  The most popular and universally predictive protein simulation models employ
all-atom molecular dynamics (MD), 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 parametrization. We demonstrate
that the model successfully predicts folded structures, intermediates,
metastable folded and unfolded basins, and the fluctuations of intrinsically
disordered proteins while it is several orders of magnitude faster than an
all-atom model. This showcases the feasibility of a universal and
computationally efficient machine-learned CG model for proteins.
