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增强采样和蛋白质模拟的自由能计算。

Enhanced sampling and free energy calculations for protein simulations.

机构信息

Science for Life Laboratory, Department of Chemistry-BMC, Uppsala University, Uppsala, Sweden.

出版信息

Prog Mol Biol Transl Sci. 2020;170:177-213. doi: 10.1016/bs.pmbts.2020.01.006. Epub 2020 Feb 19.

Abstract

Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.

摘要

分子动力学模拟是一种强大的计算技术,用于研究生物分子系统,通过在原子尺度上提供与生物功能相关的结构动力学的深入了解,补充实验。它还可用于计算构象转变的自由能景观,以更好地理解生物分子的功能。然而,生物分子构象的采样受到需要克服的自由能势垒的限制,导致 MD 模拟达到的时间尺度与控制生物过程的时间尺度之间存在相当大的差距。为了解决这个问题,已经开发了许多增强采样方法来提高分子动力学模拟和自由能计算的采样效率。通常,增强采样算法可以分为基于集体变量 (CV-based) 的方法和不需要预定义 CV 的方法 (CV-free)。本章首先简要回顾了自由能估计的理论基础,然后回顾了最常见的基于 CV 和无 CV 的方法,包括一些示例和最新进展。最后,讨论了不同增强采样方法的组合。

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