Automatic and accurate ligand structure determination guided by cryo-electron microscopy maps

Andrew Muenks, Samantha Zepeda, Guangfeng Zhou, David Veesler and Frank DiMaio ()
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Andrew Muenks: University of Washington
Samantha Zepeda: University of Washington
Guangfeng Zhou: University of Washington
David Veesler: University of Washington
Frank DiMaio: University of Washington

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Advances in cryo-electron microscopy (cryoEM) and deep-learning guided protein structure prediction have expedited structural studies of protein complexes. However, methods for accurately determining ligand conformations are lacking. In this manuscript, we develop EMERALD, a tool for automatically determining ligand structures guided by medium-resolution cryoEM density. We show this method is robust at predicting ligands along with surrounding side chains in maps as low as 4.5 Å local resolution. Combining this with a measure of placement confidence and running on all protein/ligand structures in the EMDB, we show that 57% of ligands replicate the deposited model, 16% confidently find alternate conformations, 22% have ambiguous density where multiple conformations might be present, and 5% are incorrectly placed. For five cases where our approach finds an alternate conformation with high confidence, high-resolution crystal structures validate our placement. EMERALD and the resulting analysis should prove critical in using cryoEM to solve protein-ligand complexes.

Date: 2023
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DOI: 10.1038/s41467-023-36732-5

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