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

参考文献

[1]	Thompson M C, Yeates T O, Rodriguez J A 2020 F1000 Research 9 667 Google Scholar
[2]	Bai X C, McMullan G, Scheres S H 2015 Trends Biochem. Sci. 40 49 Google Scholar
[3]	Wüthrich K 2001 Nat. Struct. Biol. 8 923 Google Scholar
[4]	Steinegger M, Mirdita M, Söding J 2019 Nat. Methods 16 603 Google Scholar
[5]	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 596 583 Google Scholar
[6]	Rohl C A, Strauss C E, Misura K M, Baker D 2004 Methods in Enzymology (Amsterdam: Elsevier) pp66–93
[7]	Zhang Y 2008 BMC Bioinf. 9 40 Google Scholar
[8]	Källberg M, Wang H P, Wang S, Peng J, Wang Z Y, Lu H, Xu J B 2012 Nat. Protoc 7 1511 Google Scholar
[9]	Yang J Y, Anishchenko I, Park H, Peng Z L, Ovchinnikov S, Baker D 2020 PNAS 117 1496 Google Scholar
[10]	Zhao K L, Xia Y H, Zhang F J, Zhou X G, Li S Z, Zhang G J 2023 Commun. Biol. 6 243 Google Scholar
[11]	Lin Z M, Akin H, Rao R, Hie B, Zhu Z K, Lu W T, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, Costa S D A, Zarandi F M, Sercu T, Candido S, Rives S 2023 Science 379 1123 Google Scholar
[12]	Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A 2022 Nucleic Acids Res. 50 D439 Google Scholar
[13]	Chen J R, Siu S W 2020 Biomolecules 10 626 Google Scholar
[14]	Zemla A J 2003 Nucleic Acids Res. 31 3370 Google Scholar
[15]	Zhang Y, Skolnick J 2004 Proteins Struct. Funct. Bioinf. 57 702 Google Scholar
[16]	Mariani V, Biasini M, Barbato A, Schwede T J 2013 Bioinformatics 29 2722 Google Scholar
[17]	Olechnovič K, Kulberkytė E, Venclovas Č 2013 Proteins Struct. Funct. Bioinf. 81 149 Google Scholar
[18]	Antczak P L M, Ratajczak T, Lukasiak P, Blazewicz J 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Washington D. C, November 9–12, 2015 p665
[19]	Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A 2016 Proteins Struct. Funct. Bioinf. 84 4 Google Scholar
[20]	Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J 2019 Proteins Struct. Funct. Bioinf. 87 1011 Google Scholar
[21]	Moult J, Pedersen J T, Judson R, Fidelis K 1995 Proteins Struct. Funct. Bioinf. 23 R2 Google Scholar
[22]	Robin X, Haas J, Gumienny R, Smolinski A, Tauriello G, Schwede T 2021 Proteins Struct. Funct. Bioinf. 89 1977 Google Scholar
[23]	Fowler N J, Williamson M P 2022 Structure 30 925 Google Scholar
[24]	Kryshtafovych A, Antczak M, Szachniuk M, Zok T, Kretsch R C, Rangan R, Pham P, Das R, Robin X, Studer G, Durairaj J, Eberhardt J, Sweeney A, Topf M, Schwede T, Fidelis K, Moult J 2023 Proteins Struct. Funct. Bioinf. 91 1550 Google Scholar
[25]	Basu S, Wallner B 2016 PLoS One 11 e0161879 Google Scholar
[26]	Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T 2017 Sci. Rep. 7 10480 Google Scholar
[27]	Hiranuma N, Park H, Baek M, Anishchenko I, Dauparas J Baker D 2021 Nat. Commun. 12 1340 Google Scholar
[28]	Wang Z, Eickholt J, Cheng J L 2010 Bioinformatics 26 882 Google Scholar
[29]	Cheng J L, Wang Z, Tegge A N, Eickholt J 2009 Proteins Struct. Funct. Bioinf. 77 181 Google Scholar
[30]	Wu T Q, Guo Z Y, Hou J, Cheng J L 2021 BMC Bioinf. 22 1 Google Scholar
