CryoDRGN: Reconstruction of heterogeneous cryo-EM structures using neural networks

Abstract

Cryo-EM single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit complex conformational and compositional heterogeneity that pose a major challenge to existing 3D reconstruction methods. Here, we present cryoDRGN, an algorithm that leverages the representation power of deep neural networks to directly reconstruct continuous distributions of 3D density maps and map per-particle heterogeneity of single particle cryo-EM datasets. Using cryoDRGN, we uncovered residual heterogeneity in high-resolution datasets of the 80S ribosome and the RAG complex, revealed a new structural state of the assembling 50S ribosome, and visualized large-scale continuous motions of a spliceosome complex. CryoDRGN contains interactive tools to visualize a dataset’s distribution of per-particle variability, generate density maps for exploratory analysis, extract particle subsets for use with other tools, and generate trajectories to visualize molecular motions. CryoDRGN is open-source software freely available at cryodrgn.csail.mit.edu.