CryoSampler: Amortized Atomic Model Building Across Cryo-EM Conformational Ensembles

Cryo-electron microscopy (cryo-EM) increasingly resolves proteins in motion, recovering an ensemble of maps spanning a molecule's conformational landscape. Converting these maps into atomic coordinates is an inverse problem known as model building, a necessary step for every structure deposited in the Protein Data Bank, which in turn supplies the training data for the next generation of AlphaFold-like models. However, existing model building methods treat each map as an independent inverse problem, ignoring that every map in an ensemble is an observation of the same molecule. We ask whether solving this inverse problem jointly across the ensemble with a single amortized model yields better atomic models than per-map inference. In this work, we propose CryoSampler, an amortized framework for ensemble model building that trains a 3D variational autoencoder to map each cryo-EM map to a neural deformation field over a shared reference structure from a frozen Boltz-2 model, supervised end-to-end through a differentiable cryo-EM forward model and stereochemical restraints. We find that amortization acts as an implicit structural prior that produces atomic models of higher geometric quality at lower compute cost, achieving state-of-the-art performance across several experimental datasets. These results establish amortized inference across conformational ensembles as a simple and effective principle for converting heterogeneous cryo-EM maps into high-quality atomic structures.