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• W2331756548 abstract "Elastic network models (ENMs) are valuable tools for investigating collective motions of proteins, and a rich variety of simple models have been proposed over the past decade. A good representation of the collective motions requires a good approximation of the covariances between the fluctuations of the individual atoms. Nevertheless, most studies have validated such models only by the magnitudes of the single-atom fluctuations they predict. In the present study, we have quantified the agreement between the covariance structure predicted by molecular dynamics (MD) simulations and those predicted by a representative selection of proposed coarse-grained ENMs. We then contrast this approach with the comparison to MD-predicted atomic fluctuations and comparison to crystallographic B-factors. While all the ENMs yield approximations to the MD-predicted covariance structure, we report large and consistent differences between proposed models. We also find that the ability of the ENMs to predict atomic fluctuations is correlated with their ability to capture the covariance structure. In contrast, we find that the models that agree best with B-factors model collective motions less reliably and recommend against using B-factors as a benchmark." @default.
• W2331756548 created "2016-06-24" @default.
• W2331756548 creator A5008543195 @default.
• W2331756548 creator A5081877565 @default.
• W2331756548 creator A5090274388 @default.
• W2331756548 date "2013-11-21" @default.
• W2331756548 modified "2023-10-16" @default.
• W2331756548 title "Evaluation of Protein Elastic Network Models Based on an Analysis of Collective Motions" @default.
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• W2331756548 doi "https://doi.org/10.1021/ct400399x" @default.
• W2331756548 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26592296" @default.
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