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

被引:19
|
作者
Terashi, Genki [1 ]
Wang, Xiao [2 ]
Subramaniya, Sai Raghavendra Maddhuri Venkata [2 ]
Tesmer, John J. G. [1 ,3 ]
Kihara, Daisuke [1 ,2 ,3 ]
机构
[1] Purdue Univ, Dept Biol Sci, W Lafayette, IN 47907 USA
[2] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA
[3] Purdue Univ, Dept Med Chem & Mol Pharmacol, W Lafayette, IN 47907 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
VALIDATION;
D O I
10.1038/s41592-022-01574-4
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The DAQ score assesses the consistency of amino acid assignment in protein structure models with local density from cryo-EM maps. The method complements existing quality metrics and is a versatile tool for highlighting problematic regions of model structures. 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.
引用
收藏
页码:1116 / +
页数:14
相关论文
共 50 条
  • [1] Residue-wise local quality estimation for protein models from cryo-EM maps
    Genki Terashi
    Xiao Wang
    Sai Raghavendra Maddhuri Venkata Subramaniya
    John J. G. Tesmer
    Daisuke Kihara
    Nature Methods, 2022, 19 : 1116 - 1125
  • [2] Deep learning-based local quality estimation for protein structure models from cryo-EM maps
    Terashi, Genki
    Wang, Xiao
    Subramaniya, Sai Raghavendra Maddhuri Venkata
    Tesmer, John J.
    Kihara, Daisuke
    BIOPHYSICAL JOURNAL, 2022, 121 (03) : 129 - 129
  • [3] Building Protein Atomic Models from Cryo-EM Density Maps and Residue Co-Evolution
    Bouvier, Guillaume
    Bardiaux, Benjamin
    Pellarin, Riccardo
    Rapisarda, Chiara
    Nilges, Michael
    BIOMOLECULES, 2022, 12 (09)
  • [4] Automatic Building of Protein Atomic Models from Cryo-EM Maps
    Bouvier, Guillaume
    Bardiaux, Benjamin
    Nilges, Michael
    BIOPHYSICAL JOURNAL, 2018, 114 (03) : 190A - 191A
  • [6] CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning
    Dai, Muzhi
    Dong, Zhuoer
    Xu, Kui
    Zhang, Qiangfeng Cliff
    JOURNAL OF MOLECULAR BIOLOGY, 2023, 435 (09)
  • [7] Quantifying the local resolution of cryo-EM density maps
    Kucukelbir A.
    Sigworth F.J.
    Tagare H.D.
    Nature Methods, 2014, 11 (1) : 63 - 65
  • [8] The accuracy of protein models automatically built into cryo-EM maps with ARP/wARP
    Chojnowski, Grzegorz
    Sobolev, Egor
    Heuser, Philipp
    Lamzin, Victor S.
    ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 2021, 77 : 142 - 150
  • [9] Quality vs. Resolution in Cryo-EM Maps
    Stagg, Scott M.
    ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, 2019, 75 : A412 - A412
  • [10] Likelihood-based docking of models into cryo-EM maps
    Millan, Claudia
    McCoy, Airlie J.
    Terwilliger, Thomas C.
    Read, Randy J.
    ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 2023, 79 : 281 - 289