title: Validating and improving elastic network models with molecular dynamics simulations.
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.
authors.affiliation: Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, New York 14642, USA.
authors.forename: Tod D
authors.initials: TD
authors.lastname: Romo
authors.forename: Alan
authors.initials: A
authors.lastname: Grossfield
mesh_terms: D003198:Computer Simulation; D008958:Models, Molecular; D025341:Principal Component Analysis; D043885:Receptor, Cannabinoid, CB2 / Q000737:chemistry; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry*; D012243:Rhodopsin / Q000737:chemistry
chemical_list: D043885:Receptor, Cannabinoid, CB2; D018343:Receptors, Adrenergic, beta-2; D043562:Receptors, G-Protein-Coupled; D012243:Rhodopsin