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大分子衍射的分子动力学模拟,第二部分:蛋白质晶体模拟分析。

Molecular-dynamics simulations of macromolecular diffraction, part II: Analysis of protein crystal simulations.

机构信息

Computer, Computational and Statistical Sciences Division, Los Alamos, NM, United States; Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, United States.

Computer, Computational and Statistical Sciences Division, Los Alamos, NM, United States.

出版信息

Methods Enzymol. 2023;688:115-143. doi: 10.1016/bs.mie.2023.06.012. Epub 2023 Aug 18.

Abstract

Molecular-dynamics (MD) simulations have contributed substantially to our understanding of protein structure and dynamics, yielding insights into many biological processes including protein folding, drug binding, and mechanisms of protein-protein interactions. Much of what is known about protein structure comes from macromolecular crystallography (MX) experiments. MD simulations of protein crystals are useful in the study of MX because the simulations can be analyzed to calculate almost any crystallographic observable of interest, from atomic coordinates to structure factors and densities, B-factors, multiple conformations and their populations/occupancies, and diffuse scattering intensities. Computing resources and software to support crystalline MD simulations are now readily available to many researchers studying protein structure and dynamics and who may be interested in advanced interpretation of MX data, including diffuse scattering. In this work, we outline methods of analyzing MD simulations of protein crystals and provide accompanying Jupyter notebooks as practical resources for researchers wishing to perform similar analyses on their own systems of interest.

摘要

分子动力学(MD)模拟在理解蛋白质结构和动力学方面做出了重要贡献,为许多生物过程提供了深入的认识,包括蛋白质折叠、药物结合以及蛋白质-蛋白质相互作用的机制。我们对蛋白质结构的了解很大程度上来自于大分子晶体学(MX)实验。蛋白质晶体的 MD 模拟在 MX 研究中很有用,因为可以对模拟进行分析,以计算几乎任何感兴趣的晶体学可观测值,从原子坐标到结构因子和密度、B 因子、多种构象及其丰度/占有率,以及漫散射强度。现在,许多研究蛋白质结构和动力学的研究人员都可以轻松获得支持晶体 MD 模拟的计算资源和软件,并且他们可能对 MX 数据的高级解释(包括漫散射)感兴趣。在这项工作中,我们概述了分析蛋白质晶体 MD 模拟的方法,并提供了相应的 Jupyter 笔记本,作为希望对自己感兴趣的系统进行类似分析的研究人员的实用资源。

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