Deep learning to decompose macromolecules into independent Markovian domains
| dc.citation.articleNumber | 7101 | |
| dc.citation.journalTitle | Nature Communications | |
| dc.citation.volumeNumber | 13 | |
| dc.contributor.author | Mardt, Andreas | |
| dc.contributor.author | Hempel, Tim | |
| dc.contributor.author | Clementi, Cecilia | |
| dc.contributor.author | NoƩ, Frank | |
| dc.contributor.org | Center for Theoretical Biological Physics | |
| dc.date.accessioned | 2022-12-13T19:11:35Z | |
| dc.date.available | 2022-12-13T19:11:35Z | |
| dc.date.issued | 2022 | |
| 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. | |
| dc.identifier.citation | Mardt, 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. | |
| dc.identifier.digital | s41467-022-34603-z | |
| dc.identifier.doi | https://doi.org/10.1038/s41467-022-34603-z | |
| dc.identifier.uri | https://hdl.handle.net/1911/114122 | |
| dc.language.iso | eng | |
| dc.publisher | Springer Nature | |
| dc.rights | This 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 articleās Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the articleās 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. | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Deep learning to decompose macromolecules into independent Markovian domains | |
| dc.type | Journal article | |
| dc.type.dcmi | Text | |
| dc.type.publication | publisher version |
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