检索结果-内蒙古大学图书馆

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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PLUMED Tutorials: a collaborative, community-driven learning ecosystem

arXiv

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arXiv 2024年

作者： Tribello, Gareth A. Bonomi, Massimiliano Bussi, Giovanni Camilloni, Carlo Armstrong, Blake I. Arsiccio, Andrea Aureli, Simone Ballabio, Federico Bernetti, Mattia Bonati, Luigi Brookes, Samuel G.H. Faidon Brotzakis, Z. Capelli, Riccardo Ceriotti, Michele Chan, Kam-Tung Cossio, Pilar Dasetty, Siva Donadio, Davide Ensing, Bernd Ferguson, Andrew L. Fraux, Guillaume Gale, Julian D. Gervasio, Francesco Luigi Giorgino, Toni Herringer, Nicholas S.M. Hocky, Glen M. Hoff, Samuel E. Invernizzi, Michele Languin-Cattoën, Olivier Leone, Vanessa Limongelli, Vittorio Lopez-Acevedo, Olga Marinelli, Fabrizio Martinez, Pedro Febrer Masetti, Matteo Mehdi, Shams Michaelides, Angelos Murtada, Mhd Hussein Parrinello, Michele Piaggi, Pablo M. Pietropaolo, Adriana Pietrucci, Fabio Pipolo, Silvio Pritchard, Claire Raiteri, Paolo Raniolo, Stefano Rapetti, Daniele Rizzi, Valerio Rydzewski, Jakub Salvalaglio, Matteo Schran, Christoph Seal, Aniruddha Zadeh, Armin Shayesteh Silva, Tomás F.D. Spiwok, Vojtěch Stirnemann, Guillaume Sucerquia, Daniel Tiwary, Pratyush Valsson, Omar Vendruscolo, Michele Voth, Gregory A. White, Andrew D. Wu, Jiangbo Centre for Quantum Materials and Technology School of Mathematics and Physics Queen’s University Belfast United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Via Bonomea 265 Trieste34136 Italy Department of Biosciences University of Milano Milano Italy School of Molecular and Life Sciences Curtin University GPO Box U1987 PerthWA6845 Australia Coriolis Pharma Fraunhoferstrasse 18b Martinsried82152 Germany School of Pharmaceutical Sciences University of Geneva Rue Michel-Servet 1 GenevaCH-1206 Switzerland Institute of Pharmaceutical Sciences of Western Switzerland University of Geneva GenevaCH-1206 Switzerland Swiss Bioinformatics Institute University of Geneva GenevaCH-1206 Switzerland Via Celoria 26 Milan20133 Italy Department of Biomolecular Sciences University of Urbino "Carlo Bo" Piazza Rinascimento 6 Urbino61029 Italy Department of Pharmacy and Biotechnology Alma Mater Studiorum University of Bologna Via Belmeloro 6 Bologna40126 Italy Atomistic Simulations Italian Institute of Technology Genova Italy Yusuf Hamied Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Institute for Bioinnovation Biomedical Sciences Research Center "Alexander Fleming" Vari16672 Greece Laboratory of Computational Science and Modeling Institut des Matériaux École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Chemistry University of California Davis DavisCA95616 United States Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States Center for Computational Biology Flatiron Institute New YorkNY10010 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States AI4Science Lab Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Computational Chemistry Group Van’t Hoff Institute for Molecular Sciences Unive

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce "PLUMED Tutorials", a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources. © 2024, CC BY.

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Correction to: On the Role of a Conserved Methionine in the Na+-Coupling Mechanism of a Neurotransmitter Transporter Homolog

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Neurochemical research 2022年第1期47卷 176页

作者： Wenchang Zhou Gianluca Trinco Dirk J Slotboom Lucy R Forrest José D Faraldo-Gómez Theoretical Molecular Biophysics Laboratory National Heart Lung and Blood Institute National Institutes of Health Bethesda MD 20892 USA. Groningen Biomolecular Sciences and Biotechnology Institute Zernike Institute for Advanced Materials University of Groningen Groningen The Netherlands. Groningen Biomolecular Sciences and Biotechnology Institute Zernike Institute for Advanced Materials University of Groningen Groningen The Netherlands. d.j.slotboom@rug.nl. Computational Structural Biology Section National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda MD 20892 USA. lucy.forrest@nih.gov. Theoretical Molecular Biophysics Laboratory National Heart Lung and Blood Institute National Institutes of Health Bethesda MD 20892 USA. jose.faraldo@nih.gov.

