Publication: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms
Authors
Moebel, Emmanuel ; Martínez Sánchez, Antonio ; Lamm, Lorenz ; Righetto, Ricardo ; Wietrzynski, Wojciech ; Albert, Sahradha ; Lariviere, Damien ; Fourmentin, Eric ; Pfeffer, Stefan ; Ortiz, Julio ; Baumeister, Wolfgang ; Peng, Tingying ; Engel, Benjamin ; Kervrann, Charles
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Publisher
Nature Research
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DOI
https://doi.org/10.1038/s41592-021-01275-4
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info:eu-repo/semantics/article
Description
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. This document is the Accepted version of a Published Work that appeared in final form in Nature Methods. To access the final edited and published work see https://doi.org/10.1038/s41592-021-01275-4
Abstract
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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Citation
Nature Methods 18, 1386–1394 (2021)
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