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Residue-wise local quality estimation for protein models from cryo-EM maps

Nature Methods volume 19, pages 1116–1125 (2022)Cite this article

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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.

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Fig. 1: Overview of DAQ.
Fig. 2: Comparison of DAQ scores between first and revised protein models in the same PDB entry.
Fig. 3: Analysis of the DAQ score distribution for PDB entry 7JSN-B (EMD-22458).
Fig. 4: DAQ score analysis of misaligned residues in the PDBNR90 dataset.
Fig. 5: Analysis of 4,485 non-redundant PDB chain models in PDBNR1Å by DAQ score.

Data availability

The list of IDs of PDB and EMDB entries used in the datasets are provided in Supplementary Tables 2, 3, 5, and 6.

Code availability

The DAQ program is freely available for academic use from Github at https://github.com/kiharalab/DAQ. The program is available to run on a Google Collab website at https://bit.ly/daq-score.

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Acknowledgements

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.

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Author notes

  1. These authors contributed equally: Genki Terashi, Xiao Wang.

Authors and Affiliations

  1. Department of Biological Sciences, Purdue University, West Lafayette, IN, USA

    Genki Terashi, John J. G. Tesmer & Daisuke Kihara

  2. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Xiao Wang, Sai Raghavendra Maddhuri Venkata Subramaniya & Daisuke Kihara

  3. Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA

    John J. G. Tesmer & Daisuke Kihara

Authors
  1. Genki Terashi
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Contributions

J.J.G.T. and D.K. conceived the study. G.T. designed and implemented the DAQ score. X.W. coded and trained Emap2sec+ and computed probability values of structure features for cryo-EM maps. S.R.M.V.S. participated in coding Emap2sec+. G.T. and X.W. constructed datasets. G.T. and X.W. performed the computation and G.T., D.K., X.W., and J.J.G.T. analyzed the data. J.J.G.T. examined individual examples of potentially misassigned models. G.T. drafted the manuscript and J.J.G.T. and D.K. edited it. All the authors read and approved the manuscript.

Corresponding author

Correspondence to Daisuke Kihara.

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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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Supplementary information

Supplementary Information

Supplementary Figs 1–12 and Supplementary Tables 1–9.

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Supplementary Table

Supplementary Table. 1, 2, 4, 6, and 7

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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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