CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells.
Nat Methods
; 21(8): 1537-1545, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-39025970
ABSTRACT
Advances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecules in native cellular environments. While cryo-ET can reveal structures at molecular resolution, image processing algorithms remain a bottleneck in resolving the heterogeneity of biomolecular structures in situ. Here, we introduce cryoDRGN-ET for heterogeneous reconstruction of cryo-ET subtomograms. CryoDRGN-ET learns a deep generative model of three-dimensional density maps directly from subtomogram tilt-series images and can capture states diverse in both composition and conformation. We validate this approach by recovering the known translational states in Mycoplasma pneumoniae ribosomes in situ. We then perform cryo-ET on cryogenic focused ion beam-milled Saccharomyces cerevisiae cells. CryoDRGN-ET reveals the structural landscape of S. cerevisiae ribosomes during translation and captures continuous motions of fatty acid synthase complexes inside cells. This method is openly available in the cryoDRGN software.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Ribossomos
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Saccharomyces cerevisiae
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Processamento de Imagem Assistida por Computador
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Microscopia Crioeletrônica
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Tomografia com Microscopia Eletrônica
Idioma:
En
Revista:
Nat Methods
Assunto da revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos