Machine learning coarse-grained potentials of protein thermodynamics

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dc.citation.articleNumber5739en_US
dc.citation.journalTitleNature Communicationsen_US
dc.citation.volumeNumber14en_US
dc.contributor.authorMajewski, Maciejen_US
dc.contributor.authorPérez, Adriàen_US
dc.contributor.authorThölke, Philippen_US
dc.contributor.authorDoerr, Stefanen_US
dc.contributor.authorCharron, Nicholas E.en_US
dc.contributor.authorGiorgino, Tonien_US
dc.contributor.authorHusic, Brooke E.en_US
dc.contributor.authorClementi, Ceciliaen_US
dc.contributor.authorNoé, Franken_US
dc.contributor.authorDe Fabritiis, Giannien_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2024-05-03T15:51:17Zen_US
dc.date.available2024-05-03T15:51:17Zen_US
dc.date.issued2023en_US
dc.description.abstractA 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.en_US
dc.identifier.citationMajewski, M., Pérez, A., Thölke, P., Doerr, S., Charron, N. E., Giorgino, T., Husic, B. E., Clementi, C., Noé, F., & De Fabritiis, G. (2023). Machine learning coarse-grained potentials of protein thermodynamics. Nature Communications, 14(1), 5739. https://doi.org/10.1038/s41467-023-41343-1en_US
dc.identifier.digitals41467-023-41343-1en_US
dc.identifier.doihttps://doi.org/10.1038/s41467-023-41343-1en_US
dc.identifier.urihttps://hdl.handle.net/1911/115608en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleMachine learning coarse-grained potentials of protein thermodynamicsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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