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

• Published in: Nature methods Vol. 18; no. 2; pp. 176 - 185
• Main Authors: Zhong, Ellen D., Bepler, Tristan, Berger, Bonnie, Davis, Joseph H.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.02.2021; Nature Publishing Group
• Subjects: 631/114/1305; 631/1647/2258/1258/1259; Algorithms; Artificial neural networks; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Cryoelectron microscopy; Cryoelectron Microscopy - methods; Datasets; Density; Electron microscopy; Heterogeneity; Learning algorithms; Life Sciences; Machine learning; Macromolecular Substances - chemistry; Macromolecules; Methods; Molecular Structure; Neural networks; Neural Networks, Computer; Proteomics; Public software; Reconstruction; Source code; United States
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-020-01049-4

Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit conformational and compositional heterogeneity that poses a major challenge to existing three-dimensional 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 http://cryodrgn.csail.mit.edu . CryoDRGN is an unsupervised machine learning algorithm that reconstructs continuous distributions of three-dimensional density maps from heterogeneous single-particle cryo-EM data.