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Epitope identification of SARS-CoV-2 structural proteins using in silico approaches to obtain a conserved rational immunogenic peptide

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Immunoinformatics (Amsterdam, Netherlands) 2022年 7卷 100015页

作者： Leonardo Pereira de Araújo Maria Eduarda Carvalho Dias Gislaine Cristina Scodeler Ana de Souza Santos Letícia Martins Soares Patrícia Paiva Corsetti Ana Carolina Barbosa Padovan Nelson José de Freitas Silveira Leonardo Augusto de Almeida Laboratory of Molecular Biology of Microorganisms Federal University of Alfenas Alfenas Minas Gerais Brazil. Laboratory of Molecular Modeling and Computer Simulation Federal University of Alfenas Alfenas Minas Gerais Brazil.

The short time between the first cases of COVID-19 and the declaration of a pandemic initiated the search for ways to stop the spread of SARS-CoV-2. There are great expectations regarding the development of effective vaccines that protect against all variants, and in the search for it, we hypothesized the obtention of a predicted rational immunogenic peptide from structural components of SARS-CoV-2 might help the vaccine research direction. In the search for a candidate of an immunogenic peptide of the SARS-CoV-2 envelope (E), membrane (M), nucleocapsid (N), or spike (S) proteins, we access the predicted sequences of each protein after the genome sequenced worldwide. We obtained the consensus amino acid sequences of about 14,441 sequences of each protein of each continent and the worldwide consensus sequence. For epitope identification and characterization from each consensus structural protein related to MHC-I or MHC-II interaction and B-cell receptor recognition, we used the IEDB reaching 68 epitopes to E, 174 to M, 245 to N, and 833 to S proteins. To select an epitope with the highest probability of binding to the MHC or BCR, all epitopes of each consensus sequence were aligned. The curation indicated 1, 4, 8, and 21 selected epitopes for E, M, N, and S proteins, respectively. Those epitopes were tested in silico for antigenicity obtaining 16 antigenic epitopes. Physicochemical properties and allergenicity evaluation of the obtained epitopes were done. Ranking the results, we obtained one epitope of each protein except for the S protein that presented two epitopes after the selection. To check the 3D position of each selected epitope in the protein structure, we used molecular homology modeling. Afterward, each selected epitope was evaluated by molecular docking to reference MHC-I or MHC-II allelic protein sequences. Taken together, the results obtained in this study showed a rational search for a putative immunogenic peptide of SARS-CoV-2 structural proteins tha

关键词： COVID-19 New coronavirus Reverse vaccinology SARS-CoV-2

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Structure-Based Experimental Datasets for Benchmarking of Protein simulation Force Fields

arXiv

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arXiv 2023年

作者： Cavender, Chapin E. Case, David A. Chen, Julian C.-H. Chong, Lillian T. Keedy, Daniel A. Lindorff-Larsen, Kresten Mobley, David L. Ollila, O.H. Samuli Oostenbrink, Chris Robustelli, Paul Voelz, Vincent A. Wall, Michael E. Wych, David C. Gilson, Michael K. Skaggs School of Pharmacy and Pharmaceutical Sciences UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Dept. of Chemistry & Chemical Biology Rutgers University PiscatawayNJ08854 United States Bioscience Division Los Alamos National Laboratory Los Alamos NM87545 United States Department of Chemistry and Biochemistry The University of Toledo ToledoOH43606 United States University of Pittsburgh 219 Parkman Avenue PittsburghPA15260 United States Structural Biology Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Department of Chemistry and Biochemistry City College of New York New YorkNY10031 United States PhD Programs in Biochemistry Biology and Chemistry CUNY Graduate Center New YorkNY10016 United States Linderstrøm-Lang Centre for Protein Science Department of Biology University of Copenhagen Copenhagen NDK-2200 Denmark Pharmaceutical Sciences Department University of California Irvine IrvineCA United States University of Helsinki Institute of Biotechnology Helsinki Finland Institute for Molecular Modeling and Simulation University of Natural Resources and Life Sciences Muthgasse 11 Vienna1190 Austria Dartmouth College Department of Chemistry HanoverNH03755 United States Department of Chemistry Temple University PhiladelphiaPA United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States The Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States

This review article provides an overview of structurally oriented, experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss why these observables are useful for assessing force field accuracy, how they can be calculated from simulation trajectories, and statistical issues that arise when comparing simulations with experiment. The target audience for this article is computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations. © 2023, CC BY.

关键词： Proteins

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： molecular dynamics

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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ENteric Immunity SImulator: A Tool for in silico Study of Gut Immunopathologies

ENteric Immunity SImulator: A Tool for in silico Study of Gu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Katherine V. Wendeldorf Josep Bassaganya-Riera Keith Bisset Stephen Eubank Raquel Hontecillas Madhav Marathe Network Dynamics and Simulation Science Laboratory Center of Modeling Immunity to Enteric Pathogens Virginia Bioinformatics Institute Virginia Polytechnic Institute and University Blacksburg VA USA Nutritional Immunology and Molecular Medicine Laboratory Center of Modeling Immunity to Enteric Pathogens Virginia Bioinformatics Institute Virginia Polytechnic Institute and University Blacksburg VA USA Department of Computer Science Center of Modeling Immunity to Enteric Pathogens Virginia Bioinformatics Institute Virginia Polytechnic Institute and University Blacksburg VA USA

Clinical symptoms of gastrointestinal (GI) infections are often caused by the inflammatory response elicited to eliminate the invading microbe. Here we present ENISI, a simulator of GI immune mechanisms in response to resident commensal bacteria as well as invading pathogens and the effect on host clinical symptoms. ENISI is a tool for identifying treatment strategies that reduce inflammation-induced damage and, at the same time, ensure pathogen removal by allowing one to test plausibility of in vitro observed behavior as explanations for observations in vivo, propose behaviors not yet tested in vitro that could explain these tissue-level observations, and conduct low-cost, preliminary experiments of proposed interventions/ treatments. An example of such application is shown in which we simulate dysentery resulting from B. hyodysenteriae infection and identify aspects of the host immune pathways that lead to continued inflammation-induced tissue damage even after pathogen elimination.

关键词： Immune system Microorganisms Automata Pathogens Blood Tissue damage Computational modeling

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Inside Back Cover: The Reaction Coordinate of a Bacterial GH47 α-Mannosidase: A Combined Quantum Mechanical and Structural Approach (Angew. Chem. Int. Ed. 44/2012)

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Angewandte Chemie International Edition 2012年第44期51卷

作者： Andrew J. Thompson Dr. Jerome Dabin Javier Iglesias-Fernández Dr. Albert Ardèvol Dr. Zoran Dinev Assoc. Prof. Spencer J. Williams Dr. Omprakash Bande Dr. Aloysius Siriwardena Carl Moreland Dr. Ting-Chou Hu David K. Smith Prof. Harry J. Gilbert Prof. Carme Rovira Prof. Gideon J. Davies Department of Chemistry University of York Heslington York YO10 5DD (UK) Computer Simulation & Modeling Laboratory Parc Científic de Barcelona Baldiri Reixac 4 08028 Barcelona (Spain) Institut de Química Teòrica i Computacional (IQTCUB) (Spain) Present address: Departament de Química Orgànica Universitat de Barcelona Martí i Franquès 1 08028 Barcelona (Spain) Present address: Department of Chemistry and Applied Biosciences ETH Zurich USI Campus Via Giuseppe Buffi 13 CH-6900 Lugano (Switzerland) School of Chemistry and Bio21 Molecular Science and Biotechnology Institute University of Melbourne Parkville (Australia) Université de Picardie Jules Vernes Laboratoire des Glucides (CNRS-FRE 3617) 33 Rue Saint Leu 80039 Amiens (France) Institute for Cell and Molecular Biosciences The Medical School Newcastle University Framlington Place Newcastle upon Tyne NE2 4HH (UK) Institució Catalana de Recerca i Estudis Avançats (ICREA) Passeig Lluís Companys 23 08020 Barcelona (Spain) Carme Rovira Computer Simulation & Modeling Laboratory Parc Científic de Barcelona Baldiri Reixac 4 08028 Barcelona (Spain) Gideon J. Davies Department of Chemistry University of York Heslington York YO10 5DD (UK)

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Innenrücktitelbild: The Reaction Coordinate of a Bacterial GH47 α‐Mannosidase: A Combined Quantum Mechanical and Structural Approach (Angew. Chem. 44/2012)

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Angewandte Chemie 2012年第44期124卷

作者： Andrew J. Thompson Jerome Dabin Javier Iglesias‐Fernández Albert Ardèvol Zoran Dinev Spencer J. Williams Omprakash Bande Aloysius Siriwardena Carl Moreland Ting‐Chou Hu David K. Smith Harry J. Gilbert Carme Rovira Gideon J. Davies Department of Chemistry University of York Heslington York YO10 5DD (UK) Computer Simulation & Modeling Laboratory Parc Científic de Barcelona Baldiri Reixac 4 08028 Barcelona (Spain) Institut de Química Teòrica i Computacional (IQTCUB) (Spain) Present address: Departament de Química Orgànica Universitat de Barcelona Martí i Franquès 1 08028 Barcelona (Spain) Present address: Department of Chemistry and Applied Biosciences ETH Zurich USI Campus Via Giuseppe Buffi 13 CH‐6900 Lugano (Switzerland) School of Chemistry and Bio21 Molecular Science and Biotechnology Institute University of Melbourne Parkville (Australia) Université de Picardie Jules Vernes Laboratoire des Glucides (CNRS‐FRE 3617) 33 Rue Saint Leu 80039 Amiens (France) Institute for Cell and Molecular Biosciences The Medical School Newcastle University Framlington Place Newcastle upon Tyne NE2 4HH (UK) Institució Catalana de Recerca i Estudis Avançats (ICREA) Passeig Lluís Companys 23 08020 Barcelona (Spain)

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The Reaction Coordinate of a Bacterial GH47 α‐Mannosidase: A Combined Quantum Mechanical and Structural Approach†

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Angewandte Chemie 2012年第44期124卷

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Ion beam nanobiology

Ion beam nanobiology

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IEEE International Nanoelectronics Conference (INEC)

作者： L.D. Yu P. Nimmanpipug V.S. Lee S. Anuntalabhochai Plasma and Beam Physics Research Facility Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai Thailand Computer Simulation and Modeling Laboratory Department of Chemistry Faculty of Science Chiang Mai University Chiang Mai Thailand Molecular Biology Laboratory Department of Biology Faculty of Science Chiang Mai University Chiang Mai Thailand

In ion implantation in materials, when the ion energy is sufficiently low, the ion interaction with materials is in the nanoscale. When low-energy ion beam irradiates biological living materials such as cells and DNA, the nanoscaled ion interaction with the biological objects is a novelty. Theoretical calculation demonstrates the low-energy ion range in DNA being in the order of nanometer. In experiments, keV ions were applied to irradiate naked DNA to investigate primary effects of the low-energy ion interaction with DNA, which would be served as a basis for further investigations on mechanisms involved in ion beam induced mutation of biological species. Preliminary results showed that the nanoscale low-energy ion interaction with DNA could indeed induce changes in the DNA forms. DNA transfer in bacteria demonstrated mutation occurred after low-energy ion irradiation of the DNA. In computer molecular dynamics simulation, 1-100-eV ions bombarded A-DNA and revealed DNA changes occurring in nanoscale with preferential consequence.

关键词： Ion beams Nanobioscience Biological materials Genetic mutations Ion implantation Cells (biology) Microorganisms Biology computing DNA computing Biological system modeling

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