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Author Correction: Unidirectional single-file transport of full-length proteins through a nanopore

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Nature biotechnology 2023年第10期41卷 1483页

作者： Luning Yu Xinqi Kang Fanjun Li Behzad Mehrafrooz Amr Makhamreh Ali Fallahi Joshua C Foster Aleksei Aksimentiev Min Chen Meni Wanunu Department of Physics Northeastern University Boston MA USA. Department of Bioengineering Northeastern University Boston MA USA. Department of Chemistry University of Massachusetts at Amherst Amherst MA USA. Center for Biophysics and Computational Biology University of Illinois at Urbana-Champaign Urbana IL USA. Molecular and Cellular Biology Program University of Massachusetts at Amherst Amherst MA USA. Department of Physics University of Illinois at Urbana-Champaign Urbana IL USA. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana IL USA. Department of Physics Northeastern University Boston MA USA. wanunu@neu.edu. Department of Bioengineering Northeastern University Boston MA USA. wanunu@neu.edu. Chemistry and Chemical Biology Northeastern University Boston MA USA. wanunu@neu.edu.

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Cryo-FIB Lamella Milling: A Comprehensive Technique to Prepare Samples of Both Plunge- and High-pressure Frozen-hydrated Specimens for in situ Studies.

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Microscopy and Microanalysis 2018年第S1期24卷 820-821页

作者： Miroslava Schaffer Stefan Pfeffer Stephan Kleindiek Julia Mahamid Michael Heymann Andrew J. Smith Tim Laugks Benjamin D. Engel Sahradha Albert Wolfgang Baumeister Max Planck Institute of Biochemistry Dep. of Molecular Structural Biology Martinsried Germany Kleindiek Nanotechnik GmbH Markwiesenstraße 55 Reutlingen Germany Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg Max Planck Institute of Biochemistry Dep. of Cellular and Molecular Biophysics Martinsried Germany

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TOPAZ: A Positive-Unlabeled Convolutional Neural Network CryoEM Particle Picker that can Pick Any Size and Shape Particle

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Microscopy and Microanalysis 2019年第S2期25卷 986-987页

作者： Tristan Bepler Andrew Morin Micah Rapp Julia Brasch Lawrence Shapiro Alex J Noble Bonnie Berger Computational and Systems Biology MIT Cambridge MA USA. Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA USA. Department of Mathematics MIT Cambridge MA USA. Department of Biochemistry and Molecular Biophysics Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University NY NY USA. National Resource for Automated Molecular Microscopy Simons Electron Microscopy Center New York Structural Biology Center NY NY USA.

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Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

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iScience 2023年第4期26卷 106460页

作者： Su, Chang Hou, Yu Zhou, Manqi Rajendran, Suraj Maasch, Jacqueline R.M. A. Abedi, Zehra Zhang, Haotan Bai, Zilong Cuturrufo, Anthony Guo, Winston Chaudhry, Fayzan F. Ghahramani, Gregory Tang, Jian Cheng, Feixiong Li, Yue Zhang, Rui DeKosky, Steven T. Bian, Jiang Wang, Fei Department of Health Service Administration and Policy College of Public Health Temple University Philadelphia 19122 PA United States Department of Population Health Sciences Weill Cornell Medicine New York 10065 NY United States Department of Surgery University of Minnesota Minneapolis 55455 MN United States Department of Computational Biology Cornell University Ithaca 14850 NY United States Tri-Institutional Computational Biology & Medicine Program Cornell University New York 10065 NY United States Department of Computer Science Cornell Tech New York 10044 NY United States Department of Physiology and Biophysics Weill Cornell Medicine New York 10065 NY United States Computer Science Cornell University Ithaca 14850 NY United States Department of Medicine Weill Cornell Medicine New York 10021 NY United States Mila-Quebec AI Institute and HEC Montreal Montreal H2S 3H1 QC Canada Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland 44195 OH United States Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland 44195 OH United States Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland 44106 OH United States School of Computer Science McGill University Montreal H3A 0C6 QC Canada Department of Neurology College of Medicine University of Florida Gainesville 32610 FL United States Department of Health Outcomes & Biomedical Informatics College of Medicine University of Florida Gainesville 32610 FL United States

