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 dataefficient 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
A.M., T.H. performed research (A.M. derived loss functions and implemented deep learning framework; T.H. designed method and developed test systems); A.M., T.H. analyzed data; A.M., T.H., C.C., F.N. designed research; A.M., T.H., C.C., F.N. wrote the paper.
The authors declare no conflicts of interests.