title: Accurately modeling nanosecond protein dynamics requires at least microseconds of simulation.
abstract: Advances in hardware and algorithms have greatly extended the timescales accessible to molecular simulation. This article assesses whether such long timescale simulations improve our ability to calculate order parameters that describe conformational heterogeneity on ps-ns timescales or if force fields are now a limiting factor. Order parameters from experiment are compared with order parameters calculated in three different ways from simulations ranging from 10 ns to 100 μs in length. Importantly, bootstrapping is employed to assess the variability in results for each simulation length. The results of 10-100 ns timescale simulations are highly variable, possibly explaining the variation in levels of agreement between simulation and experiment in published works examining different proteins. Fortunately, microsecond timescale simulations improve both the accuracy and precision of calculated order parameters, at least so long as the full exponential fit or truncated average approximation is used instead of the common long-time limit approximation. The improved precision of these long timescale simulations allows a statistically sound comparison of a number of modern force fields, such as Amber03, Amber99sb-ILDN, and Charmm27. While there is some variation between these force fields, they generally give very similar results for sufficiently long simulations. The fact that so much simulation is required to precisely capture ps-ns timescale processes suggests that extremely extensive simulations are required for slower processes. Advanced sampling techniques could aid greatly, however, such methods will need to maintain accurate kinetics if they are to be of value for calculating dynamical properties like order parameters. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Department of Biochemistry & Molecular Biophysics, Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University School of Medicine, St. Louis, Missouri, 63110.
authors.forename: Gregory R
authors.identifier: http://orcid.org/0000-0002-2083-4892
authors.initials: GR
authors.lastname: Bowman
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000737:chemistry / Q000201:enzymology; D006801:Humans; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012260:Ribonucleases / Q000737:chemistry; D013997:Time Factors; D025801:Ubiquitin / Q000737:chemistry
chemical_list: D011506:Proteins; D025801:Ubiquitin; D012260:Ribonucleases
keywords: conformational heterogeneity; enhanced sampling; force fields; molecular dynamics; order parameters