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

doi:10.7498/aps.72.20231071

摘要

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.

关键词:

Abstract

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.

Keywords:

作者及机构信息

浙江工业大学信息工程学院, 杭州　310014

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.

Authors and contacts

School of Information Engineering, Zhejiang University of Technology, Hangzhou 310014, China

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).

文章全文 : translate this paragraph

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施引文献

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.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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
CASP	https://predictioncenter.org/download_area/
CAMEO	https://www.cameo3d.org/
Zhanglab	https://zhanglab.ccmb.med.umich.edu/decoys/
AlphaFoldDB	https://alphafold.ebi.ac.uk/
ESM Metagenomic Atlas	https://esmatlas.com/resources?action=search_structure
DeepAccNet	https://github.com/hiranumn/DeepAccNet
GNNRefine	http://raptorx.uchicago.edu/download/
DeepUMQA	https://academic.oup.com/bioinformatics/article/38/7/1895/6520805?login=true
DeepUMQA3	https://www.biorxiv.org/content/10.1101/2023.04.24.538194v1.full.pdf+html
GraphCPLMQA	https://www.biorxiv.org/content/10.1101/2023.05.16.540981v1.full.pdf+html
GraphGPSM	https://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	ROC^normalized		PR^normalized		Models
Predictor Name	$\rm AUC_{0,1}$	$ \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

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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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