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Nat Methods. 2017 Oct;14(10):983-985. doi: 10.1038/nmeth.4405. Epub 2017 Aug 28.

Convolutional neural networks for automated annotation of cellular cryo-electron tomograms.

Author information

1
Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, USA.
2
Verna Marrs and McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA.
3
Department of Biological Science, Centre for BioImaging Sciences, National University of Singapore, Singapore.

Abstract

Cellular electron cryotomography offers researchers the ability to observe macromolecules frozen in action in situ, but a primary challenge with this technique is identifying molecular components within the crowded cellular environment. We introduce a method that uses neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yield in situ structures of molecular components of interest. The method is available in the EMAN2.2 software package.

PMID:
28846087
PMCID:
PMC5623144
DOI:
10.1038/nmeth.4405
[Indexed for MEDLINE]
Free PMC Article

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