https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2025Kinman_SIREN2025Kinman SIREN - Revision history2026-05-24T20:20:43ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2025Kinman_SIREN&diff=4985&oldid=prevWikiSysop: Created page with "== Citation == L. F. Kinman, M. V. Carreira, B. M. Powell, and J. H. Davis, “Automated model-free analysis of cryo-EM volume ensembles with SIREn,” Structure, vol. 33, no. 5, pp. 974–987, 2025. == Abstract == Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly realized by the advent of machine learnin..."2025-06-20T09:01:59Z<p>Created page with "== Citation == L. F. Kinman, M. V. Carreira, B. M. Powell, and J. H. Davis, “Automated model-free analysis of cryo-EM volume ensembles with SIREn,” Structure, vol. 33, no. 5, pp. 974–987, 2025. == Abstract == Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly realized by the advent of machine learnin..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
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L. F. Kinman, M. V. Carreira, B. M. Powell, and J. H. Davis, “Automated model-free analysis of cryo-EM volume ensembles with SIREn,” Structure, vol. 33, no. 5, pp. 974–987, 2025.<br />
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== Abstract ==<br />
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Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and<br />
generate hypotheses linking conformational states to biological function. This potential has been increasingly<br />
realized by the advent of machine learning models that allow 100s-1,000s of 3D density maps to be<br />
generated from a single dataset. How to identify distinct structural states within these volume ensembles<br />
and quantify their relative occupancies remain open questions. Here, we present an approach to inferring variable<br />
regions directly from a volume ensemble based on the statistical co-occupancy of voxels, as well as a<br />
3D convolutional neural network that predicts binarization thresholds for volumes in an unbiased and automated<br />
manner. We show that these tools recapitulate known heterogeneity in a variety of simulated and real<br />
cryo-EM datasets and highlight how integrating these tools with existing data processing pipelines enables<br />
improved particle curation.<br />
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== Keywords ==<br />
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== Links ==<br />
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https://www.cell.com/structure/fulltext/S0969-2126(25)00057-7<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop