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RNAGCN: RNA tertiary structure assessment with a graph convolutional network |
| Chengwei Deng(邓成伟)1, Yunxin Tang(唐蕴芯)1, Jian Zhang(张建)1,2,†, Wenfei Li(李文飞)1,2, Jun Wang(王骏)1,2, and Wei Wang(王炜)1,2,‡ |
1 Collaborative Innovation Center of Advanced Microstructures, School of Physics, Nanjing University, Nanjing 210008, China;
2 Institute for Brain Sciences, Nanjing University, Nanjing 210008, China |
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Abstract RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.
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Received: 14 June 2022
Revised: 11 August 2022
Accepted manuscript online: 26 August 2022
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PACS:
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87.15.B-
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(Structure of biomolecules)
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87.14.gn
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(RNA)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: This study was funded by the National Natural Science Foundation of China (Grant Nos. 11774158 to JZ, 11934008 to WW, and 11974173 to WFL). The authors acknowledge High Performance Computing Center of Advanced Microstructures, Nanjing University for the computational support. |
Corresponding Authors:
Jian Zhang, Wei Wang
E-mail: jzhang@nju.edu.cn;wangwei@nju.edu.cn
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Cite this article:
Chengwei Deng(邓成伟), Yunxin Tang(唐蕴芯), Jian Zhang(张建), Wenfei Li(李文飞), Jun Wang(王骏), and Wei Wang(王炜) RNAGCN: RNA tertiary structure assessment with a graph convolutional network 2022 Chin. Phys. B 31 118702
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[1] Krochmal D, Shao Y, Li N S, DasGupta S, Shelke S A, Koirala D and Piccirilli J A 2022 Nat. Chem. Biol. 18 376
[2] Oleksiak M F, Roach J L and Crawford D L 2005 Nat. Genet. 37 67
[3] Zhang Y, Wang J and Xiao Y 2020 Comput. Struct. Biotechnol. J. 18 2416
[4] Watkins A M, Rangan R and Das R 2020 Structure 28 963
[5] Krokhotin A, Houlihan K and Dokholyan N V 2015 Bioinformatics 31 2891
[6] Parisien M and Major F 2008 Nature 452 51
[7] Zhao C, Xu X, Chen S J, Kaufmann M, et al. 2017 Functional Genomics: Methods and Protocols (New York: Springer), pp. 3-15
[8] Boniecki M J, Lach G, Dawson W K, Tomala K, Lukasz P, Soltysinski T, Rother K M and Bujnicki J M 2016 Nucleic Acids Res. 44 e63
[9] Zhao Y, Huang Y, Gong Z, Wang Y, Man J and Xiao Y 2012 Sci. Rep. 2 734
[10] Wang J, Mao K, Zhao Y, Zeng C, Xiang J, Zhang Y and Xiao Y 2017 Nucleic Acids Research 45 6299
[11] Xu X and Chen S J 2021 Methods Mol. Biol 2323 1
[12] Zhang J, Zhang Y J and Wang W 2010 Chin. Phys. Lett. 27 118702
[13] Zhang D, Li J and Chen S J 2021 Journal of Chemical Theory and Computation 17 1842
[14] Xu X and Chen S J 2016 PloS One 11 e0163454
[15] Tan Z J and Chen S J 2010 Biophysical Journal 99 1565
[16] Gong S, Liu T G, Wang Y L and Zhang W B 2020 Chin. Phys. B 29 108703
[17] Shi Y Z, Wu Y Y, Wang F H and Tan Z J 2014 Chin. Phys. B 23 78701
[18] Wang W B, Li X Y and Su J G 2022 Chin. Phys. B 31 68704
[19] He X L, Wang J, Wang J and Xiao Y 2020 Chin. Phys. B 29 78702
[20] Tan Y L, Feng C J, Wang X X, Zhang W B and Tan Z J 2021 Chin. Phys. B 30 28705
[21] Yang Y, Gu Q, Zhang B G, Shi Y Z and Shao Z G 2018 Chin. Phys. B 27 38701
