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蛋白质结构模型质量评估方法综述

刘栋 崔新月 王浩东 张贵军

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蛋白质结构模型质量评估方法综述

刘栋, 崔新月, 王浩东, 张贵军

Recent advances in estimating protein structure model accuracy

Liu Dong, Cui Xin-Yue, Wang Hao-Dong, Zhang Gui-Jun
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  • 蛋白质模型质量评估方法是蛋白质结构预测的关键技术, 自CASP7以来一直是结构生物信息学领域的研究热点. 模型质量评估方法不仅可以指导蛋白质结构模型的精修, 还能够从多个候选构象中筛选出最佳模型, 具有重要的生物学研究和实际应用价值. 本文首先回顾了国际蛋白质结构预测关键评估竞赛(CASP)、全球持续蛋白质结构预测竞赛(CAMEO)以及单体蛋白和复合物的模型评估指标, 主要梳理了近5年来包括共识方法(多模型方法)、准单模型方法和单模型方法在内的模型质量评估方法的发展历程, 并介绍CASP15中的复合物模型评估方法; 鉴于深度学习在蛋白质预测领域所取得的巨大进展, 重点分析了深度学习在单模型方法数据集生成、蛋白质特征提取以及网络架构构建方面的深入应用, 并进一步介绍了本课题组近年来在模型质量评估方面开展的工作; 最后, 总结分析了目前蛋白质模型质量评估技术的局限性及所面临的挑战, 并对未来发展趋势进行了展望.
    The quality assessment of protein models is a key technology in protein structure prediction and has become a prominent research focus in the field of structural bioinformatics since advent of CASP7. Model quality assessment method not only guides the refinement of protein structure model but also plays a crucial role in selecting the best model from multiple candidate conformations, offering significant value in biological research and practical applications. This study begins with reviewing the critical assessment of protein structure prediction (CASP) and continuous automated model evaluation (CAMEO), and model evaluation metrics for monomeric and complex proteins. It primarily summarizes the development of model quality assessment methods in the last five years, including consensus methods (multi-model methods), single-model methods, and quasi-single-model methods, and also introduces the evaluation methods for protein complex models in CASP15. Given the remarkable progress of deep learning in protein prediction, the article focuses on the in-depth application of deep learning in single-model methods, including data set generation, protein feature extraction, and network architecture construction. Additionally, it presents the recent efforts of our research group in the field of model quality assessment. Finally, the article analyzes the limitations and challenges of current protein model quality assessment technology, and also looks forward to future development trends.
      通信作者: 张贵军, zgj@zjut.edu.cn
    • 基金项目: 科技创新2030—“新一代人工智能”重大项目(批准号: 2022ZD0115103)、国家自然科学基金(批准号: 62173304)和浙江省自然科学基金重点项目(批准号: LZF030002)资助的课题.
      Corresponding author: Zhang Gui-Jun, zgj@zjut.edu.cn
    • Funds: Project supported by the Scientific and Technological Innovation 2030−“New Generation Artificial Intelligence”, China (Grant No. 2022ZD0115103), the National Nature Science Foundation of China (Grant No. 62173304), and the Key Project of Zhejiang Provincial Natural Science Foundation of China (Grant No. LZ20F030002).
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  • 图 1  在CASP中主流的模型质量评估方法

    Fig. 1.  Mainstream model quality assessment methods in CASP.

    图 2  模型质量评估三类方法示意图

    Fig. 2.  Schematic diagram of three methods of model quality assessment.

    图 3  (a) lDDT, CAD, PatchDockQ和PatchQS的平均Z分数之和, CASP15官方公布各个小组在界面残基精确度估计排名(数据来自https://predictioncenter.org/casp15). CASP15中DeepUMQA3的组名称为“GuijunLab-RocketX”; (b) 针对CASP15, 每个蛋白质目标上的预测的lDDT质量与真实lDDT质量的Pearson相关性, 其中, 白色方框是均值, 中间横线是中位数

    Fig. 3.  (a) The sum of average Z-scores of lDDT, CAD, PatchDockQ and PatchQS, CASP15 officially announces the ranking of each group in the interface residue accuracy estimation (data from https://predictioncenter.org/casp15). The group name of DeepUMQA3 in CASP15 is “GuijunLab-RocketX”. (b) Pearson correlation of predicted and true lDDT quality on each protein target. The white box is the mean and the middle horizontal line is the median.

