Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours.

ABSTRACT: An ongoing challenge in protein chemistry is to identify the underlying interaction energies that capture protein dynamics. The traditional trade-off in biomolecular simulation between accuracy and computational efficiency is predicated on the assumption that detailed force fields are typically well-parameterized, obtaining a significant fraction of possible accuracy. We re-examine this trade-off in the more realistic regime in which parameterization is a greater source of error than the level of detail in the force field. To address parameterization of coarse-grained force fields, we use the contrastive divergence technique from machine learning to train from simulations of 450 proteins. In our procedure, the computational efficiency of the model enables high accuracy through the precise

SUBMITTER: Jumper JM 

PROVIDER: S-EPMC6307714 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature