CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
Name
nihms-1656428.pdf
Description
Accepted version
Size
5.64 MB
Format
Adobe PDF
Checksum (MD5)
4b3e0e50bea72bcf5974bae6f163056f
Author(s) • • •
Zhong, Ellen D
Bepler, Tristan
Berger, Bonnie
Davis, Joseph H
Date Issued
2021
Journal
Nature Methods
Publisher
Springer Science and Business Media LLC
Version
Author's final manuscript
Abstract
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc. 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.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41592-020-01049-4