Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN

Laurel F. Kinman, Barrett M. Powell, Ellen D. Zhong, Bonnie Berger, Joseph H. Davis

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)319-339
Number of pages21
JournalNature Protocols
Issue number2
StatePublished - Feb 2023

All Science Journal Classification (ASJC) codes

  • General Biochemistry, Genetics and Molecular Biology


Dive into the research topics of 'Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN'. Together they form a unique fingerprint.

Cite this