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

Moebel, Emmanuel and Martinez-Sanchez, Antonio and Lamm, Lorenz and Righetto, Ricardo D. and Wietrzynski, Wojciech and Albert, Sahradha and Larivière, Damien and Fourmentin, Eric and Pfeffer, Stefan and Ortiz, Julio and Baumeister, Wolfgang and Peng, Tingying and Engel, Benjamin D. and Kervrann, Charles. (2021) Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nature Methods, 18 (11). pp. 1386-1394.

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Official URL: https://edoc.unibas.ch/89574/

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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.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Structural Biology & Biophysics > Structural Biology and Biophysics (Engel)
UniBasel Contributors:Engel, Ben and Lamm, Lorenz and Diogo Righetto, Ricardo and Wietrzynski, Wojciech
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Nature Publishing Group
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:06 Sep 2022 10:20
Deposited On:23 Aug 2022 10:30

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