Machine learning coarse-grained potentials of protein thermodynamics

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• dc.contributor.author Majewski, Maciej
• dc.contributor.author Pérez, Adrià
• dc.contributor.author Thölke, Philipp
• dc.contributor.author Doerr, Stefan, 1987-
• dc.contributor.author Charron, Nicholas E.
• dc.contributor.author Giorgino, Toni
• dc.contributor.author Husic, Brooke E.
• dc.contributor.author Clementi, Cecilia
• dc.contributor.author Noé, Frank
• dc.contributor.author De Fabritiis, Gianni
• dc.date.accessioned 2023-10-17T06:18:48Z
• dc.date.available 2023-10-17T06:18:48Z
• dc.date.issued 2023
• dc.description.abstract A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.
• dc.description.sponsorship The project PID2020-116564GB-I00 has been funded by MCIN/AEI/10.13039/501100011033 (G.D.F.) This project has received funding from the Torres-Quevedo Program from the Spanish National Agency for Research (PTQ2020-011145/AEI/10.13039/501100011033) (M.M.); the Torres-Quevedo Program from the Spanish National Agency for Research (PTQ2021-011669/AEI/10.13039/501100011033) (A.P.); the European Union’s Horizon 2020 research and innovation program under grant agreement No. 823712 (G.D.F.); NLM Training Program in Biomedical Informatics and Data Science (grant no. 5T15LM007093-27) (N.E.C.); Deutsche Forschungsgemeinschaft (DFG, GRK DAEDALUS, RTG 2433, Project Q05) (N.E.C.); National Science Foundation (CHE-1900374 and PHY-2019745) (C.C.); Einstein Foundation Berlin (Project 0420815101) (C.C.); Deutsche Forschungsgemeinschaft (DFG) SFB 1114 projects A04, B03, and B08, SFB/TRR 186 project A12, and SFB 1078 project C7 (C.C.); Deutsche Forschungsgemeinschaft (DFG) projects CRC1114/A04, CRC1114/C03 (F.N.); European Research Council (ERC) project ERG CoG 772230 (F.N.); Berlin Mathematics Center MATH+ project AA1-6 (F.N.); the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health under award number GM140090 (G.D.F.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the volunteers of GPUGRID.net for donating computing time. T.G. acknowledges the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support. This project has received funding from the Spoke 7 of the National Centre for HPC, Big Data and Quantum Computing (CN00000013) of the NextGenerationEU initiative (T.G.).
• dc.format.mimetype application/pdf
• dc.identifier.citation Majewski M, Pérez A, Thölke P, Doerr S, Charron NE, Giorgino T, Husic BE, Clementi C, Noé F, De Fabritiis G. Machine learning coarse-grained potentials of protein thermodynamics. Nat Commun. 2023 Sep 15;14(1):5739. DOI: 10.1038/s41467-023-41343-1
• dc.identifier.doi http://dx.doi.org/10.1038/s41467-023-41343-1
• dc.identifier.issn 2041-1723
• dc.identifier.uri http://hdl.handle.net/10230/58075
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nat Commun. 2023 Sep 15;14(1):5739
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/823712
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-116564GB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/772230
• dc.rights © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Machine learning
• dc.subject.keyword Molecular dynamics
• dc.subject.keyword Molecular modelling
• dc.subject.keyword Protein analysis
• dc.subject.keyword Protein function predictions
• dc.title Machine learning coarse-grained potentials of protein thermodynamics
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion