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1. Explaining Conformational Diversity in Protein Families through Molecular Motions

Author

Valentin Lombard, Sergei Grudinin, and Elodie Laine

Subjects
Science
Abstract

Abstract Proteins play a central role in biological processes, and understanding their conformational variability is crucial for unraveling their functional mechanisms. Recent advancements in high-throughput technologies have enhanced our knowledge of protein structures, yet predicting their multiple conformational states and motions remains challenging. This study introduces Dimensionality Analysis for protein Conformational Exploration (DANCE) for a systematic and comprehensive description of protein families conformational variability. DANCE accommodates both experimental and predicted structures. It is suitable for analysing anything from single proteins to superfamilies. Employing it, we clustered all experimentally resolved protein structures available in the Protein Data Bank into conformational collections and characterized them as sets of linear motions. The resource facilitates access and exploitation of the multiple states adopted by a protein and its homologs. Beyond descriptive analysis, we assessed classical dimensionality reduction techniques for sampling unseen states on a representative benchmark. This work improves our understanding of how proteins deform to perform their functions and opens ways to a standardised evaluation of methods designed to sample and generate protein conformations.

Published
2024
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3. Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data

Author

Sri Devan Appasamy, John Berrisford, Romana Gaborova, Sreenath Nair, Stephen Anyango, Sergei Grudinin, Mandar Deshpande, David Armstrong, Ivanna Pidruchna, Joseph I. J. Ellaway, Grisell Díaz Leines, Deepti Gupta, Deborah Harrus, Mihaly Varadi, and Sameer Velankar

Subjects
Science
Abstract

Abstract Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately. Employing the proposed approach, we assigned standard names to over 90% of unique assemblies in the PDB and provided persistent identifiers for each assembly. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of macromolecular complexes. Furthermore, the data standardisation improves the PDB’s FAIR attributes, fostering more effective basic and translational research and scientific education.

Published
2023
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17. Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution.

Author

Andreas Haahr Larsen, Yong Wang, Sandro Bottaro, Sergei Grudinin, Lise Arleth, and Kresten Lindorff-Larsen

Subjects
Biology (General), QH301-705.5
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.

Published
2020
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30. Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Author

Ilya A. Vakser, Sergei Grudinin, Nathan W. Jenkins, Petras J. Kundrotas, Eric J. Deeds, University of Kansas [Lawrence] (KU), 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), and Institute for Quantitative and Computational Biosciences - University of california LA

Subjects
[SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Kinetics, Multidisciplinary, Proteins, Molecular Dynamics Simulation, Monte Carlo Method, Algorithms, Biophysical Phenomena
Abstract

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, so far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions, but are relatively slow, if carried out at all-atom resolution, or are significantly coarse-grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise Fast Fourier Transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

Published
2022
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38. 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
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39. 3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework

Author

William Pierré, Lionel Hervé, Chiara Paviolo, Ondrej Mandula, Vincent Remondiere, Sophie Morales, Sergei Grudinin, Pierre F. Ray, Magali Dhellemmes, Christophe Arnoult, Cédric Allier, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), 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), Laboratoire Photonique, Numérique et Nanosciences (LP2N), Université de Bordeaux (UB)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS), Département Microtechnologies pour la Biologie et la Santé (DTBS), 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)), Laboratoire d'Innovation pour les Technologies des Energies Nouvelles et les nanomatériaux (LITEN), Institut National de L'Energie Solaire (INES), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-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), Institute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) (IAB), Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Etablissement français du sang - Auvergne-Rhône-Alpes (EFS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CHU Grenoble, and ANR-21-CE19-0020,LIVE3D_CNN,Développement d'un vidéo microscope holographique 3D associée à des réseaux de neurones convolutifs (CNN) pour l'identification de nouveaux marqueurs de la santé embryonnaire préimplantatoire(2021)

Subjects
[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics], Mice, Refractometry, Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Animals, Electrical and Electronic Engineering, Tomography, X-Ray Computed, Engineering (miscellaneous), Time-Lapse Imaging, Tomography, Atomic and Molecular Physics, and Optics
Abstract

