Principal component analysis: a method for determining the essential dynamics of proteins.
Principal component analysis: a method for determining the essential dynamics of proteins.
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DOI：
10.1007/978-1-62703-658-0_11
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Jacobs DJ
中科院分区：
文献类型：
--
作者：
David CC;Jacobs DJ
利用主成分分析来揭示蛋白质中最重要的运动已经变得很普遍。这种方法通常以其首字母缩写PCA而闻名。虽然大多数流行的分子动力学软件包不可避免地提供PCA工具来分析蛋白质轨迹，但研究人员经常在没有深入了解如何进行解释的情况下对其结果进行推断，并且他们通常没有意识到这种分析的局限性和普遍性。在这里，我们回顾了应用标准PCA的最佳实践，描述了有用的变体，讨论了为什么要进行比较研究，并描述了一组使比较成为可能的指标。在实践中，人们将被迫在没有所需数量的样品的情况下对蛋白质的基本动力学进行推断。因此，大量的时间花在描述如何判断结果的重要性上，突出了陷阱。从许多实际考虑的角度审查了PCA的主题，并提供了有用的配方。
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.