2022Terashi DAQ

Citation

Terashi, Genki / Wang, Xiao / Maddhuri Venkata Subramaniya, Sai Raghavendra / Tesmer, John J. G. / Kihara, Daisuke Residue-wise local quality estimation for protein models from cryo-EM maps. 2022. Nature Methods, Vol. 19, No. 9, p. 1116-1125

Abstract

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.

Links

https://www.nature.com/articles/s41592-022-01574-4