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• W2078646305 abstract "Elastic network models (ENMs) are a class of simple models intended to represent the collective motions of proteins. In contrast to all-atom molecular dynamics simulations, the low computational investment required to use an ENM makes them ideal for speculative hypothesis-testing situations. Historically, ENMs have been validated via comparison to crystallographic B-factors, but this comparison is relatively low-resolution and only tests the predictions of relative flexibility. In this work, we systematically validate and optimize a number of ENM-type models by quantitatively comparing their predictions to microsecond-scale all-atom simulations of three different G protein coupled receptors. We show that, despite their apparent simplicity, well-optimized ENMs perform remarkably well, reproducing the protein fluctuations with an accuracy comparable to what one would expect from all-atom simulations run for several hundred nanoseconds." @default.
• W2078646305 created "2016-06-24" @default.
• W2078646305 creator A5041647675 @default.
• W2078646305 creator A5073891484 @default.
• W2078646305 date "2010-09-24" @default.
• W2078646305 modified "2023-10-09" @default.
• W2078646305 title "Validating and improving elastic network models with molecular dynamics simulations" @default.
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• W2078646305 doi "https://doi.org/10.1002/prot.22855" @default.
• W2078646305 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20872850" @default.
• W2078646305 hasPublicationYear "2010" @default.
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