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Ultramicroscopy. 2019 Aug;203:170-180. doi: 10.1016/j.ultramic.2018.11.016. Epub 2018 Nov 29.

Analysis of discrete local variability and structural covariance in macromolecular assemblies using Cryo-EM and focused classification.

Author information

1
Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA.
2
Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA.
3
Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA, USA. Electronic address: niko@grigorieff.org.
4
Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA. Electronic address: dlyumkis@salk.edu.

Abstract

Single-particle electron cryo-microscopy and computational image classification can be used to analyze structural variability in macromolecules and their assemblies. In some cases, a particle may contain different regions that each display a range of distinct conformations. We have developed strategies, implemented within the Frealign and cisTEM image processing packages, to focus-classify on specific regions of a particle and detect potential covariance. The strategies are based on masking the region of interest using either a 2-D mask applied to reference projections and particle images, or a 3-D mask applied to the 3-D volume. We show that focused classification approaches can be used to study structural covariance, a concept that is likely to gain more importance as datasets grow in size, allowing the distinction of more structural states and smaller differences between states. Finally, we apply the approaches to an experimental dataset containing the HIV-1 Transactivation Response (TAR) element RNA fused into the large bacterial ribosomal subunit to deconvolve structural mobility within localized regions of interest, and to a dataset containing assembly intermediates of the large subunit to measure structural covariance.

KEYWORDS:

Classification; Frealign; Heterogeneity; Ribosome; Single-particle cryo-EM; cisTEM

PMID:
30528101
PMCID:
PMC6476647
[Available on 2020-08-01]
DOI:
10.1016/j.ultramic.2018.11.016

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