[31]	Wang J L, Wang W B, Shang Y, Xu D 2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Las Vegas, NV, USA & Changsha, China, December 6–8, 2022 p84
[32]	Wang W B, Li Z Y, Wang J L, Xu D, Shang Y 2019 Nucleic Acids Res. 47 W443 Google Scholar
[33]	McGuffin L J, Aldowsari F M, Alharbi S M, Adiyaman R 2021 Nucleic Acids Res. 49 W425 Google Scholar
[34]	McGuffin L J, Buenavista M T, Roche D B 2013 Nucleic Acids Res. 41 W368 Google Scholar
[35]	McGuffin L J 2008 Bioinformatics 24 586 Google Scholar
[36]	Uziela K, Wallner B 2016 Bioinformatics 32 1411 Google Scholar
[37]	Uziela K, Shu N, Wallner B, Elofsson A 2016 Sci. Rep. 6 33509 Google Scholar
[38]	Olechnovič K, Venclovas Č 2017 Proteins Struct. Funct. Bioinf. 85 1131 Google Scholar
[39]	Olechnovič K, Venclovas Č 2019 Nucleic Acids Res. 47 W437 Google Scholar
[40]	Igashov I, Olechnovič K, Kadukova M, Venclovas Č, Grudinin S 2021 Bioinformatics 37 2332 Google Scholar
[41]	Ye L S, Wu P K, Peng Z L, Gao J Z, Liu J, Yang J Y 2021 Bioinformatics 37 3752 Google Scholar
[42]	Guo S S, Liu J, Zhou X G, Zhang G J 2022 Bioinformatics 38 1895 Google Scholar
[43]	Liu J, Liu D, He G X, Zhang G J 2023 Proteins Struct. Funct. Bioinf. 91 1861 Google Scholar
[44]	Liu J, Zhao K L, Zhang G J 2023 Brief. Bioinform. 24 bbac507 Google Scholar
[45]	Kryshtafovych A, Barbato A, Fidelis K, Monastyrskyy B, Schwede T, Tramontano A 2014 Proteins Struct. Funct. Bioinf. 82 112 Google Scholar
[46]	Kryshtafovych A, Monastyrskyy B, Fidelis K, Schwede T, Tramontano A 2018 Proteins Struct. Funct. Bioinf. 86 345 Google Scholar
[47]	Won J, Baek M, Monastyrskyy B, Kryshtafovych A, Seok C 2019 Proteins Struct. Funct. Bioinf. 87 1351 Google Scholar
[48]	Haas J, Barbato A, Behringer D, Studer G, Roth S, Bertoni M, Mostaguir K, Gumienny R, Schwede T 2018 Proteins Struct. Funct. Bioinf. 86 387 Google Scholar
[49]	Jones T A, Kleywegt G J 1999 Proteins Struct. Funct. Bioinf. 37 30 Google Scholar
[50]	Martin A C, MacArthur M W, Thornton J M 1997 Proteins Struct. Funct. Bioinf. 29 14 Google Scholar
[51]	Keedy D A, Williams C J, Headd J J, Arendall III W B, Chen V B, Kapral G J, Gillespie R A, Block J N, Zemla A, Richardson D C, Richardson 2009 Proteins Struct. Funct. Bioinf. 77 29 Google Scholar
[52]	Janin J, Henrick K, Moult J, Eyck T L, Sternberg G E, Vajda S, Vakser L, Wodak S J 2003 Proteins Struct. Funct. Bioinf. 52 2 Google Scholar
[53]	Lipton Z C, Elkan C, Narayanaswamy B 2014 Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15–19, 2014 p225
[54]	Ozden B, Kryshtafovych A, Karaca E 2021 Proteins Struct. Funct. Bioinf. 89 1787 Google Scholar
[55]	Kwon S, Won J, Kryshtafovych A, Seok C 2021 Proteins Struct. Funct. Bioinf. 89 1940 Google Scholar
[56]	Lobo J M, Jiménez-Valverde A, Real R 2008 Global Ecol. Biogeogr. 17 145 Google Scholar
[57]	Spearman correlation coefficients, differences between, Myers L, Sirois M J https://doi.org/10.1002/0471667196.ess5050.pub2 [2023-11-21
[58]	Ron K, Foster P 1998 J. Mach. Learn. 30 271 Google Scholar
[59]	Wang W B, Wang J L, Li Z Y, Xu D, Shang Y 2021 Comput. Struct. Biotechnol. J. 19 6282 Google Scholar
[60]	McGuffin L J, Roche D B 2010 Bioinformatics 26 182 Google Scholar
[61]	McGuffin L J 2009 Proteins Struct. Funct. Bioinf. 77 185 Google Scholar
[62]	Ben-David M, Noivirt-Brik O, Paz A, Prilusky J, Sussman J L, Levy Y 2009 Proteins Struct. Funct. Bioinf. 77 50 Google Scholar
[63]	Alapati R, Bhattacharya D 2018 Proceedings of the 2018 ACM International Conference on Bioinformatics, Computa tional Biology, and Health Informatics Washington DC, USA, August 29–September 1, 2018 p307
[64]	Cheng J L, Choe M H, Elofsson A, Han K S, Hou J, Maghrabi A H, McGuffin L J, Menéndez-Hurtado D, Olechnovič K, Schwede T , Studer G, Uziela K, Venclovas Č, Wallner B 2019 Proteins Struct. Funct. Bioinf. 87 1361 Google Scholar
[65]	Bitton M, Keasar C 2022 Sci. Rep. 12 14074.Google Scholar
[66]	Ke G L, Meng Q, Finley T, Wang T F, Chen W, Ma W D, Ye Q W, Liu T Y 2017 Adv. Neural Inf. Process. Syst. 30 3149 Google Scholar
[67]	Maghrabi A H, McGuffin L J 2017 Nucleic Acids Res. 45 W416 Google Scholar
[68]	Maghrabi A H, McGuffin L J 2020 Protein Struct. Prediction 2165 69 Google Scholar
[69]	McGuffin L J, Shuid A N, Kempster R, Maghrabi A H, Nealon J O, Salehe B R, Atkins J D, Roche D B 2018 Proteins Struct. Funct. Bioinf. 86 335 Google Scholar
[70]	Studer G, Rempfer C, Waterhouse A M, Gumienny R, Haas J, Schwede T 2020 Bioinformatics 36 1765 Google Scholar
[71]	Benkert P, Tosatto S C, Schomburg D 2008 Proteins Struct. Funct. Bioinf. 71 261 Google Scholar
[72]	Manavalan B, Lee J 2017 Bioinformatics 33 2496 Google Scholar
[73]	Derevyanko G, Grudinin S, Bengio Y, Lamoureux G 2018 Bioinformatics 34 4046 Google Scholar
[74]	Pagès G, Charmettant B, Grudinin S 2019 Bioinformatics 35 3313 Google Scholar
[75]	Uziela K, Menéndez Hurtado D, Shu N, Wallner B, Elofsson A 2017 Bioinformatics 33 1578 Google Scholar
[76]	Rother K, Hildebrand PW, Goede A, Gruening B, Preissner R 2009 Nucleic Acids Res. 37 D393 Google Scholar
[77]	Krivov G G, Shapovalov M V, Dunbrack Jr R L 2009 Proteins Struct. Funct. Bioinf. 77 778 Google Scholar
[78]	Hurtado D M, Uziela K, Elofsson A 2018 arXiv:1804.06281 [q-bio.BM
[79]	Shuvo M H, Bhattacharya S, Bhattacharya D 2020 Bioinformatics 36 i285 Google Scholar
[80]	Laine E, Karami Y, Carbone A 2019 Mol. Biol. Evol. 36 2604 Google Scholar
[81]	Dapkūnas J, Olechnovič K, Venclovas Č 2021 Proteins Struct. Funct. Bioinf. 89 1834 Google Scholar
[82]	Cao R Z, Bhattacharya D, Hou J, Cheng J L 2016 BMC Bioinf. 17 495 Google Scholar
[83]	Fischer A, Igel C 2012 Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, Argentina, September 3–6, 2012 p14
[84]	Conover M, Staples M, Si D, Sun M, Cao R Z 2019 Comput. Math. Biophys. 7 1 Google Scholar
[85]	Yu Y, Si X S, Hu C H, Zhang J X 2019 Neural Comput. 31 1235 Google Scholar
[86]	Baldassarre F, Menéndez Hurtado D, Elofsson A, Azizpour H 2021 Bioinformatics 37 360 Google Scholar
[87]	Shen T, Wu J X, Lan H D, Zheng L Z, Pei J G, Wang S, Liu W, Huang J Z 2021 Proteins Struct. Funct. Bioinf. 89 1901 Google Scholar
[88]	Gilmer J, Schoenholz S S, Riley P F, Vinyals O, Dahl G 2017 International Conference on Machine Learning Sydney, Australia, August 6–11, 2017 p1263
[89]	Mukherjee S, Zhang Y 2009 Nucleic Acids Res. 37 e83 Google Scholar
[90]	Chen X, Morehead A, Liu J, Cheng J L 2023 Bioinformatics 39 i308 Google Scholar
[91]	McGuffin L J, Edmunds N S, Genc A G, Alharbi S, Salehe B R, Adiyaman R 2023 Nucleic Acids Res. 51 W274 Google Scholar
[92]	Evans R, O’Neill M, Pritzel A, Antropova N, Senior A, Green T, Žídek A, Bates R, Blackwell S, Yim J, Ronneberger O, Bodenstein1 S, Zielinski1 M, Bridgland A, Potapenko A, Cowie A, Tunyasuvunakool K, Jain R, Clancy E, Kohli1 P, Jumper J, Hassabis D 2022 bioRxiv 2021.10.04.463034
[93]	Olechnovic K, Venclovas Č 2023 Proteins Struct. Funct. Bioinf. 91 1879 Google Scholar
[94]	Wang Z, Eickholt J, Cheng J L 2011 Bioinformatics 27 1715 Google Scholar
[95]	He G, Liu J, Liu D, Zhang G 2023 Brief. Bioinform. 24 4 Google Scholar
[96]	Ballester P J, Richards W G 2007 J. Comput. Chem. 28 1711 Google Scholar
[97]	Liu J, Liu D, Zhang G 2023 bioRxiv 2023.04.24.538194
[98]	Meier J, Rao R, Verkuil R, Liu J, Sercu T, Rives A 2021 Adv. Neural Inf. Process. Syst. 34 29287 Google Scholar
[99]	Ivankov D N, Garbuzynskiy S O, Alm E, Plaxco K W, Baker D, Finkelstein A V 2003 Protein Sci. 12 2057 Google Scholar
[100]	Liu D, Zhang B, Liu J, Li H, Song L, Zhang G 2023 bioRxiv 2023.05.16.540981
[101]	Satorras V G, Hoogeboom E, Welling M 2021 International Conference on Machine Learning Vienna, Austria, July 18–24, 2021 p9323

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

[1]	Thompson M C, Yeates T O, Rodriguez J A 2020 F1000 Research 9 667 Google Scholar
[2]	Bai X C, McMullan G, Scheres S H 2015 Trends Biochem. Sci. 40 49 Google Scholar
[3]	Wüthrich K 2001 Nat. Struct. Biol. 8 923 Google Scholar
[4]	Steinegger M, Mirdita M, Söding J 2019 Nat. Methods 16 603 Google Scholar
[5]	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 596 583 Google Scholar
[6]	Rohl C A, Strauss C E, Misura K M, Baker D 2004 Methods in Enzymology (Amsterdam: Elsevier) pp66–93
[7]	Zhang Y 2008 BMC Bioinf. 9 40 Google Scholar
[8]	Källberg M, Wang H P, Wang S, Peng J, Wang Z Y, Lu H, Xu J B 2012 Nat. Protoc 7 1511 Google Scholar
[9]	Yang J Y, Anishchenko I, Park H, Peng Z L, Ovchinnikov S, Baker D 2020 PNAS 117 1496 Google Scholar
[10]	Zhao K L, Xia Y H, Zhang F J, Zhou X G, Li S Z, Zhang G J 2023 Commun. Biol. 6 243 Google Scholar
[11]	Lin Z M, Akin H, Rao R, Hie B, Zhu Z K, Lu W T, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, Costa S D A, Zarandi F M, Sercu T, Candido S, Rives S 2023 Science 379 1123 Google Scholar
[12]	Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A 2022 Nucleic Acids Res. 50 D439 Google Scholar
[13]	Chen J R, Siu S W 2020 Biomolecules 10 626 Google Scholar
[14]	Zemla A J 2003 Nucleic Acids Res. 31 3370 Google Scholar
[15]	Zhang Y, Skolnick J 2004 Proteins Struct. Funct. Bioinf. 57 702 Google Scholar
[16]	Mariani V, Biasini M, Barbato A, Schwede T J 2013 Bioinformatics 29 2722 Google Scholar
[17]	Olechnovič K, Kulberkytė E, Venclovas Č 2013 Proteins Struct. Funct. Bioinf. 81 149 Google Scholar
[18]	Antczak P L M, Ratajczak T, Lukasiak P, Blazewicz J 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Washington D. C, November 9–12, 2015 p665
[19]	Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A 2016 Proteins Struct. Funct. Bioinf. 84 4 Google Scholar
[20]	Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J 2019 Proteins Struct. Funct. Bioinf. 87 1011 Google Scholar
[21]	Moult J, Pedersen J T, Judson R, Fidelis K 1995 Proteins Struct. Funct. Bioinf. 23 R2 Google Scholar
[22]	Robin X, Haas J, Gumienny R, Smolinski A, Tauriello G, Schwede T 2021 Proteins Struct. Funct. Bioinf. 89 1977 Google Scholar
[23]	Fowler N J, Williamson M P 2022 Structure 30 925 Google Scholar
[24]	Kryshtafovych A, Antczak M, Szachniuk M, Zok T, Kretsch R C, Rangan R, Pham P, Das R, Robin X, Studer G, Durairaj J, Eberhardt J, Sweeney A, Topf M, Schwede T, Fidelis K, Moult J 2023 Proteins Struct. Funct. Bioinf. 91 1550 Google Scholar
[25]	Basu S, Wallner B 2016 PLoS One 11 e0161879 Google Scholar
[26]	Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T 2017 Sci. Rep. 7 10480 Google Scholar
[27]	Hiranuma N, Park H, Baek M, Anishchenko I, Dauparas J Baker D 2021 Nat. Commun. 12 1340 Google Scholar
[28]	Wang Z, Eickholt J, Cheng J L 2010 Bioinformatics 26 882 Google Scholar
[29]	Cheng J L, Wang Z, Tegge A N, Eickholt J 2009 Proteins Struct. Funct. Bioinf. 77 181 Google Scholar
[30]	Wu T Q, Guo Z Y, Hou J, Cheng J L 2021 BMC Bioinf. 22 1 Google Scholar
[31]	Wang J L, Wang W B, Shang Y, Xu D 2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Las Vegas, NV, USA & Changsha, China, December 6–8, 2022 p84
[32]	Wang W B, Li Z Y, Wang J L, Xu D, Shang Y 2019 Nucleic Acids Res. 47 W443 Google Scholar
[33]	McGuffin L J, Aldowsari F M, Alharbi S M, Adiyaman R 2021 Nucleic Acids Res. 49 W425 Google Scholar
[34]	McGuffin L J, Buenavista M T, Roche D B 2013 Nucleic Acids Res. 41 W368 Google Scholar
[35]	McGuffin L J 2008 Bioinformatics 24 586 Google Scholar
[36]	Uziela K, Wallner B 2016 Bioinformatics 32 1411 Google Scholar
[37]	Uziela K, Shu N, Wallner B, Elofsson A 2016 Sci. Rep. 6 33509 Google Scholar
[38]	Olechnovič K, Venclovas Č 2017 Proteins Struct. Funct. Bioinf. 85 1131 Google Scholar
[39]	Olechnovič K, Venclovas Č 2019 Nucleic Acids Res. 47 W437 Google Scholar
[40]	Igashov I, Olechnovič K, Kadukova M, Venclovas Č, Grudinin S 2021 Bioinformatics 37 2332 Google Scholar
[41]	Ye L S, Wu P K, Peng Z L, Gao J Z, Liu J, Yang J Y 2021 Bioinformatics 37 3752 Google Scholar
[42]	Guo S S, Liu J, Zhou X G, Zhang G J 2022 Bioinformatics 38 1895 Google Scholar
[43]	Liu J, Liu D, He G X, Zhang G J 2023 Proteins Struct. Funct. Bioinf. 91 1861 Google Scholar
[44]	Liu J, Zhao K L, Zhang G J 2023 Brief. Bioinform. 24 bbac507 Google Scholar
[45]	Kryshtafovych A, Barbato A, Fidelis K, Monastyrskyy B, Schwede T, Tramontano A 2014 Proteins Struct. Funct. Bioinf. 82 112 Google Scholar
[46]	Kryshtafovych A, Monastyrskyy B, Fidelis K, Schwede T, Tramontano A 2018 Proteins Struct. Funct. Bioinf. 86 345 Google Scholar
