  Cryo-electron microscopy (cryo-EM) is unique among tools in structural
biology in its ability to image large, dynamic protein complexes. Key to this
ability is image processing algorithms for heterogeneous cryo-EM
reconstruction, including recent deep learning-based approaches. The
state-of-the-art method cryoDRGN uses a Variational Autoencoder (VAE) framework
to learn a continuous distribution of protein structures from single particle
cryo-EM imaging data. While cryoDRGN can model complex structural motions, the
Gaussian prior distribution of the VAE fails to match the aggregate approximate
posterior, which prevents generative sampling of structures especially for
multi-modal distributions (e.g. compositional heterogeneity). Here, we train a
diffusion model as an expressive, learnable prior in the cryoDRGN framework.
Our approach learns a high-quality generative model over molecular
conformations directly from cryo-EM imaging data. We show the ability to sample
from the model on two synthetic and two real datasets, where samples accurately
follow the data distribution unlike samples from the VAE prior distribution. We
also demonstrate how the diffusion model prior can be leveraged for fast latent
space traversal and interpolation between states of interest. By learning an
accurate model of the data distribution, our method unlocks tools in generative
modeling, sampling, and distribution analysis for heterogeneous cryo-EM
ensembles.
