@article {49533, title = {Analysis of discrete local variability and structural covariance in macromolecular assemblies using Cryo-EM and focused classification.}, journal = {Ultramicroscopy}, volume = {203}, year = {2018}, month = {2018 Nov 29}, pages = {170}, 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.

}, doi = {10.1016/j.ultramic.2018.11.016}, author = {Zhang, Cheng and Cantara, William and Jeon, Youngmin and Musier-Forsyth, Karin and Grigorieff, Nikolaus and Lyumkis, Dmitry} } @article {6318, title = {Likelihood-based classification of cryo-EM images using FREALIGN.}, journal = {Journal of Structural Biology}, volume = {183}, year = {2013}, month = {2013 Sep}, pages = {377-88}, abstract = {

We describe an implementation of maximum likelihood classification for single particle electron cryo-microscopy that is based on the FREALIGN software. Particle alignment parameters are determined by maximizing a joint likelihood that can include hierarchical priors, while classification is performed by expectation maximization of a marginal likelihood. We test the FREALIGN implementation using a simulated dataset containing computer-generated projection images of three different 70S ribosome structures, as well as a publicly available dataset of 70S ribosomes. The results show that the mixed strategy of the new FREALIGN algorithm yields performance on par with other maximum likelihood implementations, while remaining computationally efficient.

}, issn = {1095-8657}, doi = {10.1016/j.jsb.2013.07.005}, author = {Lyumkis, Dmitry and Brilot, Axel F and Theobald, Douglas L and Grigorieff, Nikolaus} } @article {6319, title = {Movies of ice-embedded particles enhance resolution in electron cryo-microscopy.}, journal = {Structure}, volume = {20}, year = {2012}, month = {2012 Nov 7}, pages = {1823-8}, abstract = {

Low-dose images obtained by electron cryo-microscopy (cryo-EM) are often affected by blurring caused by sample motion during electron beam exposure, degrading signal especially at high resolution. We show here that we can align frames of movies, recorded with a direct electron detector during beam exposure of rotavirus double-layered particles, thereby greatly reducing image blurring caused by beam-induced motion and sample stage instabilities. This procedure increases the efficiency of cryo-EM imaging and enhances the resolution obtained in three-dimensional reconstructions of the particle. Using movies in this way is generally applicable to all cryo-EM samples and should also improve the performance of midrange electron microscopes that may have limited mechanical stability and beam coherence.

}, keywords = {Cryoelectron Microscopy, Ice, Rotavirus}, issn = {1878-4186}, doi = {10.1016/j.str.2012.08.026}, author = {Campbell, Melody G and Cheng, Anchi and Brilot, Axel F and Moeller, Arne and Lyumkis, Dmitry and Veesler, David and Pan, Junhua and Harrison, Stephen C and Potter, Clinton S and Carragher, Bridget and Grigorieff, Nikolaus} }