https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Rangan_CryoDRGNET2024Rangan CryoDRGNET - Revision history2026-06-13T12:13:27ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2024Rangan_CryoDRGNET&diff=4703&oldid=prevWikiSysop: Created page with "== Citation == Rangan, Ramya / Feathers, Ryan / Khavnekar, Sagar / Lerer, Adam / Johnston, Jake D. / Kelley, Ron / Obr, Martin / Kotecha, Abhay / Zhong, Ellen D. CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells. 2024. Nature Methods, p. 1-9 == Abstract == Advances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecules in native cellular environmen..."2024-08-19T06:26:08Z<p>Created page with "== Citation == Rangan, Ramya / Feathers, Ryan / Khavnekar, Sagar / Lerer, Adam / Johnston, Jake D. / Kelley, Ron / Obr, Martin / Kotecha, Abhay / Zhong, Ellen D. CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells. 2024. Nature Methods, p. 1-9 == Abstract == Advances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecules in native cellular environmen..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
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Rangan, Ramya / Feathers, Ryan / Khavnekar, Sagar / Lerer, Adam / Johnston, Jake D. / Kelley, Ron / Obr, Martin / Kotecha, Abhay / Zhong, Ellen D. CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells. 2024. Nature Methods, p. 1-9<br />
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== Abstract ==<br />
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Advances in cryo-electron tomography (cryo-ET) have produced new<br />
opportunities to visualize the structures of dynamic macromolecules<br />
in native cellular environments. While cryo-ET can reveal structures at<br />
molecular resolution, image processing algorithms remain a bottleneck<br />
in resolving the heterogeneity of biomolecular structures in situ.<br />
Here, we introduce cryoDRGN-ET for heterogeneous reconstruction<br />
of cryo-ET subtomograms. CryoDRGN-ET learns a deep generative<br />
model of three-dimensional density maps directly from subtomogram<br />
tilt-series images and can capture states diverse in both composition<br />
and conformation. We validate this approach by recovering the known<br />
translational states in Mycoplasma pneumoniae ribosomes in situ. We then<br />
perform cryo-ET on cryogenic focused ion beam–milled Saccharomyces<br />
cerevisiae cells. CryoDRGN-ET reveals the structural landscape of<br />
S. cerevisiae ribosomes during translation and captures continuous motions<br />
of fatty acid synthase complexes inside cells. This method is openly available<br />
in the cryoDRGN software.<br />
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== Keywords ==<br />
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== Links ==<br />
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https://www.nature.com/articles/s41592-024-02340-4<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop