), European Project: 307629,EC:FP7:ERC,ERC-2012-StG_20111012,ADAPT(2012), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), and École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC)
Subjects: Physics, Statistical Mechanics (cond-mat.stat-mech), Ecological Modeling, General Physics and Astronomy, Sampling (statistics), FOS: Physical sciences, 010103 numerical & computational mathematics, General Chemistry, Kinetic energy, 01 natural sciences, Computer Science Applications, 010104 statistics & probability, Quadratic equation, Modeling and Simulation, Metastability, Statistical physics, 0101 mathematics, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Langevin dynamics, Condensed Matter - Statistical Mechanics
Abstract: International audience We study Langevin dynamics with a kinetic energy different from the standard, quadratic one in order to accelerate the sampling of Boltzmann-Gibbs distributions. In particular, this kinetic energy can be nonglobally Lipschitz, which raises issues for the stability of discretizations of the associated Langevin dynamics. We first prove the exponential convergence of the law of the continuous process to the Boltzmann-Gibbs measure by a hypocoercive approach and characterize the asymptotic variance of empirical averages over trajectories. We next develop numerical schemes which are stable and of weak order two by considering splitting strategies where the discretizations of the fluctuation/dissipation are corrected by a Metropolis procedure. We use the newly developed schemes for two applications: optimizing the shape of the kinetic energy for the so-called adaptively restrained Langevin dynamics (which considers perturbations of standard quadratic kinetic energies vanishing around the origin) and reducing the metastability of some toy models using nonglobally Lipschitz kinetic energies.
Published: 2016
Full Text: View/download PDF

49. Predicting binding poses and affinities for protein-ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

Author: Simon Marillet, Maria Kadukova, Frédéric Cazals, Sergei Grudinin, Andreas Eisenbarth, 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]), Moscow Institute of Physics and Technology [Moscow] (MIPT), Algorithms, Biology, Structure (ABS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité de recherche Virologie et Immunologie Moléculaires (VIM (UR 0892)), Institut National de la Recherche Agronomique (INRA), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), 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), Molecular Immunology and Virology (VIM-UR892), and Université Côte d'Azur (UCA)
Subjects: 0301 basic medicine, Protein Conformation, Computer science, Entropy, Ligands, computer.software_genre, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 01 natural sciences, Molecular Docking Simulation, Structure-Activity Relationship, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0103 physical sciences, Drug Discovery, Convex-PL, ridge regression, scoring function, Humans, Entropy (information theory), HSP90 Heat-Shock Proteins, Prospective Studies, Physical and Theoretical Chemistry, Databases, Protein, AutoDock Vina, Binding Sites, 010304 chemical physics, protein-ligand docking, Estimation theory, Statistical parameter, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, 030104 developmental biology, machine learning, Protein–ligand docking, Drug Design, Test set, Regression Analysis, Thermodynamics, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], parameter estimation, computer, Protein Binding, Protein ligand, Free parameter
Abstract: International audience; The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.
Published: 2016
Full Text: View/download PDF

50. Spherical polar Fourier assembly of protein complexes with arbitrary point group symmetry

Author: Sergei Grudinin, David W. Ritchie, 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), 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]), and ANR-11-MONU-0006,PEPSI,Expansions Polynomiales de Structures de Protéines et Interactions(2011)
Subjects: 0301 basic medicine, Fast Fourier transform, symmetry docking, point group symmetry, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, symbols.namesake, chemistry.chemical_compound, polar Fourier correlation, Molecular symmetry, Macromolecular docking, Fourier domain, Physics, 030102 biochemistry & molecular biology, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, protein docking, Crystallography, 030104 developmental biology, Fourier transform, Monomer, chemistry, Homogeneous space, symbols, Polar, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: A novel fast Fourier transform-basedab initodocking algorithm calledSAMis presented, for building perfectly symmetrical models of protein complexes with arbitrary point group symmetry. The basic approach uses a novel and very fast one-dimensional symmetry-constrained spherical polar Fourier search to assemble cyclicCnsystems from a given protein monomer. Structures with higher-order (Dn,T,OandI) point group symmetries may be built using a subsequent symmetry-constrained Fourier domain search to assemble trimeric sub-units. The results reported here show that theSAMalgorithm can correctly assemble monomers of up to around 500 residues to produce a near-native complex structure with the given point group symmetry in 17 out of 18 test cases. TheSAMprogram may be downloaded for academic use at http://sam.loria.fr/.
Published: 2016
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Journal

Database

Publisher

77 results on '"Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources