ArticleNature communications2023
A method for restoring signals and revealing individual macromolecule states in cryo-ET, REST.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Cryo-ET comparison of the hierarchical ultrastructure of silkworm, spider, and artificial silk fibers.Nature communications · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Nanoscale analysis of human G1 and metaphase chromatin in situ.The EMBO journal · 2025Article
- CryoSamba: Self-supervised deep volumetric denoising for cryo-electron tomography data.Journal of structural biology · 2025Article
- An image processing pipeline for electron cryo-tomography in RELION-5.FEBS open bio · 2024Article
- A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography.Nature communications · 2024Article
- Advances in cryo-ET data processing: meeting the demands of visual proteomics.Current opinion in structural biology · 2024Review
- Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies.Structural dynamics (Melville, N.Y.) · 2024Article
- DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography.bioRxiv : the preprint server for biology · 2024Article
- Combining per-particle cryo-ET and cryo-EM single particle analysis to elucidate heterogeneous DNA-protein organization.Current opinion in structural biology · 2024Review
- The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography.Frontiers in molecular biosciences · 2024Review
- Recent advances in infectious disease research using cryo-electron tomography.Frontiers in molecular biosciences · 2023Review
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8 authors.
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Abstract
Cryo-electron tomography (cryo-ET) is widely used to explore the 3D density of biomacromolecules. However, the heavy noise and missing wedge effect prevent directly visualizing and analyzing the 3D reconstructions. Here, we introduced REST, a deep learning strategy-based method to establish the relationship between low-quality and high-quality density and transfer the knowledge to restore signals in cryo-ET. Test results on the simulated and real cryo-ET datasets show that REST performs well in denoising and compensating the missing wedge information. The application in dynamic nucleosomes, presenting either in the form of individual particles or in the context of cryo-FIB nuclei section, indicates that REST has the capability to reveal different conformations of target macromolecules without subtomogram averaging. Moreover, REST noticeably improves the reliability of particle picking. These advantages enable REST to be a powerful tool for the straightforward interpretation of target macromolecules by visual inspection of the density and of a broad range of other applications in cryo-ET, such as segmentation, particle picking, and subtomogram averaging.
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