Deep learning to decompose macromolecules into independent Markovian domains

Andreas Mardt, Tim Hempel, Cecilia Clementi and Frank Noé ()
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Andreas Mardt: Freie Universität Berlin, Department of Mathematics and Computer Science
Tim Hempel: Freie Universität Berlin, Department of Mathematics and Computer Science
Cecilia Clementi: Freie Universität Berlin, Department of Physics
Frank Noé: Freie Universität Berlin, Department of Mathematics and Computer Science

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-34603-z

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