https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2023Reggiano_MEDIC2023Reggiano MEDIC - Revision history2026-05-24T21:14:36ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2023Reggiano_MEDIC&diff=4450&oldid=prevWikiSysop: Created page with "== Citation == Reggiano, Gabriella / Lugmayr, Wolfgang / Farrell, Daniel / Marlovits, Thomas C. / DiMaio, Frank. Residue-level error detection in cryoelectron microscopy mode..."2023-08-30T10:14:15Z<p>Created page with "== Citation == Reggiano, Gabriella / Lugmayr, Wolfgang / Farrell, Daniel / Marlovits, Thomas C. / DiMaio, Frank. Residue-level error detection in cryoelectron microscopy mode..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
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Reggiano, Gabriella / Lugmayr, Wolfgang / Farrell, Daniel / Marlovits, Thomas C. / DiMaio, Frank. Residue-level error detection in cryoelectron microscopy models. 2023. Structure, 31, 1-10<br />
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== Abstract ==<br />
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Building accurate protein models into moderate resolution (3–5A) cryoelectron microscopy (cryo-EM) maps<br />
is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical<br />
model that identifies local backbone errors in protein structures built into cryo-EM maps by combining<br />
local fit-to-density with deep-learning-derived structural information. MEDIC is validated on a set of 28 structures<br />
that were subsequently solved to higher resolutions, where we identify the differences between lowand<br />
high-resolution structures with 68% precision and 60% recall. We additionally use this model to fix<br />
over 100 errors in 12 deposited structures and to identify errors in 4 refined AlphaFold predictions with<br />
80% precision and 60% recall. Asmodelers more frequently use deep learning predictions as a starting point<br />
for refinement and rebuilding, MEDIC’s ability to handle errors in structures derived from hand-building and<br />
machine learning methods makes it a powerful tool for structural biologists.<br />
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== Keywords ==<br />
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== Links ==<br />
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https://www.cell.com/structure/pdf/S0969-2126(23)00158-2.pdf<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop