Similarities between principal components of protein dynamics and random diffusion

Berk Hess
Phys. Rev. E 62, 8438 – Published 1 December 2000
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Abstract

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.

  • Received 11 February 2000

DOI:https://doi.org/10.1103/PhysRevE.62.8438

©2000 American Physical Society

Authors & Affiliations

Berk Hess

  • Department of Biophysical Chemistry, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands

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Vol. 62, Iss. 6 — December 2000

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