Machine learning in biological physics: From biomolecular prediction to design

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dc.citation.articleNumbere2311807121en_US
dc.citation.issueNumber27en_US
dc.citation.journalTitleProceedings of the National Academy of Sciencesen_US
dc.citation.volumeNumber121en_US
dc.contributor.authorMartin, Jonathanen_US
dc.contributor.authorLequerica Mateos, Marcosen_US
dc.contributor.authorOnuchic, José N.en_US
dc.contributor.authorColuzza, Ivanen_US
dc.contributor.authorMorcos, Farucken_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2024-08-22T15:28:50Zen_US
dc.date.available2024-08-22T15:28:50Zen_US
dc.date.issued2024en_US
dc.description.abstractMachine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is “learnable” and propose its future use in the generation of unique sequences that can fold into a target structure.en_US
dc.identifier.citationMartin, J., Lequerica Mateos, M., Onuchic, J. N., Coluzza, I., & Morcos, F. (2024). Machine learning in biological physics: From biomolecular prediction to design. Proceedings of the National Academy of Sciences, 121(27), e2311807121. https://doi.org/10.1073/pnas.2311807121en_US
dc.identifier.digitalmartin-et-al-2024en_US
dc.identifier.doihttps://doi.org/10.1073/pnas.2311807121en_US
dc.identifier.urihttps://hdl.handle.net/1911/117699en_US
dc.language.isoengen_US
dc.publisherNational Academy of Sciencesen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 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-nc-nd/4.0/en_US
dc.titleMachine learning in biological physics: From biomolecular prediction to designen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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