Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN
Name
2022.08.09.503342v1.full.pdf
Description
Submitted version
Size
26.45 MB
Format
Adobe PDF
Checksum (MD5)
8d0a773bb12c7c07b781788e3e6717dc
Author(s)
Kinman, Laurel F
Powell, Barrett M
Zhong, Ellen D
Berger, Bonnie
Davis, Joseph H
Date Issued
November 14, 2022
Journal
Nature Protocols
Publisher
Springer Science and Business Media LLC
Citation
Kinman, Laurel F, Powell, Barrett M, Zhong, Ellen D, Berger, Bonnie and Davis, Joseph H. 2022. "Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN." Nature Protocols.
Version
Original manuscript
Abstract
Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful technique to visualize the structural landscape sampled by a protein complex. However, algorithmic and computational bottlenecks in analyzing heterogeneous cryo-EM datasets have prevented the full realization of this potential. CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single-particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find is effective in modeling both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for the analysis of an existing assembling 50S ribosome dataset, including preparation of inputs, network training and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single-particle cryo-EM datasets and with moderate experience navigating Python and Jupyter notebooks. It requires 3-4 days to complete. CryoDRGN is open source software that is freely available.
MIT Department
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/s41596-022-00763-x
