Atomic modeling of protein dynamics from cryo-EM with co-folding priors

Understanding how proteins and other biomacromolecular machines function requires characterizing not only individual structures, but their full conformational landscapes. New protein foundation models provide powerful priors over atomic structure, and recent work has leveraged experimental measurements to guide the generation of individual structures at inference time. However, recovering atomic conformational landscapes from experimental data remains an open problem. We introduce CryoCHEF, a method for all-atom modeling of protein $\text{C}$onformational $\text{H}$eterogeneous $\text{E}$nsembles using co-$\text{F}$olding priors. CryoCHEF proposes a new, general framework for mapping learned conformational representations from experimental cryo-EM data into the latent space of a pretrained co-folding model. Specifically, CryoCHEF learns a conformation-conditioned neural field over the pair representations of a pretrained co-folding model, inducing a continuous atomic ensemble conditioned on experimental data. Across synthetic benchmarks and experimental datasets, CryoCHEF recovers continuous structural ensembles with side-chain-level detail, captures known global and local conformational motions, and enables efficient generation of atomic trajectories across experimentally observed conformational landscapes.