全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于Wasserstein生成对抗网络和残差网络的8类蛋白质二级结构预测
Protein 8-State Secondary Structure Prediction Based on Wasserstein Generative Adversarial Network and Residual Network

DOI: 10.12677/HJCB.2023.131001, PP. 1-9

Keywords: Wasserstein生成对抗网络,残差网络,蛋白质二级结构预测,Wasserstein Generative Adversarial Network, Residual Network, Protein Secondary Structure Prediction

Full-Text   Cite this paper   Add to My Lib

Abstract:

蛋白质二级结构包含充分的蛋白质信息,而且蛋白质二级结构是研究蛋白质三级结构和药物设计的基础,因此,本文提出了一种基于Wasserstein生成对抗网络(WGAN)和残差网络(ResNet)的蛋白质8态二级结构预测的方法。该方法首先通过Wasserstein生成对抗网络(WGAN)提取蛋白质特征,将其与PSSM结合成新的特征集合,然后将新的特征集合输入到残差网络(ResNet)预测并得到最终结果。经过实验,该方法在测试集CASP10-14和CB513中的Q8预测准确率分别为73.21%,72.43%,71.67%,69.83%,70.17%和73.89%。通过实验表明,Wasserstein生成对抗网络(WGAN)具有出色的特征提取能力,ResNet能够有效地训练深层网络结构,从而提高蛋白质二级结构的预测精度。
Protein secondary structure is the basis for studying protein tertiary structure and drug design, because the 8-state protein secondary structure can provide sufficient protein information for this. Therefore, this paper proposes a method for predicting the 8-state secondary structure of proteins based on Wasserstein generative adversarial network (WGAN) and residual network (ResNet). This method first extracts protein features by Wasserstein generative adversarial network (WGAN), combines them with PSSM to form a new feature set, and then inputs the new feature set to the residual network (ResNet) prediction and obtains the final result. After experiments, the Q8 prediction accuracy of this method in the test set CASP10-14 and CB513 was 73.21%, 72.43%, 71.67%, 69.83%, 70.17% and 73.89%, respectively. Experiments show that the Wasserstein generative adversarial network (WGAN) has excellent feature extraction ability, and ResNet can effectively train the deep network structure, thereby improving the prediction accuracy of protein secondary structure.

