dc.contributor.author
Mardt, Andreas
dc.contributor.author
Hempel, Tim
dc.contributor.author
Clementi, Cecilia
dc.contributor.author
Noé, Frank
dc.date.accessioned
2022-11-21T07:57:32Z
dc.date.available
2022-11-21T07:57:32Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/36933
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-36646
dc.description.abstract
The 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
dc.format.extent
11 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Computational biophysics
en
dc.subject
Machine learning
en
dc.subject
Statistical methods
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::530 Physik::530 Physik
dc.title
Deep learning to decompose macromolecules into independent Markovian domains
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
7101
dcterms.bibliographicCitation.doi
10.1038/s41467-022-34603-z
dcterms.bibliographicCitation.journaltitle
Nature Communications
dcterms.bibliographicCitation.volume
13
dcterms.bibliographicCitation.url
https://doi.org/10.1038/s41467-022-34603-z
refubium.affiliation
Mathematik und Informatik
refubium.affiliation
Physik
refubium.affiliation.other
Institut für Mathematik
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refubium.funding
Springer Nature DEAL
refubium.note.author
Die Publikation wurde aus Open Access Publikationsgeldern der Freien Universität Berlin gefördert.
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
2041-1723