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available. © 2023 The Authors

关键词： Bioinformatics Drugs Systems biology

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Development of diagnostic and prognostic molecular biomarkers in hepatocellular carcinoma using machine learning: A systematic review

Liver Cancer International

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Liver Cancer International 2023年第4期3卷

作者： Amanpreet Brar Alice Zhu Cristina Baciu Divya Sharma Wei Xu Ani Orchanian-Cheff Bo Wang Jüri Reimand Robert Grant Mamatha Bhat Department of Surgery University of Toronto Toronto Canada Ajmera Transplant Program University Health Network Toronto General Hospital Toronto Canada Department of Biostatistics Dalla Lana School of Public Health Princess Margaret Cancer Centre University Health Network Toronto Ontario Canada Library and Information Services University Health Network Toronto Ontario Canada Vector Institute for Artificial Intelligence University of Toronto Toronto Canada Computational Biology Program Ontario Institute for Cancer Research Toronto Canada Department of Medical Biophysics University of Toronto Toronto Canada Department of Molecular Genetics University of Toronto Toronto Canada Department of Medical Oncology Princess Margaret Cancer Centre University Health Network Toronto Ontario Canada Division of Gastroenterology & Hepatology Department of Medicine Temerty Faculty of Medicine University of Toronto Toronto Canada

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality and morbidity worldwide. Machine learning (ML) tools have been developed in recent years to generate diagnostic and prognostic molecular biomarkers for this high-fatality cancer. To delineate the landscape of ML in HCC, we performed a systematic search of Ovid Medline, Ovid Embase, Cochrane Database of Systematic Reviews (Ovid) and Cochrane CENTRAL (Ovid) to identify studies of HCC molecular biomarkers using ML strategies. In total, 75 studies met our inclusion criteria, 53 of which were pertinent to diagnosis of HCC and 22 of which were pertinent to prognostication of HCC. Genomic, transcriptomic, epigenomic, proteomic and metabolomic signatures were derived using various ML techniques (supervised, unsupervised and deep learning approaches) using serum, urine and tissue samples of HCC. The ML algorithms achieved a sensitivity of up to 95% for the diagnosis of HCC. Through pathway analysis of the signatures derived by ML tools, we identified regulators of epithelial-mesenchymal transition and the cancer pathway Ras/Raf/MAPK as being particularly prognostic of HCC outcome. The application of ML to molecular data in HCC has thus far resulted in the generation of highly sensitive diagnostic and prognostic signatures. In future, development of ML algorithms that incorporate clinical, laboratory, alongside molecular features will be needed to fulfil the promise of personalized HCC diagnosis and treatment.

关键词： diagnostic marker hepatocellular carcinoma machine learning molecular markers prognostic marker

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DNA repair after oxidative stress: Current challenges

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Current Opinion in Toxicology 2018年 7卷 9-16页

作者： Van Houten, Bennett Santa-Gonzalez, Gloria A. Camargo, Mauricio Program in Molecular Biophysics and Structural Biology University of Pittsburgh Pittsburgh 15261 PA United States Hillman Cancer Center Pittsburgh 15213 PA United States Department of Pharmacology and Chemical Biology University of Pittsburgh Pittsburgh 15261 PA United States University Research Center and Biology Institute Genetics Regeneration and Cancer Laboratory SIU Lab 432 Universidad de Antioquia Medellin Colombia

Reactive oxygen and nitrogen species damage cellular macromolecules including DNA. Cells have a robust base excision repair pathway to deal with this damage in both nuclear and mitochondrial genomes. However, mitochondria lack nucleotide excision repair. Evidence suggests that chronic oxidative stress can induce protective pathways lowering genotoxicity. Understanding oxidant injury to DNA and its repair is critical for our understanding the pathophysiology of a wide range of human disorders. © 2017 Elsevier B.V.

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Using physical features of protein core packing to distinguish real proteins from decoys

arXiv

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arXiv 2020年

作者： Grigas, Alex T. Mei, Zhe Treado, John D. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute signifficantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (speciffically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

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