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Deep learning to decompose macromolecules into independent Markovian domains

Author

Listed:
  • Andreas Mardt

    (Freie Universität Berlin, Department of Mathematics and Computer Science)

  • Tim Hempel

    (Freie Universität Berlin, Department of Mathematics and Computer Science
    Freie Universität Berlin, Department of Physics)

  • Cecilia Clementi

    (Freie Universität Berlin, Department of Physics
    Rice University, Department of Chemistry
    Rice University, Center for Theoretical Biological Physics)

  • Frank Noé

    (Freie Universität Berlin, Department of Mathematics and Computer Science
    Freie Universität Berlin, Department of Physics
    Rice University, Department of Chemistry
    Microsoft Research AI4Science)

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.

Suggested Citation

  • Andreas Mardt & Tim Hempel & Cecilia Clementi & Frank Noé, 2022. "Deep learning to decompose macromolecules into independent Markovian domains," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34603-z
    DOI: 10.1038/s41467-022-34603-z
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    References listed on IDEAS

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    1. Peter Eastman & Jason Swails & John D Chodera & Robert T McGibbon & Yutong Zhao & Kyle A Beauchamp & Lee-Ping Wang & Andrew C Simmonett & Matthew P Harrigan & Chaya D Stern & Rafal P Wiewiora & Bernar, 2017. "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-17, July.
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    Cited by:

    1. Yuxuan Zhuang & Rebecca J. Howard & Erik Lindahl, 2024. "Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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