Gaussian Accelerated Molecular Dynamics in Drug Discovery

分子动力学 药物发现 毫秒 生物分子 计算生物学 微秒 高斯分布 生物系统 虚拟筛选 化学 计算机科学 纳米技术 计算化学 物理 生物 材料科学 天文 生物化学
作者
N. Hung,Jinan Wang,Keya Joshi,Kushal Koirala,Yinglong Miao
标识
DOI:10.1002/9783527840748.ch2
摘要

Chapter 2 Gaussian Accelerated Molecular Dynamics in Drug Discovery Hung N. Do, Hung N. Do Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorJinan Wang, Jinan Wang Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorKeya Joshi, Keya Joshi Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorKushal Koirala, Kushal Koirala Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorYinglong Miao, Yinglong Miao Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this author Hung N. Do, Hung N. Do Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorJinan Wang, Jinan Wang Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorKeya Joshi, Keya Joshi Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorKushal Koirala, Kushal Koirala Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this authorYinglong Miao, Yinglong Miao Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047 USASearch for more papers by this author Book Editor(s):Vasanthanathan Poongavanam, Vasanthanathan Poongavanam Uppsala University, Uppsala, 75105 SwedenSearch for more papers by this authorVijayan Ramaswamy, Vijayan Ramaswamy Univ. Texas MD Anderson Cancer Center, Houston, 77054 United StatesSearch for more papers by this author First published: 19 January 2024 https://doi.org/10.1002/9783527840748.ch2 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary This chapter summarizes recent methodological developments and application studies of Gaussian accelerated molecular dynamics (GaMD) in drug discovery. GaMD is an unconstrained enhanced sampling technique that allows for exploration of the conformational space of proteins and understanding of complex biological interactions that often occur on millisecond and longer timescales. The boost potential in GaMD usually exhibits a Gaussian distribution, enabling accurate reweighting of the simulations using cumulant expansion to the second order. Recently developed "selective GaMD" algorithms such as ligand GaMD (LiGaMD), peptide GaMD (Pep-GaMD), and protein–protein interaction GaMD (PPI-GaMD) have enabled microsecond-timescale all-atom simulations to characterize the binding thermodynamics and kinetics of small molecules, flexible peptides, and proteins. GaMD has been successfully applied to reveal the mechanisms of ligand/drug binding to various biomolecules (including GPCRs, nucleic acids, and human ACE2 receptors), as well as generating protein structures for virtual screening in drug discovery. References Karplus , M. and McCammon , J.A. ( 2002 ). Nat. Struct. Biol. 9 : 646 – 652 , https://doi.org/10.1038/Nsb0902-646 . 10.1038/nsb0902-646 CASPubMedWeb of Science®Google Scholar Hollingsworth , S. and Dror , R. ( 2018 ). Neuron 99 : 1129 – 1143 . 10.1016/j.neuron.2018.08.011 CASPubMedWeb of Science®Google Scholar Wang , J. et al. ( 2021 ). WIREs Comput. Mol. 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