130773 20240201145618.0 doi 10.1021/acs.jcim.9b00430 sideral 114299 ART-2019-114299 eng Galano-Frutos, Juan José Universidad de Zaragoza (orcid)0000-0002-1896-7805 Accurate Calculation of Barnase and SNase Folding Energetics Using Short Molecular Dynamics Simulations and an Atomistic Model of the Unfolded Ensemble: Evaluation of Force Fields and Water Models 2019 Access copy available to the general public Unrestricted As proteins perform most cellular functions, quantitative understanding of protein energetics is required to gain control of biological phenomena. Accurate models of native proteins can be obtained experimentally, but the lack of equally fine models of unfolded ensembles impedes the calculation of protein folding energetics from first principles. Here, we show that an atomistic unfolded ensemble model, consisting of a few dozen conformations built from a protein sequence, can be used in conjunction with an X-ray structure of its native state to calculate accurately by difference the changes in enthalpy and heat capacity of the polypeptide upon folding. The calculation is done using molecular dynamics simulations, popular force fields, and water models, and for the two model proteins studied (barnase and SNase), the results agree within error or are very close to their experimentally determined properties. The enthalpy sampling of the unfolded ensemble is done through short 2 ns simulations that do not significantly modify the representative distribution of R<inf>g</inf> of the starting conformations. The impressive accuracy obtained opens the possibility to investigate quantitatively systems or phenomena not amenable to experiment and paves the way for addressing the calculation of protein conformational stability (i.e., the change in Gibbs energy upon folding), a central goal of structural biology. So far, these calculated enthalpy and heat capacity changes, combined with the experimentally determined melting temperatures of the corresponding protein, allow us to reproduce the stability curves of both barnase and SNase. info:eu-repo/grantAgreement/ES/MINECO/BFU2016-78232-P info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 4.549 2019 CHEMISTRY, MEDICINAL 11 / 61 = 0.18 2019 Q1 T1 COMPUTER SCIENCE, INFORMATION SYSTEMS 28 / 155 = 0.181 2019 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 17 / 109 = 0.156 2019 Q1 T1 CHEMISTRY, MULTIDISCIPLINARY 49 / 176 = 0.278 2019 Q2 T1 1.329 2019 Chemical Engineering (miscellaneous) 2019 Q1 Library and Information Sciences 2019 Q1 Computer Science Applications 2019 Q1 Chemistry (miscellaneous) 2019 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Sancho, Javier Universidad de Zaragoza (orcid)0000-0002-2879-9200 1002 060 Universidad de Zaragoza Dpto. Bioq.Biolog.Mol. Celular Área Bioquímica y Biolog.Mole. (2019), [11 pp] J. Chem Inf. Model. Journal of Chemical Information and Modeling 1549-9596 1680996 http://zaguan.unizar.es/record/130773/files/texto_completo.pdf Postprint 790813 http://zaguan.unizar.es/record/130773/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:130773 articulos driver 2024-02-01-14:52:27 ARTICLE