We present a compact 3D diffractive microscope that can be inserted directly in a cell incubator for long-term observation of developing organisms. Our setup is particularly simple and robust, since it does not include any moving parts and is compatible with commercial cell culture containers. It has been designed to image large specimens ( > 100 × 100 × 100 µ m 3 ) with subcellular resolution. The sample’s optical properties [refractive index (RI) and absorption] are reconstructed in 3D from intensity-only images recorded with different illumination angles produced by an LED array. The reconstruction is performed using the beam propagation method embedded inside a deep-learning network where the layers encode the optical properties of the object. This deep neural network is trained for a given multiangle intensity acquisition. After training, the weights of the neural network deliver the 3D distribution of the optical properties of the sample. The effect of spherical aberrations due to the sample holder/air interfaces are taken into account in the forward model. Using this approach, we performed time-lapse 3D imaging of preimplantation mouse embryos over six days. Images of embryos from a single cell (low-scattering regime) to the blastocyst stage (highly scattering regime) were successfully reconstructed. Due to its subcellular resolution, our system can provide quantitative information on the embryos’ development and viability. Hence, this technology opens what we believe to be novel opportunities for 3D label-free live-cell imaging of whole embryos or organoids over long observation times.

Published
2022
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40. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Author

Xiaoqin Zou, Théo Mauri, Hang Shi, Shaowen Zhu, Justas Dapkūnas, Yuanfei Sun, Didier Barradas-Bautista, Raphael A. G. Chaleil, Ragul Gowthaman, Sohee Kwon, Xianjin Xu, Zuzana Jandova, Genki Terashi, Ryota Ashizawa, Petras J. Kundrotas, Shuang Zhang, Tunde Aderinwale, Jian Liu, Sandor Vajda, Paul A. Bates, Jianlin Cheng, Daisuke Kihara, Luis A. Rodríguez-Lumbreras, Carlos A. Del Carpio Muñoz, Liming Qiu, Guillaume Brysbaert, Jorge Roel-Touris, Česlovas Venclovas, Tereza Clarence, Rui Yin, Amar Singh, Patryk A. Wesołowski, Rafał Ślusarz, Adam Liwo, Guangbo Yang, Agnieszka S. Karczyńska, Yoshiki Harada, Sergei Kotelnikov, Yuya Hanazono, Charlotte W. van Noort, Marc F. Lensink, Jonghun Won, Adam K. Sieradzan, Israel Desta, Xufeng Lu, Charles Christoffer, Anna Antoniak, Taeyong Park, Sheng-You Huang, Tsukasa Nakamura, Brian G. Pierce, Usman Ghani, Yang Shen, Luigi Cavallo, Chaok Seok, Hao Li, Nurul Nadzirin, Ghazaleh Taherzadeh, Jacob Verburgt, Rodrigo V. Honorato, Artur Giełdoń, Jeffrey J. Gray, Dima Kozakov, Ming Liu, Shan Chang, Eiichiro Ichiishi, Manon Réau, Rui Duan, Francesco Ambrosetti, Johnathan D. Guest, Juan Fernández-Recio, Alexandre M. J. J. Bonvin, Ilya A. Vakser, Farhan Quadir, Yumeng Yan, Ren Kong, Sameer Velankar, Sergei Grudinin, Mateusz Kogut, Mikhail Ignatov, Yasuomi Kiyota, Hyeonuk Woo, Shoshana J. Wodak, Ameya Harmalkar, Shinpei Kobayashi, Panagiotis I. Koukos, Zhen Cao, Kliment Olechnovič, Cezary Czaplewski, Xiao Wang, Agnieszka G. Lipska, Kathryn A. Porter, Peicong Lin, Emilia A. Lubecka, Nasser Hashemi, Bin Liu, Mayuko Takeda-Shitaka, Karolina Zięba, Dzmitry Padhorny, Zhuyezi Sun, Daipayan Sarkar, Romina Oliva, Andrey Alekseenko, Siri Camee van Keulen, Mireia Rosell, Raj S. Roy, Brian Jiménez-García, Jinsol Yang, Martyna Maszota-Zieleniak, Cancer Research UK, Department of Energy and Climate Change (UK), European Commission, Institut National de Recherche en Informatique et en Automatique (France), Medical Research Council (UK), Japan Society for the Promotion of Science, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), National Institute of General Medical Sciences (US), National Institutes of Health (US), National Natural Science Foundation of China, National Science Foundation (US), Unité de Glycobiologie Structurale et Fonctionnelle (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Biomolecular Modelling Laboratory [London], The Francis Crick