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Title | High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering. |
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Journal, issue, pages | Proc Natl Acad Sci U S A, Vol. 120, Issue 15, Page e2213149120, Year 2023 |
Publish date | Apr 11, 2023 |
Authors | Xiangrui Zeng / Anson Kahng / Liang Xue / Julia Mahamid / Yi-Wei Chang / Min Xu / |
PubMed Abstract | Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches ...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. |
External links | Proc Natl Acad Sci U S A / PubMed:37027429 / PubMed Central |
Methods | EM (subtomogram averaging) |
Resolution | 14.17 - 38.0 Å |
Structure data | EMDB-40043: Unsupervised detection of 70S ribosome from Mycoplasma pneumoniae EMDB-40087: Unsupervised detection of TRiC in Rattus norvegicus neuron EMDB-40089: Unsupervised detection of ribosome in Rattus neuron EMDB-40090: Unsupervised detection of proteasome in Rattus neuron |
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