Developing accurate and efficient coarse-grained representations of proteins
is crucial for understanding their folding, function, and interactions over
extended timescales. Our methodology involves simulating proteins with
molecular dynamics and utilizing the resulting trajectories to train a neural
network potential through differentiable trajectory reweighting. Remarkably,
this method requires only the native conformation of proteins, eliminating the
need for labeled data derived from extensive simulations or memory-intensive
end-to-end differentiable simulations. Once trained, the model can be employed
to run parallel molecular dynamics simulations and sample folding events for
proteins both within and beyond the training distribution, showcasing its
extrapolation capabilities. By applying Markov State Models, native-like
conformations of the simulated proteins can be predicted from the
coarse-grained simulations. Owing to its theoretical transferability and
ability to use solely experimental static structures as training data, we
anticipate that this approach will prove advantageous for developing new
protein force fields and further advancing the study of protein dynamics,
folding, and interactions