Characterizing metastable states with the help of machine learning

Abstract

Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature is becoming increasingly challenging. In this paper, we first use the variational approach to conformation dynamics to discover the slowest dynamical modes of the simulations. This allows the different metastable states of the system to be located and organized hierarchically. The physical descriptors that characterize metastable states are discovered by means of a machine learning method. We show in the cases of two proteins, Chignolin and Bovine Pancreatic Trypsin Inhibitor, how such analysis can be effortlessly performed in a matter of seconds. Another strength of our approach is that it can be applied to the analysis of both unbiased and biased simulations.

Main text: 10 pages, 4 figures. Supplementary Info: 4 pages, 5, figures

References in corpus (3)

• Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
• Water regulates the residence time of Benzamidine in Trypsin
• Making the best of a bad situation: a multiscale approach to free energy calculation

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