ArticleMethods in molecular biology (Clifton, N.J.)2014
Principal component analysis: a method for determining the essential dynamics of proteins.
Article in Methods in molecular biology (Clifton, N.J.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 468 papers.
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468 citing papers in PubMed, 1,196 citations in OpenAlex.
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408 more citing papers are in PubMed but not listed here.
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2 authors at 1 institution in 1 country.
Funding
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.
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