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

As cryo-EM becomes commonplace in drug discovery, tools for automating small molecule structure determination are needed. Here, authors show a map-guided ligand modeling approach to building ligand structures at resolutions common in cryo-EM. 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 angstrom 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.

Automated summary

As cryo-EM becomes commonplace in drug discovery, tools for automating small molecule structure determination are needed. Here, the authors present a map-guided ligand modeling approach that can build ligand structures at resolutions common in cryo-EM. This method can accurately predict the positions of ligands and surrounding side chains in maps with local resolutions as low as 4.5 angstroms.