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

Andrew MuenksSamantha K ZepedaGuangfeng ZhouDavid J VeeslerFrank DiMaio
Published in: Nature communications (2023)
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.
Keyphrases
  • electron microscopy
  • high resolution
  • deep learning
  • protein protein
  • amino acid
  • machine learning
  • mass spectrometry
  • artificial intelligence
  • convolutional neural network
  • high intensity
  • case control