    表 1  模型质量评估的蛋白质结构数据集(诱饵)

    Table 1.  Protein structure dataset (Decoys) for model quality assessment.

    Data sets URLs
    CASPhttps://predictioncenter.org/download_area/
    CAMEOhttps://www.cameo3d.org/
    Zhanglabhttps://zhanglab.ccmb.med.umich.edu/decoys/
    AlphaFoldDBhttps://alphafold.ebi.ac.uk/
    ESM Metagenomic Atlashttps://esmatlas.com/resources?action=search_structure
    DeepAccNethttps://github.com/hiranumn/DeepAccNet
    GNNRefinehttp://raptorx.uchicago.edu/download/
    DeepUMQAhttps://academic.oup.com/bioinformatics/article/38/7/1895/6520805?login=true
    DeepUMQA3https://www.biorxiv.org/content/10.1101/2023.04.24.538194v1.full.pdf+html
    GraphCPLMQAhttps://www.biorxiv.org/content/10.1101/2023.05.16.540981v1.full.pdf+html
    GraphGPSMhttps://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbad219/7197734?searchresult=1#supplementary-data
    下载: 导出CSV

    表 2  CAMEO-QE: 模型质量评估性能(数据来自官网2022-6-24—2023-6-17)

    Table 2.  CAMEO-QE: Model Quality Evaluation Performance (Data from official website 2022-6-24–2023-6-17)

    Predictor Name ROCnormalized PRnormalized Models
    $\rm AUC_{0,1}$ $ \rm AUC_{0,0.2}^*$ $\rm AUC_{0,1} $ $\rm AUC_{0.8,1}^*$ $\rm Received $
    ZJUT-GraphCPLMQA 0.82 0.73 0.79 0.54 5143
    DeepUMQA2 0.72 0.62 0.68 0.47 4468
    DeepUMQA 0.73 0.60 0.67 0.45 4611
    ModFOLD9 0.63 0.52 0.59 0.36 4309
    QMEANDisCo3 0.9 0.66 0.79 0.49 6348
    ProQ3D_LDDT 0.74 0.55 0.67 0.43 5171
    QMEAN3 0.88 0.65 0.77 0.43 6348
    ProQ3 0.72 0.53 0.66 0.39 5126
    VoroMQA_v2 0.89 0.64 0.77 0.45 6350
    ProQ2 0.86 0.59 0.74 0.39 6337
    ProQ3D 0.70 0.47 0.61 0.35 5119
    ModFOLD7_lDDT 0.84 0.53 0.69 0.41 6191
    ModFOLD8 0.79 0.50 0.65 0.38 5802
    Baseline Potential 0.80 0.51 0.66 0.32 6350
    VoroMQA_sw5 0.82 0.50 0.65 0.36 6349
    ModFOLD6 0.73 0.42 0.57 0.35 5380
    下载: 导出CSV

    表 3  在所有蛋白质目标与CASP15服务器的性能比较(数据来自GraphGPSM)

    Table 3.  Performance comparison with CASP15 server on all protein targets (data from GraphGPSM).

    Method Average TM-score Average Pearson Average bias
    GraphGPSM 0.730 0.633 0.126
    MULTICOM_qa 0.485 0.715 0.258
    ModFOLDdock 0.515 0.636 0.241
    ModFOLDdockR 0.666 0.635 0.165
    Venclovas 0.449 0.494 0.339
    Manifold 0.582 0.541 0.179
    Bhattacharya 0.387 0.474 0.361
    *Real value 0.716 None None
    注: *Real value 代表CASP15中所有蛋白质目标所有模型的真实平均TM-score分数.
    Note: *Real value represents the real average T-score of all targets in CASP15.
    下载: 导出CSV
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    Steinegger M, Mirdita M, Söding J 2019 Nat. Methods 16 603Google Scholar

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    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl S A, Ballard A J, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman1 D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior A W, Kavukcuoglu K, Kohli P, Hassabis D 2021 Nature 596 583Google Scholar

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出版历程
  • 收稿日期:  2023-06-30
  • 修回日期:  2023-08-01
  • 上网日期:  2023-09-05
  • 刊出日期:  2023-12-20

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