  A generalized understanding of protein dynamics is an unsolved scientific
problem, the solution of which is critical to the interpretation of the
structure-function relationships that govern essential biological processes.
Here, we approach this problem by constructing coarse-grained molecular
potentials based on artificial neural networks and grounded in statistical
mechanics. For training, we build a unique dataset of unbiased all-atom
molecular dynamics simulations of approximately 9 ms for twelve different
proteins with multiple secondary structure arrangements. The coarse-grained
models are capable of accelerating the dynamics by more than three orders of
magnitude while preserving the thermodynamics of the systems. Coarse-grained
simulations identify relevant structural states in the ensemble with comparable
energetics to the all-atom systems. Furthermore, we show that a single
coarse-grained potential can integrate all twelve proteins and can capture
experimental structural features of mutated proteins. These results indicate
that machine learning coarse-grained potentials could provide a feasible
approach to simulate and understand protein dynamics.
