Recent advances in estimating protein structure model accuracy

Liu Dong; Cui Xin-Yue; Wang Hao-Dong; Zhang Gui-Jun

doi:10.7498/aps.72.20231071

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:

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).

FullText HTML : translate this paragraph

References

[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

Cited By

图 1 在CASP中主流的模型质量评估方法

Figure 1. Mainstream model quality assessment methods in CASP.

DownLoad: Full-Size Img PowerPoint

图 2 模型质量评估三类方法示意图

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

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

DownLoad: 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

DownLoad: 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.

DownLoad: CSV

map

[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

[1]	Tang Tian-Yi, Xiong Yi-Ming, Zhang Rui-Ge, Zhang Jian, Li Wen-Fei, Wang Jun, Wang Wei. Progress in protein pre-training models integrating structural knowledge. Acta Physica Sinica, 2024, 73(18): 188701. doi: 10.7498/aps.73.20240811
[2]	Huang Yu-Hang, Chen Li-Xiang. Fractional Fourier transform imaging based on untrained neural networks. Acta Physica Sinica, 2024, 73(9): 094201. doi: 10.7498/aps.73.20240050
[3]	Zhang Zan, Huang Bei-Ju, Chen Hong-Da. Computational reconstruction on-chip spectrometer based on reconfigurable silicon photonic filters. Acta Physica Sinica, 2024, 73(14): 140701. doi: 10.7498/aps.73.20240224
[4]	Shi Yue, Ou Pan, Zheng Ming, Tai Han-Xu, Wang Yu-Hong, Duan Ruo-Nan, Wu Jian. Artifact noise suppression of particle-field computed tomography based on lightweight residual and enhanced convergence neural network. Acta Physica Sinica, 2024, 73(10): 104202. doi: 10.7498/aps.73.20231902
[5]	Liu Hong-Jiang, Liu Yi-Fei, Gu Fu-Xing. Automatic fabrication system of optical micro-nanofiber based on deep learning. Acta Physica Sinica, 2024, 73(10): 104207. doi: 10.7498/aps.73.20240171
[6]	Luo Fang-Fang, Cai Zhi-Tao, Huang Yan-Dong. Progress in protein pK_a prediction. Acta Physica Sinica, 2023, 72(24): 248704. doi: 10.7498/aps.72.20231356
[7]	Ou Xiu-Juan, Xiao Yi. Deep learning methods of predicting RNA torsion angle. Acta Physica Sinica, 2023, 72(24): 248703. doi: 10.7498/aps.72.20231069
[8]	Sun Tao, Yuan Jian-Mei. Prediction of band gap of transition metal sulfide with Janus structure by deep learning atomic feature representation method. Acta Physica Sinica, 2023, 72(2): 028901. doi: 10.7498/aps.72.20221374
[9]	Zhu Qi, Xu Duo, Zhang Yuan-Jun, Li Yu-Juan, Wang Wen, Zhang Hai-Yan. Ultrasonic detection of white etching defect based on convolution neural network. Acta Physica Sinica, 2022, 71(24): 244301. doi: 10.7498/aps.71.20221504
[10]	Zhang Hang-Ying, Wang Xue-Qi, Wang Hua-Ying, Cao Liang-Cai. Advanced Retinex-Net image enhancement method based on value component processing. Acta Physica Sinica, 2022, 71(11): 110701. doi: 10.7498/aps.71.20220099
[11]	Zhan Qing-Liang, Ge Yao-Jun, Bai Chun-Jin. Flow feature extraction models based on deep learning. Acta Physica Sinica, 2022, 71(7): 074701. doi: 10.7498/aps.71.20211373
[12]	Liu Chun-Jie, Zhao Xin-Jun, Gao Zhi-Fu, Jiang Zhong-Ying. Modeling study of adsorption/desorption of proteins by polymer mixed brush. Acta Physica Sinica, 2021, 70(22): 224701. doi: 10.7498/aps.70.20211219
[13]	Nan Hu, Ma Xiao-Jing, Zhao Hai-Bo, Tang Shao-Jie, Liu Wei-Hua, Wang Da-Wei, Jia Chun-Lin. Detection of intensity peaks in high-resolution transmission electron microscopy image based on YOLOv3. Acta Physica Sinica, 2021, 70(7): 076803. doi: 10.7498/aps.70.20201502
[14]	Wang Tian-Tian, Wang Hui, Zhu Yan-Chun, Wang Li-Jia. Motion tracking of left myocardium in cardiac cine magnetic resonance image based on displacement flow U-Net and variational autoencoder. Acta Physica Sinica, 2021, 70(22): 228701. doi: 10.7498/aps.70.20210885
[15]	Zhao Zhi-Peng, Zhou Shuang, Wang Xing-Yuan. A new chaotic signal based on deep learning and its application in image encryption. Acta Physica Sinica, 2021, 70(23): 230502. doi: 10.7498/aps.70.20210561
[16]	Su Bo, Tao Fen, Li Ke, Du Guo-Hao, Zhang Ling, Li Zhong-Liang, Deng Biao, Xie Hong-Lan, Xiao Ti-Qiao. Image alignment for synchrotron radiation based X-ray nano-CT. Acta Physica Sinica, 2021, 70(16): 160704. doi: 10.7498/aps.70.20210156
[17]	Zhang Yao, Zhang Yun-Bo, Chen Li. Deep-learning-assisted micro impurity detection on an optical surface. Acta Physica Sinica, 2021, 70(16): 168702. doi: 10.7498/aps.70.20210403
[18]	Xu Zhao, Zhou Xin, Bai Xing, Li Cong, Chen Jie, Ni Yang. Attacking asymmetric cryptosystem based on phase truncated Fourier fransform by deep learning. Acta Physica Sinica, 2021, 70(14): 144202. doi: 10.7498/aps.70.20202075
[19]	Chen Wei, Guo Yuan, Jing Shi-Wei. General image encryption algorithm based on deep learning compressed sensing and compound chaotic system. Acta Physica Sinica, 2020, 69(24): 240502. doi: 10.7498/aps.69.20201019
[20]	Lang Li-Ying, Lu Jia-Lei, Yu Na-Na, Xi Si-Xing, Wang Xue-Guang, Zhang Lei, Jiao Xiao-Xue. In depth learning based method of denoising joint transform correlator optical image encryption system. Acta Physica Sinica, 2020, 69(24): 244204. doi: 10.7498/aps.69.20200805

Catalog

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

Metrics

Abstract views: 4635
PDF Downloads: 210
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板