High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering

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 l...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2023-04, Vol.120 (15), p.e2213149120-e2213149120
Main Authors: Zeng, Xiangrui, Kahng, Anson, Xue, Liang, Mahamid, Julia, Chang, Yi-Wei, Xu, Min
Format: Article
Language:English
Subjects:
Biological Sciences
Cellular structure
Cluster Analysis
Clustering
Cryoelectron Microscopy - methods
Data mining
Deep learning
Electron Microscope Tomography - methods
Homogeneous structure
Image Processing, Computer-Assisted - methods
Iterative methods
Macromolecular Substances - chemistry
Macromolecules
Molecular Structure
Pattern analysis
Physical Sciences
Three dimensional models
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2213149120