Charles C David1, Donald J Jacobs. 1. Department of Physics and Optical Science, University of North Carolina, Charlotte, NC, USA.
Abstract
It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided.
It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamicspackages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metripan class="Chemical">cs that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided.
Authors: J Alfredo Freites; Karin L Németh-Cahalan; James E Hall; Douglas J Tobias Journal: Biochim Biophys Acta Biomembr Date: 2019-02-22 Impact factor: 3.747
Authors: Bela Farago; Iain D Nicholl; Shen Wang; Xiaolin Cheng; David J E Callaway; Zimei Bu Journal: Proc Natl Acad Sci U S A Date: 2021-03-30 Impact factor: 11.205