Evidence map›Paper›PMID 39025970›Full record

ArticleNature methods2024

CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells.

Ramya Rangan, Ryan Feathers, Sagar Khavnekar, Adam Lerer, Jake D Johnston, Ron Kelley, Martin Obr, Abhay Kotecha, Ellen D Zhong

Abstract read
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In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. CoCryoViS: Collaborative online cryo-electron tomography visualization system.Protein science : a publication of the Protein Society · 2026
    Article
  5. Article
  6. Particles of echovirus 18 open to release their genomes in vivo.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. Article
  8. The evolving landscape of molecular visualization.Protein science : a publication of the Protein Society · 2026
    Review
  9. Article
  10. Review
  11. Review
  12. Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ramya Rangan *Department of Computer Science, Princeton University, Princeton, NJ, USA.
Ryan Feathers *Department of Computer Science, Princeton University, Princeton, NJ, USA.
Sagar KhavnekarMax Planck Institute of Biochemistry, Martinsried, Germany.
Adam LererOpenAI, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-5216-6542
Jake D JohnstonPhysiology and Cellular Biophysics, Columbia University, New York, NY, USA.
Ron KelleyMaterials and Structural Analysis Division, Thermo Fisher Scientific, Eindhoven, the Netherlands.
Martin ObrMaterials and Structural Analysis Division, Thermo Fisher Scientific, Eindhoven, the Netherlands.ORCID http://orcid.org/0000-0003-1756-6564
Abhay KotechaMaterials and Structural Analysis Division, Thermo Fisher Scientific, Eindhoven, the Netherlands. abhay.kotecha@thermofisher.com.ORCID http://orcid.org/0000-0002-4480-5439
Ellen D ZhongDepartment of Computer Science, Princeton University, Princeton, NJ, USA. zhonge@princeton.edu.ORCID http://orcid.org/0000-0001-6345-1907

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cryoelectron MicroscopyElectron Microscope TomographyImage Processing, Computer-AssistedRibosomesSaccharomyces cerevisiaeAlgorithmsMycoplasma pneumoniaeSoftware

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.