Majewski, MaciejPérez, AdriàThölke, PhilippDoerr, Stefan, 1987-Charron, Nicholas E.Giorgino, ToniHusic, Brooke E.Clementi, CeciliaNoé, FrankDe Fabritiis, Gianni2023-10-172023-10-172023Majewski 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/s41467-023-41343-12041-1723http://hdl.handle.net/10230/58075A 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.application/pdfeng© 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/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41467-023-41343-1Machine learningMolecular dynamicsMolecular modellingProtein analysisProtein function predictionsinfo:eu-repo/semantics/openAccess