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

Simple item page
dc.contributor.authorMoebel, Emmanuel
dc.contributor.authorMartínez Sánchez, Antonio
dc.contributor.authorLamm, Lorenz
dc.contributor.authorRighetto, Ricardo
dc.contributor.authorWietrzynski, Wojciech
dc.contributor.authorAlbert, Sahradha
dc.contributor.authorLariviere, Damien
dc.contributor.authorFourmentin, Eric
dc.contributor.authorPfeffer, Stefan
dc.contributor.authorOrtiz, Julio
dc.contributor.authorBaumeister, Wolfgang
dc.contributor.authorPeng, Tingying
dc.contributor.authorEngel, Benjamin
dc.contributor.authorKervrann, Charles
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.date.accessioned2025-01-18T19:28:00Z
dc.date.available2025-01-18T19:28:00Z
dc.date.issued2021-10-21
dc.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
dc.description.abstractCryogenic 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.
dc.formatapplication/pdfes
dc.format.extent31
dc.identifier.citationNature Methods 18, 1386–1394 (2021)
dc.identifier.doihttps://doi.org/10.1038/s41592-021-01275-4
dc.identifier.issnPrint: 1548-7091
dc.identifier.issnElectronic: 1548-7105
dc.identifier.urihttp://hdl.handle.net/10201/148763
dc.languageenges
dc.publisherNature Research
dc.relationThis work was jointly supported by the Fourmentin-Guilbert Foundation and Région Bretagne (Brittany Council). Calculations were performed on the Inria Rennes computing grid facilities partly funded by France-BioImaging infrastructure (French National Research Agency—ANR-10-INBS-04-07, ‘Investments for the future’) and at the Max Planck Institute for Biochemistry computing cluster, Martinsried, Germany. L.L., R.D.R., W.W., T.P. and B.D.E. were supported by DFG grant no. EN 1194/1-1 as part of FOR 2092, The Munich School for Data Science (MUDS) and Helmholtz Association. A.M.-S. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2067/1-390729940.es
dc.relation.publisherversionhttps://www.nature.com/articles/s41592-021-01275-4#citeas
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCryoelectron microscopy
dc.subjectImage processing
dc.titleDeep learning improves macromolecule identification in 3D cellular cryo-electron tomogramses
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Moebel_NatMethods_2021_accepted.pdf
Size:
19.48 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.26 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Artículos