[47]	Won J, Baek M, Monastyrskyy B, Kryshtafovych A, Seok C 2019 Proteins Struct. Funct. Bioinf. 87 1351 Google Scholar
[48]	Haas J, Barbato A, Behringer D, Studer G, Roth S, Bertoni M, Mostaguir K, Gumienny R, Schwede T 2018 Proteins Struct. Funct. Bioinf. 86 387 Google Scholar
[49]	Jones T A, Kleywegt G J 1999 Proteins Struct. Funct. Bioinf. 37 30 Google Scholar
[50]	Martin A C, MacArthur M W, Thornton J M 1997 Proteins Struct. Funct. Bioinf. 29 14 Google Scholar
[51]	Keedy D A, Williams C J, Headd J J, Arendall III W B, Chen V B, Kapral G J, Gillespie R A, Block J N, Zemla A, Richardson D C, Richardson 2009 Proteins Struct. Funct. Bioinf. 77 29 Google Scholar
[52]	Janin J, Henrick K, Moult J, Eyck T L, Sternberg G E, Vajda S, Vakser L, Wodak S J 2003 Proteins Struct. Funct. Bioinf. 52 2 Google Scholar
[53]	Lipton Z C, Elkan C, Narayanaswamy B 2014 Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15–19, 2014 p225
[54]	Ozden B, Kryshtafovych A, Karaca E 2021 Proteins Struct. Funct. Bioinf. 89 1787 Google Scholar
[55]	Kwon S, Won J, Kryshtafovych A, Seok C 2021 Proteins Struct. Funct. Bioinf. 89 1940 Google Scholar
[56]	Lobo J M, Jiménez-Valverde A, Real R 2008 Global Ecol. Biogeogr. 17 145 Google Scholar
[57]	Spearman correlation coefficients, differences between, Myers L, Sirois M J https://doi.org/10.1002/0471667196.ess5050.pub2 [2023-11-21
[58]	Ron K, Foster P 1998 J. Mach. Learn. 30 271 Google Scholar
[59]	Wang W B, Wang J L, Li Z Y, Xu D, Shang Y 2021 Comput. Struct. Biotechnol. J. 19 6282 Google Scholar
[60]	McGuffin L J, Roche D B 2010 Bioinformatics 26 182 Google Scholar
[61]	McGuffin L J 2009 Proteins Struct. Funct. Bioinf. 77 185 Google Scholar
[62]	Ben-David M, Noivirt-Brik O, Paz A, Prilusky J, Sussman J L, Levy Y 2009 Proteins Struct. Funct. Bioinf. 77 50 Google Scholar
[63]	Alapati R, Bhattacharya D 2018 Proceedings of the 2018 ACM International Conference on Bioinformatics, Computa tional Biology, and Health Informatics Washington DC, USA, August 29–September 1, 2018 p307
[64]	Cheng J L, Choe M H, Elofsson A, Han K S, Hou J, Maghrabi A H, McGuffin L J, Menéndez-Hurtado D, Olechnovič K, Schwede T , Studer G, Uziela K, Venclovas Č, Wallner B 2019 Proteins Struct. Funct. Bioinf. 87 1361 Google Scholar
[65]	Bitton M, Keasar C 2022 Sci. Rep. 12 14074.Google Scholar
[66]	Ke G L, Meng Q, Finley T, Wang T F, Chen W, Ma W D, Ye Q W, Liu T Y 2017 Adv. Neural Inf. Process. Syst. 30 3149 Google Scholar
[67]	Maghrabi A H, McGuffin L J 2017 Nucleic Acids Res. 45 W416 Google Scholar
[68]	Maghrabi A H, McGuffin L J 2020 Protein Struct. Prediction 2165 69 Google Scholar
[69]	McGuffin L J, Shuid A N, Kempster R, Maghrabi A H, Nealon J O, Salehe B R, Atkins J D, Roche D B 2018 Proteins Struct. Funct. Bioinf. 86 335 Google Scholar
[70]	Studer G, Rempfer C, Waterhouse A M, Gumienny R, Haas J, Schwede T 2020 Bioinformatics 36 1765 Google Scholar
[71]	Benkert P, Tosatto S C, Schomburg D 2008 Proteins Struct. Funct. Bioinf. 71 261 Google Scholar
[72]	Manavalan B, Lee J 2017 Bioinformatics 33 2496 Google Scholar
