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      RNA force field with accuracy comparable to state-of-the-art protein force fields

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          Significance

          The complex and often highly dynamic 3D structures of RNA molecules are central to their diverse cellular functions. Molecular dynamics (MD) simulations have played a major role in characterizing the structure and dynamics of proteins, but the physical models (“force fields”) used for simulating nucleic acids are substantially less accurate overall than those used in protein simulations, creating a major challenge for MD studies of RNA. Here, we report an RNA force field capable of describing the structural and thermodynamic properties of RNA molecules with accuracy comparable to state-of-the-art protein force fields. This force field should facilitate the use of MD simulation as a tool for the study of biologically significant RNA molecules and protein–RNA complexes.

          Abstract

          Molecular dynamics (MD) simulation has become a powerful tool for characterizing at an atomic level of detail the conformational changes undergone by proteins. The application of such simulations to RNA structures, however, has proven more challenging, due in large part to the fact that the physical models (“force fields”) available for MD simulations of RNA molecules are substantially less accurate in many respects than those currently available for proteins. Here, we introduce an extensive revision of a widely used RNA force field in which the parameters have been modified, based on quantum mechanical calculations and existing experimental information, to more accurately reflect the fundamental forces that stabilize RNA structures. We evaluate these revised parameters through long-timescale MD simulations of a set of RNA molecules that covers a wide range of structural complexity, including single-stranded RNAs, RNA duplexes, RNA hairpins, and riboswitches. The structural and thermodynamic properties measured in these simulations exhibited dramatically improved agreement with experimentally determined values. Based on the comparisons we performed, this RNA force field appears to achieve a level of accuracy comparable to that of state-of-the-art protein force fields, thus significantly advancing the utility of MD simulation as a tool for elucidating the structural dynamics and function of RNA molecules and RNA-containing biological assemblies.

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          Most cited references59

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          Molpro: a general-purpose quantum chemistry program package

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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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              Error estimates on averages of correlated data

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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                13 February 2018
                29 January 2018
                29 January 2018
                : 115
                : 7
                : E1346-E1355
                Affiliations
                [1] aD. E. Shaw Research , New York, NY 10036;
                [2] bDepartment of Biochemistry and Molecular Biophysics, Columbia University , New York, NY 10032
                Author notes

                Edited by Jennifer A. Doudna, University of California, Berkeley, CA, and approved December 21, 2017 (received for review July 26, 2017)

                Author contributions: D.T., S.P., R.M.D., and D.E.S. designed research; D.T. and R.M.D. performed research; D.T. analyzed data; and D.T., S.P., and D.E.S. wrote the paper.

                1Present address: Silicon Therapeutics, Boston, MA 02210.

                3Deceased February 3, 2015.

                Article
                201713027
                10.1073/pnas.1713027115
                5816156
                29378935
                8dc9aa5b-781f-40e9-9743-9e3844a91d51
                Copyright © 2018 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 10
                Categories
                PNAS Plus
                Physical Sciences
                Biophysics and Computational Biology
                PNAS Plus

                nucleic acid,molecular dynamics simulations,amber,anton,force field

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