Top-down machine learning of coarse-grained protein force fields

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• dc.contributor.author Navarro Ramírez, Carles
• dc.contributor.author Majewski, Maciej
• dc.contributor.author De Fabritiis, Gianni
• dc.date.accessioned 2024-03-21T07:29:44Z
• dc.date.available 2024-03-21T07:29:44Z
• dc.date.issued 2023
• dc.description.abstract Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov state models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.
• dc.description.sponsorship The project PID2020-116564GB-I00 has been funded by MCIN/AEI/10.13039/501100011033 (G.D.F.). With the support of the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Economy and Knowledge of the Generalitat of Catalonia.
• dc.format.mimetype application/pdf
• dc.identifier.citation Navarro C, Majewski M, De Fabritiis G. Top-down machine learning of coarse-grained protein force fields. J Chem Theory Comput. 2023 Nov 14;19(21):7518-26. DOI: 10.1021/acs.jctc.3c00638
• dc.identifier.doi http://dx.doi.org/10.1021/acs.jctc.3c00638
• dc.identifier.issn 1549-9618
• dc.identifier.uri http://hdl.handle.net/10230/59512
• dc.language.iso eng
• dc.publisher American Chemical Society (ACS)
• dc.relation.ispartof J Chem Theory Comput. 2023 Nov 14;19(21):7518-26
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-116564GB-I00
• dc.rights This article is licensed under CC-BY 4.0 (https://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 Computational chemistry
• dc.subject.keyword Conformation
• dc.subject.keyword Nucleic acid structure
• dc.subject.keyword Potential energy
• dc.subject.keyword Protein structure
• dc.title Top-down machine learning of coarse-grained protein force fields
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion