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  Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms

Moebel, E., Martinez-Sanchez, A., Lamm, L., Righetto, R. D., Wietrzynski, W., Albert, S., et al. (2021). Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nature Methods, 18, 1386-1394. doi:10.1038/s41592-021-01275-4.

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https://rdcu.be/cE87y (Publisher version)
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 Creators:
Moebel, Emmanuel1, Author
Martinez-Sanchez, Antonio1, Author
Lamm, Lorenz1, Author
Righetto, Ricardo D.1, Author
Wietrzynski, Wojciech1, Author
Albert, Sahradha2, Author              
Lariviere, Damien1, Author
Fourmentin, Eric1, Author
Pfeffer, Stefan2, Author              
Ortiz, Julio O.2, Author              
Baumeister, Wolfgang2, Author              
Peng, Tingying1, Author
Engel, Benjamin D.1, Author
Kervrann, Charles1, Author
Affiliations:
1external, ou_persistent22              
2Baumeister, Wolfgang / Molecular Structural Biology, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565142              

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Free keywords: IN-SITU; SINGLE; CLASSIFICATION; VISUALIZATION; LOCALIZATIONBiochemistry & Molecular Biology;
 Abstract: DeepFinder is a deep learning-based tool for identifying macromolecules in cellular cryo-electron tomograms. DeepFinder performs with an accuracy comparable to expert-supervised ground truth annotations on multiple experimental datasets. Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase-oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms.

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Language(s): eng - English
 Dates: 2021-10-212021-11
 Publication Status: Published in print
 Pages: 24
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

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Title: Nature Methods
  Other : Nature Methods
Source Genre: Journal
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Publ. Info: New York, NY : Nature Pub. Group
Pages: - Volume / Issue: 18 Sequence Number: - Start / End Page: 1386 - 1394 Identifier: ISSN: 1548-7091
CoNE: https://pure.mpg.de/cone/journals/resource/111088195279556