[73]	Derevyanko G, Grudinin S, Bengio Y, Lamoureux G 2018 Bioinformatics 34 4046 Google Scholar
[74]	Pagès G, Charmettant B, Grudinin S 2019 Bioinformatics 35 3313 Google Scholar
[75]	Uziela K, Menéndez Hurtado D, Shu N, Wallner B, Elofsson A 2017 Bioinformatics 33 1578 Google Scholar
[76]	Rother K, Hildebrand PW, Goede A, Gruening B, Preissner R 2009 Nucleic Acids Res. 37 D393 Google Scholar
[77]	Krivov G G, Shapovalov M V, Dunbrack Jr R L 2009 Proteins Struct. Funct. Bioinf. 77 778 Google Scholar
[78]	Hurtado D M, Uziela K, Elofsson A 2018 arXiv:1804.06281 [q-bio.BM
[79]	Shuvo M H, Bhattacharya S, Bhattacharya D 2020 Bioinformatics 36 i285 Google Scholar
[80]	Laine E, Karami Y, Carbone A 2019 Mol. Biol. Evol. 36 2604 Google Scholar
[81]	Dapkūnas J, Olechnovič K, Venclovas Č 2021 Proteins Struct. Funct. Bioinf. 89 1834 Google Scholar
[82]	Cao R Z, Bhattacharya D, Hou J, Cheng J L 2016 BMC Bioinf. 17 495 Google Scholar
[83]	Fischer A, Igel C 2012 Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, Argentina, September 3–6, 2012 p14
[84]	Conover M, Staples M, Si D, Sun M, Cao R Z 2019 Comput. Math. Biophys. 7 1 Google Scholar
[85]	Yu Y, Si X S, Hu C H, Zhang J X 2019 Neural Comput. 31 1235 Google Scholar
[86]	Baldassarre F, Menéndez Hurtado D, Elofsson A, Azizpour H 2021 Bioinformatics 37 360 Google Scholar
[87]	Shen T, Wu J X, Lan H D, Zheng L Z, Pei J G, Wang S, Liu W, Huang J Z 2021 Proteins Struct. Funct. Bioinf. 89 1901 Google Scholar
[88]	Gilmer J, Schoenholz S S, Riley P F, Vinyals O, Dahl G 2017 International Conference on Machine Learning Sydney, Australia, August 6–11, 2017 p1263
[89]	Mukherjee S, Zhang Y 2009 Nucleic Acids Res. 37 e83 Google Scholar
[90]	Chen X, Morehead A, Liu J, Cheng J L 2023 Bioinformatics 39 i308 Google Scholar
[91]	McGuffin L J, Edmunds N S, Genc A G, Alharbi S, Salehe B R, Adiyaman R 2023 Nucleic Acids Res. 51 W274 Google Scholar
[92]	Evans R, O’Neill M, Pritzel A, Antropova N, Senior A, Green T, Žídek A, Bates R, Blackwell S, Yim J, Ronneberger O, Bodenstein1 S, Zielinski1 M, Bridgland A, Potapenko A, Cowie A, Tunyasuvunakool K, Jain R, Clancy E, Kohli1 P, Jumper J, Hassabis D 2022 bioRxiv 2021.10.04.463034
[93]	Olechnovic K, Venclovas Č 2023 Proteins Struct. Funct. Bioinf. 91 1879 Google Scholar
[94]	Wang Z, Eickholt J, Cheng J L 2011 Bioinformatics 27 1715 Google Scholar
[95]	He G, Liu J, Liu D, Zhang G 2023 Brief. Bioinform. 24 4 Google Scholar
[96]	Ballester P J, Richards W G 2007 J. Comput. Chem. 28 1711 Google Scholar
[97]	Liu J, Liu D, Zhang G 2023 bioRxiv 2023.04.24.538194
[98]	Meier J, Rao R, Verkuil R, Liu J, Sercu T, Rives A 2021 Adv. Neural Inf. Process. Syst. 34 29287 Google Scholar
[99]	Ivankov D N, Garbuzynskiy S O, Alm E, Plaxco K W, Baker D, Finkelstein A V 2003 Protein Sci. 12 2057 Google Scholar
[100]	Liu D, Zhang B, Liu J, Li H, Song L, Zhang G 2023 bioRxiv 2023.05.16.540981
[101]	Satorras V G, Hoogeboom E, Welling M 2021 International Conference on Machine Learning Vienna, Austria, July 18–24, 2021 p9323

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