Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.
Nat Methods
; 18(11): 1386-1394, 2021 11.
Article
em En
| MEDLINE
| ID: mdl-34675434
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Processamento de Imagem Assistida por Computador
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Redes Neurais de Computação
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Microscopia Crioeletrônica
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Substâncias Macromoleculares
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Tomografia com Microscopia Eletrônica
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Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article