  The biological functions of proteins often depend on dynamic structural
ensembles. In this work, we develop a flow-based generative modeling approach
for learning and sampling the conformational landscapes of proteins. We
repurpose highly accurate single-state predictors such as AlphaFold and ESMFold
and fine-tune them under a custom flow matching framework to obtain
sequence-conditoned generative models of protein structure called AlphaFlow and
ESMFlow. When trained and evaluated on the PDB, our method provides a superior
combination of precision and diversity compared to AlphaFold with MSA
subsampling. When further trained on ensembles from all-atom MD, our method
accurately captures conformational flexibility, positional distributions, and
higher-order ensemble observables for unseen proteins. Moreover, our method can
diversify a static PDB structure with faster wall-clock convergence to certain
equilibrium properties than replicate MD trajectories, demonstrating its
potential as a proxy for expensive physics-based simulations. Code is available
at https://github.com/bjing2016/alphaflow.
