OpenSSL SSL_connect: Connection reset by peer in connection to v2.sherpa.ac.uk:443 PuSH - Publication Server of Helmholtz Zentrum München: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.

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Moebel, E.* ; Martinez-Sanchez, A.* ; Lamm, L. ; Righetto, R.D. ; Wietrzynski, W. ; Albert, S.* ; Larivière, D.* ; Fourmentin, E.* ; Pfeffer, S.* ; Ortiz, J.* ; Baumeister, W.* ; Peng, T. ; Engel, B.D. ; Kervrann, C.*

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

Nat. Methods 18, 1386-1394 (2021)
Postprint DOI PMC
Open Access Green
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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords In-situ; Single; Classification; Visualization; Localization
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Journal Nature Methods
Quellenangaben Volume: 18, Issue: 11, Pages: 1386-1394 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Helmholtz Pioneer Campus (HPC)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Grants Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy
Helmholtz Association
Munich School for Data Science (MUDS)
DFG
France-BioImaging infrastructure (French National Research Agency)
Region Bretagne (Brittany Council)
Fourmentin-Guilbert Foundation