Deep learning to decompose macromolecules into independent Markovian domains

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dc.citation.articleNumber7101en_US
dc.citation.journalTitleNature Communicationsen_US
dc.citation.volumeNumber13en_US
dc.contributor.authorMardt, Andreasen_US
dc.contributor.authorHempel, Timen_US
dc.contributor.authorClementi, Ceciliaen_US
dc.contributor.authorNoƩ, Franken_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2022-12-13T19:11:35Zen_US
dc.date.available2022-12-13T19:11:35Zen_US
dc.date.issued2022en_US
dc.description.abstractThe increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient statistics of state probabilities or state-to-state transitions because for large molecular systems the number of metastable states grows exponentially with size. In this manuscript, we approach this challenge by introducing a method that combines our recent progress on independent Markov decomposition (IMD) with VAMPnets, a deep learning approach to Markov modeling. We establish a training objective that quantifies how well a given decomposition of the molecular system into independent subdomains with Markovian dynamics approximates the overall dynamics. By constructing an end-to-end learning framework, the decomposition into such subdomains and their individual Markov state models are simultaneously learned, providing a data-efficient and easily interpretable summary of the complex system dynamics. While learning the dynamical coupling between Markovian subdomains is still an open issue, the present results are a significant step towards learning Ising models of large molecular complexes from simulation data.en_US
dc.identifier.citationMardt, Andreas, Hempel, Tim, Clementi, Cecilia, et al.. "Deep learning to decompose macromolecules into independent Markovian domains." <i>Nature Communications,</i> 13, (2022) Springer Nature: https://doi.org/10.1038/s41467-022-34603-z.en_US
dc.identifier.digitals41467-022-34603-zen_US
dc.identifier.doihttps://doi.org/10.1038/s41467-022-34603-zen_US
dc.identifier.urihttps://hdl.handle.net/1911/114122en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis 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 license, and indicate if changes were made. The images or other third party material in this article are included in the articleā€™s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the articleā€™s Creative Commons license 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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDeep learning to decompose macromolecules into independent Markovian domainsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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