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      Systematic Validation of Protein Force Fields against Experimental Data

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          Abstract

          Molecular dynamics simulations provide a vehicle for capturing the structures, motions, and interactions of biological macromolecules in full atomic detail. The accuracy of such simulations, however, is critically dependent on the force field—the mathematical model used to approximate the atomic-level forces acting on the simulated molecular system. Here we present a systematic and extensive evaluation of eight different protein force fields based on comparisons of experimental data with molecular dynamics simulations that reach a previously inaccessible timescale. First, through extensive comparisons with experimental NMR data, we examined the force fields' abilities to describe the structure and fluctuations of folded proteins. Second, we quantified potential biases towards different secondary structure types by comparing experimental and simulation data for small peptides that preferentially populate either helical or sheet-like structures. Third, we tested the force fields' abilities to fold two small proteins—one α-helical, the other with β-sheet structure. The results suggest that force fields have improved over time, and that the most recent versions, while not perfect, provide an accurate description of many structural and dynamical properties of proteins.

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          How fast-folding proteins fold.

          An outstanding challenge in the field of molecular biology has been to understand the process by which proteins fold into their characteristic three-dimensional structures. Here, we report the results of atomic-level molecular dynamics simulations, over periods ranging between 100 μs and 1 ms, that reveal a set of common principles underlying the folding of 12 structurally diverse proteins. In simulations conducted with a single physics-based energy function, the proteins, representing all three major structural classes, spontaneously and repeatedly fold to their experimentally determined native structures. Early in the folding process, the protein backbone adopts a nativelike topology while certain secondary structure elements and a small number of nonlocal contacts form. In most cases, folding follows a single dominant route in which elements of the native structure appear in an order highly correlated with their propensity to form in the unfolded state.
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            How robust are protein folding simulations with respect to force field parameterization?

            Molecular dynamics simulations hold the promise of providing an atomic-level description of protein folding that cannot easily be obtained from experiments. Here, we examine the extent to which the molecular mechanics force field used in such simulations might influence the observed folding pathways. To that end, we performed equilibrium simulations of a fast-folding variant of the villin headpiece using four different force fields. In each simulation, we observed a large number of transitions between the unfolded and folded states, and in all four cases, both the rate of folding and the structure of the native state were in good agreement with experiments. We found, however, that the folding mechanism and the properties of the unfolded state depend substantially on the choice of force field. We thus conclude that although it is important to match a single, experimentally determined structure and folding rate, this does not ensure that a given simulation will provide a unique and correct description of the full free-energy surface and the mechanism of folding. Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.
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              Long-timescale molecular dynamics simulations of protein structure and function.

              Molecular dynamics simulations allow for atomic-level characterization of biomolecular processes such as the conformational transitions associated with protein function. The computational demands of such simulations, however, have historically prevented them from reaching the microsecond and greater timescales on which these events often occur. Recent advances in algorithms, software, and computer hardware have made microsecond-timescale simulations with tens of thousands of atoms practical, with millisecond-timescale simulations on the horizon. This review outlines these advances in high-performance molecular dynamics simulation and discusses recent applications to studies of protein dynamics and function as well as experimental validation of the underlying computational models.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                22 February 2012
                : 7
                : 2
                : e32131
                Affiliations
                [1 ]D. E. Shaw Research, New York, New York, United States of America
                [2 ]Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
                Swiss Federal Institute of Technology Zurich, Switzerland
                Author notes

                Conceived and designed the experiments: KL-L PM SP MPE ROD DES. Performed the experiments: KL-L PM SP. Analyzed the data: KL-L PM SP. Contributed reagents/materials/analysis tools: KL-L PM SP. Wrote the paper: KL-L PM SP ROD DES.

                Article
                PONE-D-11-21577
                10.1371/journal.pone.0032131
                3285199
                22384157
                7ea0956c-848f-4a35-80e4-48f7c8298bcb
                Lindorff-Larsen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 1 November 2011
                : 24 January 2012
                Page count
                Pages: 6
                Categories
                Research Article
                Biology
                Biochemistry
                Biophysics
                Computational Biology
                Macromolecular Structure Analysis
                Chemistry
                Computational Chemistry
                Physical Chemistry
                Theoretical Chemistry
                Physics
                Biophysics

                Uncategorized
                Uncategorized

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