The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Beam-induced motion correction for sub-megadalton cryo-EM particles
Open accessAbstract
In electron cryo-microscopy (cryo-EM), the electron beam that is used for imaging also causes the sample to move. This motion blurs the images and limits the resolution attainable by single-particle analysis. In a previous Research article (Bai et al., 2013) we showed that correcting for this motion by processing movies from fast direct-electron detectors allowed structure determination to near-atomic resolution from 35,000 ribosome particles. In this Research advance article, we show that an improved movie processing algorithm is applicable to a much wider range of specimens. The new algorithm estimates straight movement tracks by considering multiple particles that are close to each other in the field of view, and models the fall-off of high-resolution information content by radiation damage in a dose-dependent manner. Application of the new algorithm to four data sets illustrates its potential for significantly improving cryo-EM structures, even for particles that are smaller than 200 kDa.
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Citations
Cites
RELION: Implementation of a Bayesian approach to cryo-EM structure determination
Scheres, S. H. W. (2012) · Journal of Structural Biology · doi:10.1016/j.jsb.2012.09.006 · Cited by 5,446
Self-citationRosenthal, P. B., & Henderson, R. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.07.013 · Cited by 2,416
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Metadata
- Authors
- Scheres, S. H.
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2014-08-13
- Volume
- 3
- Pages
- e03665-e03665
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W2146801942
- Cited by
- 335
- References
- 2
- FWCI
- 32.60
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
