Navigating protein landscapes with a machine-learned transferable coarse-grained model.

ABSTRACT: The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep-learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parameterization. We demonstrate that the model successfully predicts metastable states of folded, unfolded and intermediate structures, the fluctuations of intrinsically disordered proteins and relative folding

SUBMITTER: Charron NE 

PROVIDER: S-EPMC12313527 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature