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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.

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Authors and contacts

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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Cited By

DownLoad: Full-Size Img PowerPoint

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

DownLoad: CSV

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

DownLoad: CSV

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.

DownLoad: CSV

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Vol. 72, Issue 24 - 2023-12-20

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