Similarities between principal components of protein dynamics and random diffusion
Similarities between principal components of protein dynamics and random diffusion
复制标题
DOI：
10.1103/physreve.62.8438
复制
发表时间：
2000-12-01
影响因子：
2.4
通讯作者：
Hess, B
中科院分区：
文献类型：
--
作者：
Hess, B
主成分分析，也被称为基本动力学，是一个强大的工具，在原子模拟的大分子中寻找全球性的，相关的运动。它已经成为一种成熟的技术，用于分析蛋白质的分子动力学模拟。大蛋白质模拟的前几个主成分通常类似于余弦。我们推导出高维随机扩散的主成分，这是几乎完美的余弦。蛋白质模拟和噪声之间的这种相似性意味着，对于许多蛋白质，当前模拟的时间尺度太短，无法获得集体运动的收敛。
Principal component analysis, also called essential dynamics, is a powerful tool for finding global, correlated motions in atomic simulations of macromolecules. It has become an established technique for analyzing molecular dynamics simulations of proteins. The first few principal components of simulations of large proteins often resemble cosines. We derive the principal components for high-dimensional random diffusion, which are almost perfect cosines. This resemblance between protein simulations and noise implies that for many proteins the time scales of current simulations are too short to obtain convergence of collective motions.