High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.
Proc Natl Acad Sci U S A
; 120(15): e2213149120, 2023 04 11.
Article
em En
| MEDLINE
| ID: mdl-37027429
ABSTRACT
Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual labels. Here, we introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation on five experimental cryo-ET datasets shows that an unsupervised deep learning based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection paves the way for systematic unbiased recognition of macromolecular complexes in situ.
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Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Tomografia com Microscopia Eletrônica
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article