Institute [London], Jiangsu University of Technology [Changzhou], Department of Electrical Engineering and Computer Science [Columbia] (EECS), University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, Institute for Data Science and Informatics [Columbia], University of Gdańsk (UG), Faculty of Electronics, Telecommunications and Informatics [GUT Gdańsk] (ETI), Gdańsk University of Technology (GUT), Medical University of Gdańsk, Graduate School of Medical Sciences [Nagoya], Nagoya City University [Nagoya, Japan], International University of Health and Welfare Hospital (IUHW Hospital), Department of Chemical and Biomolecular Engineering [Baltimore], Johns Hopkins University (JHU), Bijvoet Center of Biomolecular Research [Utrecht], Utrecht University [Utrecht], Stony Brook University [SUNY] (SBU), State University of New York (SUNY), Innopolis University, Boston University [Boston] (BU), Russian Academy of Sciences [Moscow] (RAS), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Universidad de La Rioja (UR), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Huazhong University of Science and Technology [Wuhan] (HUST), Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Graduate School of Information Sciences [Sendaï], Tohoku University [Sendai], National Institutes for Quantum and Radiological Science and Technology (QST), University of Maryland [Baltimore], King Abdullah University of Science and Technology (KAUST), University of Naples Federico II, Texas A&M University [Galveston], Seoul National University [Seoul] (SNU), Kitasato University, University of Kansas [Lawrence] (KU), Vilnius University [Vilnius], University of Missouri System, VIB-VUB Center for Structural Biology [Bruxelles], VIB [Belgium], Sub NMR Spectroscopy, Sub Overig UiLOTS, Sub Mathematics Education, NMR Spectroscopy, Université de Lille, CNRS, Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576, European Bioinformatics Institute [Hinxton] [EMBL-EBI], Department of Electrical Engineering and Computer Science [Columbia] [EECS], Faculty of Chemistry [Univ Gdańsk], Faculty of Electronics, Telecommunications and Informatics [GUT Gdańsk] [ETI], International University of Health and Welfare Hospital [IUHW Hospital], Johns Hopkins University [JHU], Stony Brook University [SUNY] [SBU], Department of Biomedical Engineering [Boston], Instituto de Ciencias de la Vid y el Vino [ICVV], Huazhong University of Science and Technology [Wuhan] [HUST], Indiana University - Purdue University Indianapolis [IUPUI], National Institutes for Quantum and Radiological Science and Technology [QST], King Abdullah University of Science and Technology [KAUST], Università degli Studi di Napoli 'Parthenope' = University of Naples [PARTHENOPE], Seoul National University [Seoul] [SNU], University of Kansas [Lawrence] [KU], University of Missouri [Columbia] [Mizzou], Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Naples Federico II = Università degli studi di Napoli Federico II, European Project: 675728,H2020,H2020-EINFRA-2015-1,BioExcel(2015), European Project: 823830,H2020-EU.1.4.1.3. Development, deployment and operation of ICT-based e-infrastructures, H2020-EU.1.4. EXCELLENT SCIENCE - Research Infrastructures ,BioExcel-2(2019), European Project: 777536,H2020-EU.1.4.1.3. Development, deployment and operation of ICT-based e-infrastructures, and H2020-EU.1.4. EXCELLENT SCIENCE - Research Infrastructures,EOSC-hub(2018)

Subjects
Models, Molecular, blind prediction, CAPRI, CASP, docking, oligomeric state, protein assemblies, protein complexes, protein docking, protein–protein interaction, template-based modeling, Computer science, [SDV]Life Sciences [q-bio], Machine learning, computer.software_genre, Biochemistry, Article, protein-protein interaction, 03 medical and health sciences, Sequence Analysis, Protein, Structural Biology, Server, Protein Interaction Domains and Motifs, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Binding Sites, business.industry, 030302 biochemistry & molecular biology, Computational Biology, Proteins, 3. Good health, Molecular Docking Simulation, Artificial intelligence, business, computer, Software
Abstract

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands., Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC001003

Published
2021
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41. Convex-PLR – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Author

Sergei Grudinin, Vladimir Chupin, and Maria Kadukova

Subjects
Structure (mathematical logic), 0303 health sciences, Virtual screening, 010304 chemical physics, business.industry, media_common.quotation_subject, Parameterized complexity, Machine learning, computer.software_genre, 01 natural sciences, Pipeline (software), 03 medical and health sciences, Docking (molecular), 0103 physical sciences, Artificial intelligence, business, Heuristics, Function (engineering), computer, 030304 developmental biology, Interpretability, media_common
Abstract

Virtual screening is an essential part of the modern drug design pipeline, which significantly accelerates the discovery of new drug candidates. Structure-based virtual screening involves ligand conformational sampling, which is often followed by re-scoring of docking poses. A great variety of scoring functions have been designed for this purpose. The advent of structural and affinity databases and the progress in machine-learning methods have recently boosted scoring function performance. Nonetheless, the most successful scoring functions are typically designed for specific tasks or systems. All-purpose scoring functions still perform poorly on the virtual screening tests, compared to precision with which they are able to predict co-crystal binding poses. Another limitation is the low interpretability of the heuristics being used.We analyzed scoring functions’ performance in the CASF benchmarks and discovered that the vast majority of them have a strong bias towards predicting larger binding interfaces. This motivated us to develop a physical model with additional entropic terms with the aim of penalizing such a preference. We parameterized the new model using affinity and structural data, solving a classification problem followed by regression. The new model, called Convex-PLR, demonstrated high-quality results on multiple tests and a substantial improvement over its predecessor Convex-PL. Convex-PLR can be used for molecular docking together with VinaCPL, our version of AutoDock Vina, with Convex-PL integrated as a scoring function. Convex-PLR, Convex-PL, and VinaCPL are available at https://team.inria.fr/nano-d/convex-pl/.

Published
2021
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42. Deep learning entering the post-protein structure prediction era : new horizons for structural biology

Author

Sergei Grudinin, 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 ), and Université Grenoble Alpes (UGA)

Subjects
Inorganic Chemistry, Protein language models, Protein structure prediction, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Structural Biology, Geometric learning, General Materials Science, Deep learning, Physical and Theoretical Chemistry, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Condensed Matter Physics, Biochemistry, End-to-end architectures
Abstract

International audience; The potential of deep learning has been recognized in structural bioinformatics for already some time, and became indisputable after the CASP13 (Critical Assessment of Structure Prediction) community-wide experiment in 2018. In CASP14, held in 2020, deep learning has boosted the field to unexpected levels reaching near-experimental accuracy. Its results demonstrate dramatic improvement in computing the three-dimensional structure of proteins from amino acid sequence, with many models rivalling experimental structures. This success comes from advances transferred from several machine-learning areas, including computer vision and natural language processing. At the same time, the community has developed methods specifically designed to deal with protein sequences and structures, and their representations. Novel emerging approaches include (i) geometric learning, i.e. learning on non-regular representations such as graphs, 3D Voronoi tessellations, and point clouds; (ii) pre-trained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of big data, e.g. large meta-genome databases; (v) combining protein representations; (vi) and finally truly end-to-end architectures, i.e. single differentiable models starting from a sequence and returning a 3D structure. These observations suggest that deep learning approaches will also be effective for a range of related structural biology applications that will be discussed in this lecture.

Published
2021
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43. Author response for 'Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment'

