An increasing number of protein structures are being determined by cryogenic electron microscopy (cryo-EM). Although the resolution of determined cryo-EM density maps is improving in general, there are still many cases where amino acids of a protein are assigned with different levels of confidence. Here we developed a method that identifies potential misassignment of residues in the map, including residue shifts along an otherwise correct main-chain trace. The score, named DAQ, computes the likelihood that the local density corresponds to different amino acids, atoms, and secondary structures, estimated via deep learning, and assesses the consistency of the amino acid assignment in the protein structure model with that likelihood. When DAQ was applied to different versions of model structures in the Protein Data Bank that were derived from the same density maps, a clear improvement in the DAQ score was observed in the newer versions of the models. DAQ also found potential misassignment errors in a substantial number of deposited protein structure models built into cryo-EM maps.
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This work was partly supported by the National Institutes of Health (R01GM133840, R01GM123055, and 3R01GM133840-02S1 to D.K.; R01CA254402, R01CA221289, and R01HL071818 to J.J.G.T.); the National Science Foundation (CMMI1825941, MCB1925643, DBI2003635, and DBI2146026) to D.K.; and the Walther Foundation for Cancer Research to J.J.G.T.
The authors declare no competing interests.
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Nature Methods thanks Grigore Pintilie, Alexis Rohou, and Carlos Óscar Sánchez-Sorzano for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team. Peer reviewer reports are available.
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Terashi, G., Wang, X., Maddhuri Venkata Subramaniya, S.R. et al. Residue-wise local quality estimation for protein models from cryo-EM maps. Nat Methods 19, 1116–1125 (2022). https://doi.org/10.1038/s41592-022-01574-4
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