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蛋白质构象集合分析方法的发展回顾。

A Retrospective on the Development of Methods for the Analysis of Protein Conformational Ensembles.

机构信息

Laboratory for Computational Biology, School of Computing Sciences, University of East Anglia, Norwich, UK.

出版信息

Protein J. 2023 Jun;42(3):181-191. doi: 10.1007/s10930-023-10113-9. Epub 2023 Apr 19.

Abstract

Analysing protein conformational ensembles whether from molecular dynamics (MD) simulation or other sources for functionally relevant conformational changes can be very challenging. In the nineteen nineties dimensional reduction methods were developed primarily for analysing MD trajectories to determine dominant motions with the aim of understanding their relationship to function. Coarse-graining methods were also developed so the conformational change between two structures could be described in terms of the relative motion of a small number of quasi-rigid regions rather than in terms of a large number of atoms. When these methods are combined, they can characterize the large-scale motions inherent in a conformational ensemble providing insight into possible functional mechanism. The dimensional reduction methods first applied to protein conformational ensembles were referred to as Quasi-Harmonic Analysis, Principal Component Analysis and Essential Dynamics Analysis. A retrospective on the origin of these methods is presented, the relationships between them explained, and more recent developments reviewed.

摘要

分析蛋白质构象集合,无论是来自分子动力学(MD)模拟还是其他来源,以寻找与功能相关的构象变化,都是非常具有挑战性的。在 1990 年代,降维方法主要是为了分析 MD 轨迹而开发的,目的是确定主导运动,以了解它们与功能的关系。还开发了粗粒化方法,以便可以根据少数几个准刚性区域的相对运动来描述两个结构之间的构象变化,而不是根据大量原子来描述。当这些方法结合使用时,它们可以描述构象集合中的大规模运动,从而深入了解可能的功能机制。首先应用于蛋白质构象集合的降维方法被称为准谐分析、主成分分析和基本动力学分析。本文回顾了这些方法的起源,解释了它们之间的关系,并回顾了最近的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de4f/10264293/ba246ca3c114/10930_2023_10113_Fig1_HTML.jpg

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