Author

Yumeng Yan, Mateusz Kogut, Sohee Kwon, Israel Desta, Petras J. Kundrotas, Xiaoqin Zou, Xiao Wang, Dima Kozakov, Eiichiro Ichiishi, Kathryn A. Porter, Johnathan D. Guest, Brian G. Pierce, Daisuke Kihara, Česlovas Venclovas, Agnieszka G. Lipska, Luigi Cavallo, Panagiotis I. Koukos, Yang Shen, Ren Kong, Brian Jiménez-García, Kliment Olechnovič, Cezary Czaplewski, Peicong Lin, Sameer Velankar, Shoshana J. Wodak, Agnieszka S. Karczyńska, Emilia A. Lubecka, Mikhail Ignatov, Shan Chang, Daipayan Sarkar, Sheng-You Huang, Chaok Seok, Nurul Nadzirin, Hao Li, Anna Antoniak, Manon Réau, Hyeonuk Woo, Siri Camee van Keulen, Ryota Ashizawa, Nasser Hashemi, Adam Liwo, Zhen Cao, Yoshiki Harada, Genki Terashi, Ameya Harmalkar, Farhan Quadir, Shinpei Kobayashi, Sandor Vajda, Zuzana Jandova, Juan Fernández-Recio, Amar Singh, Martyna Maszota-Zieleniak, Rodrigo V. Honorato, Usman Ghani, Sergei Grudinin, Xufeng Lu, Jorge Roel-Touris, Ming Liu, Paul A. Bates, Ghazaleh Taherzadeh, Adam K. Sieradzan, Patryk A. Wesołowski, Théo Mauri, Ilya A. Vakser, Francesco Ambrosetti, Jinsol Yang, Sergei Kotelnikov, Hang Shi, Shuang Zhang, Marc F. Lensink, Justas Dapkūnas, Yasuomi Kiyota, Taeyong Park, Mayuko Takeda-Shitaka, Andrey Alekseenko, Jian Liu, Artur Giełdoń, Ragul Gowthaman, Jonghun Won, Tsukasa Nakamura, Tunde Aderinwale, Yuanfei Sun, Guillaume Brysbaert, Jeffrey J. Gray, Luis A. Rodríguez-Lumbreras, Yuya Hanazono, Charlotte W. van Noort, Carlos A. Del Carpio Muñoz, Rui Duan, Alexandre M. J. J. Bonvin, Jianlin Cheng, Liming Qiu, Tereza Clarence, Rui Yin, Guangbo Yang, Shaowen Zhu, Didier Barradas-Bautista, Rafał Ślusarz, Raphael A. G. Chaleil, Charles Christoffer, Jacob Verburgt, Dzmitry Padhorny, Zhuyezi Sun, Romina Oliva, Mireia Rosell, Raj S. Roy, Bin Liu, and Karolina Zięba

Subjects
Frontier, Computer science, Econometrics
Published
2021
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44. 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
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45. Two distinct states of the HAMP domain from sensory rhodopsin transducer observed in unbiased molecular dynamics simulations.

Author

Ivan Gushchin, Valentin Gordeliy, and Sergei Grudinin

Subjects
Medicine, Science
Abstract

HAMP domain is a ubiquitous module of bacterial and archaeal two-component signaling systems. Considerable progress has been made recently in studies of its structure and conformational changes. However, the mechanism of signal transduction through the HAMP domain is not clear. It remains a question whether all the HAMPs have the same mechanism of action and what are the differences between the domains from different protein families. Here, we present the results of unbiased molecular dynamics simulations of the HAMP domain from the archaeal phototaxis signal transducer NpHtrII. Two distinct conformational states of the HAMP domain are observed, that differ in relative position of the helices AS1 and AS2. The longitudinal shift is roughly equal to a half of an α-helix turn, although sometimes it reaches one full turn. The states are closely related to the position of bulky hydrophobic aminoacids at the HAMP domain core. The observed features are in good agreement with recent experimental results and allow us to propose that the states detected in the simulations are the resting state and the signaling state of the NpHtrII HAMP domain. To the best of our knowledge, this is the first observation of the same HAMP domain in different conformations. The simulations also underline the difference between AMBER ff99-SB-ILDN and CHARMM22-CMAP forcefields, as the former favors the resting state and the latter favors the signaling state.

Published
2013
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47. AnAnaS: Software for Analytical Analysis of Symmetries in Protein Structures

Author

Guillaume, Pagès and Sergei, Grudinin

Subjects
Isomerism, Protein Conformation, Sequence Analysis, Protein, Protein Multimerization, Software
Abstract

Symmetry is very common among proteins found in structural databases such as the Protein Data Bank (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 pseudosymmetry. The method is very fast as most of the steps are performed analytically.

Published
2020

48. 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
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49. 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
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50. 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
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