[22] Alford R F, Leaver-Fay A, Jeliazkov J R, et al. 2017 Journal of Chemical Theory and Computation 13 3031
[23] Wang J, Zhao Y, Zhu C and Xiao Y 2015 Nucleic Acids Res. 43 e63
[24] Capriotti E, Norambuena T, Marti-Renom M A and Melo F 2011 Bioinformatics 27 1086
[25] Bernauer J, Huang X, Sim A Y and Levitt M 2011 RNA 17 1066
[26] Zhang T, Hu G, Yang Y, Wang J and Zhou Y 2020 J. Comput. Biol 27 856
[27] Tan Y L, Wang X, Shi Y Z, Zhang W and Tan Z J 2022 Biophys. J. 121 142
[28] Devlin J, Chang M W, Lee K and Toutanova K 2019 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171-4186
[29] Brown T B, Mann B, Ryder N, et al. 2020 Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, Article 159
[30] Arumugam K, Naved M, Shinde P, Leiva-Chauca O, Huaman-Osorio A and Gonzales-Yanac T 2021 Materials Today: Proceedings
[31] Jumper J, Evans R, Pritzel A, et al. 2021 Nature 596 583
[32] Eismann S, Townshend R J L, Thomas N, Jagota M, Jing B and Dror R O 2021 Proteins 89 493
[33] Pei B and Hsu Y H 2020 Immunogenetics 72 295
[34] Huang B, Du Y Y, Zhang S, Li W F, Wang J and Zhang J 2020 Chin. Phys. B 29 108704
[35] Li J, Zhu W, Wang J, Li W, Gong S, Zhang J and Wang W 2018 PLoS Comput. Biol. 14 e1006514
[36] Min C, Xu J, Xiao L, Zhao D, Nie Y and Dai B 2021 IEEE Robotics and Automation Letters 6 3445
[37] Wu Z, Pan S, Chen F, Long G, Zhang C and Yu P S 2021 IEEE Trans. Neural Netw. Learn. Syst. 32 4
[38] Li G, Muller M, Thabet A and Ghanem B 2019 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9266-9275
[39] Zhang C, Song D, Huang C, Swami A and Chawla N V 2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 793-803
[40] Li G, Müler M, Ghanem B and Koltun V 2021 Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research, pp. 6437-6449
[41] Li X and Cheng Y 2021 Neural Netw. 140 130
[42] Fang X, Liu L, Lei J, He D, Zhang S, Zhou J, Wang F, Wu H and Wang H 2022 Nature Machine Intelligence 4 127
[43] Fout A, Byrd J, Shariat B and Ben-Hur A 2017 Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, pp. 6533-6542
[44] Baldassarre F, Menendez Hurtado D, Elofsson A and Azizpour H 2021 Bioinformatics 37 360
[45] Sanyal S, Anishchenko I, Dagar A, Baker D and Talukdar P 2020 bioRxiv:0406.028266v1
[46] Quan Z, Guo Y, Lin X, Wang Z J and Zeng X 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 18-21 Nov. 2019, pp. 717-722
[47] Huang Y A, Huang Z A, You Z H, Zhu Z, Huang W Z, Guo J X and Yu C Q 2019 Front Genet 10 758
[48] Leontis N B, Zirbel C L (Leontis N, Westhof E ed) 2012 RNA 3D Structure Analysis and Prediction (Berlin, Heidelberg: Springer Berlin Heidelberg), pp. 281-298
[49] Nawrocki E P and Eddy S R 2013 Bioinformatics 29 2933
[50] Abraham M J, Murtola T, Schulz R, Páll S, Smith J C, Hess B and Lindahl E 2015 Software X 1-2 19
[51] Miao Z, Adamiak R W, Antczak M, et al. 2020 RNA 26 982
[52] Tan Y L, Feng C J, Jin L, Shi Y Z, Zhang W and Tan Z J 2019 RNA 25 793
[53] Zwieb C, Nakao Y, Nakashima T, Takagi H, Goda S, Andersen E S, Kakuta Y and Kimura M 2011 Biochem Biophys Res. Commun. 414 517
[54] Townshend R, Eismann S, Watkins A, Rangan R, Karelina M, Das R and Dror R 2021 Science 373 1047
[55] Xiong G, Xi K, Zhang X and Tan Z J 2018 Chin. Phys. B 27 18203
[56] Bao L, Zhang X, Lei J and Tan Z J 2016 Chin. Phys. B 25 18703 |
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