References

[1]  Zhou, J. and Troyanskaya, O. (2014) Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction. Proceedings of the 31st International Conference on Machine Learning, Beijing, 22-24 June 2014, 745-753.
[2]  Yaseen, A. and Li, Y. (2014) Template-Based C8-Scorpion: A Protein 8-State Secondary Structure Prediction Method Using Structural Information and Context-Based Features. BMC Bioinformatics, 15, Article No. S3.
https://doi.org/10.1186/1471-2105-15-S8-S3
[3]  Kabsch, W. and Sander, C. (1983) Dictionary of Protein Sec-ondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features. Biopolymers, 22, 2577-2637.
https://doi.org/10.1002/bip.360221211
[4]  Senior, A.W., Evans, R., Jumper, J., et al. (2020) Improved Protein Structure Prediction Using Potentials from Deep Learning. Nature, 577, 706-710.
https://doi.org/10.1038/s41586-019-1923-7
[5]  骆建新, 郑崛村, 马用信, 张思仲. 人类基因组计划与后基因组时代[J]. 中国生物工程杂志, 2003, 23(11): 87-94.
[6]  Jumper, J., Evans, R., Pritzel, A., et al. (2021) Highly Ac-curate Protein Structure Prediction with AlphaFold. Nature, 596, 583-589.
https://doi.org/10.1038/s41586-021-03819-2
[7]  Busia, A. and Jaitly, N. (2017) Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction. ArXiv Preprint ArXiv: 1702.03865.
[8]  Zhang, B., Li, J. and Lü, Q. (2018) Prediction of 8-State Protein Secondary Structures by a Novel Deep Learning Architecture. BMC Bioinformatics, 19, Article No. 293.
https://doi.org/10.1186/s12859-018-2280-5
[9]  Krieger, S. and Kececioglu, J. (2020) Boosting the Accuracy of Protein Secondary Structure Prediction through Nearest Neighbor Search and Method Hybridization. Bioinformatics, 36, i317-i325.
https://doi.org/10.1093/bioinformatics/btaa336
[10]  Uddin, M.R., Mahbub, S., Rahman, M.S. and Bayzid, M.S. (2020) SAINT: Self-Attention Augmented Inception-Inside-Inception Network Improves Protein Secondary Structure Prediction. Bioinformatics, 36, 4599-4608.
https://doi.org/10.1093/bioinformatics/btaa531
[11]  Sonsare, P.M. and Gunavathi, C. (2021) Cascading 1D-Convnet Bidirectional Long Short Term Memory Network with Modified COCOB Optimizer: A Novel Approach for Protein Secondary Structure Prediction. Chaos, Solitons & Fractals, 153, Article ID: 111446.
https://doi.org/10.1016/j.chaos.2021.111446
[12]  Zvelebil, M. and Baum, J.O. (2007) Understanding Bioinformat-ics. Garland Science, London.
https://doi.org/10.1201/9780203852507
[13]  Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J. (1997) Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs. Nucleic Acids Research, 25, 3389-3402.
https://doi.org/10.1093/nar/25.17.3389
[14]  Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2020) Generative Ad-versarial Nets. Communications of the ACM, 63, 139-144.
https://doi.org/10.1145/3422622
[15]  Wang, R., Xiao, X., Guo, B., Qin, Q. and Chen, R. (2018) An Effective Image Denoising Method for UAV Images via Improved Gener-ative Adversarial Networks. Sensors, 18, Article No. 1985.
https://doi.org/10.3390/s18071985
[16]  Yu, S., Chen, H., Reyes, E.B.G. and Poh, N. (2017) GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Net-works. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, 21-26 July 2017, 532-539.
https://doi.org/10.1109/CVPRW.2017.80
[17]  赵亚武, 张华兰, 刘毅慧. 基于生成对抗和卷积神经网络的蛋白质二级结构预测[J]. 计算生物学, 2020, 10(4): 49-56.
[18]  Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90.
https://doi.org/10.1145/3065386
[19]  He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Ve-gas, 27-30 June 2016, 770-778.
https://doi.org/10.1109/CVPR.2016.90
[20]  Wang, G. and Dunbrack, R.L. (2005) PISCES: Recent Improvements to a PDB Sequence Culling Server. Nucleic Acids Research, 33, W94-W98.
https://doi.org/10.1093/nar/gki402
[21]  Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T. and Tramontano, A. (2014) Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round X. Proteins: Structure, Func-tion, and Bioinformatics, 82, 1-6.
https://doi.org/10.1002/prot.24452
[22]  Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T. and Tramontano, A. (2016) Critical Assessment of Methods of Protein Structure Prediction: Progress and New Directions in Round XI. Pro-teins: Structure, Function, and Bioinformatics, 84, 4-14.
https://doi.org/10.1002/prot.25064
[23]  Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T. and Tramontano, A. (2018) Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round XII. Proteins: Structure, Function, and Bioinformatics, 86, 7-15.
https://doi.org/10.1002/prot.25415
[24]  Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. and Moult, J. (2019) Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round XIII. Proteins: Structure, Function, and Bioinformatics, 87, 1011-1020.
https://doi.org/10.1002/prot.25823
[25]  Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. and Moult, J. (2021) Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round XIV. Proteins: Structure, Function, and Bioinformatics, 89, 1607-1617.
https://doi.org/10.1002/prot.26237
[26]  Cuff, J.A. and Barton, G.J. (1999) Evaluation and Improvement of Multi-ple Sequence Methods for Protein Secondary Structure Prediction. Proteins: Structure, Function, and Bioinformatics, 34, 508-519.
https://doi.org/10.1002/(SICI)1097-0134(19990301)34:4<508::AID-PROT10>3.0.CO;2-4

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133