Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

doi:10.1186/s12859-021-04101-y

	Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks
	Yang, Yingxi 1; Wang, Hui 2; Li, Wen 1; Wang, Xiaobo 1; Wei, Shizhao 3; Liu, Yulong 3; Xu, Yan 1
	2021-03-31
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	22 期号:1 页码:17
摘要	BackgroundProtein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins.MethodWe proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories.ResultsIn the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN.ConclusionsThe CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
关键词	Post-translational modification Deep learning Generative adversarial networks Random forest
DOI	10.1186/s12859-021-04101-y
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[12071024] ; Ministry of Science and Technology of China[2019AAA0105103]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:000636449300003
出版者	BMC
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16735
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Yan
作者单位	1.Univ Sci & Technol Beijing, Dept Informat & Comp Sci, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China 3.China Elect Technol Grp Corp, Res Inst 15, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Yang, Yingxi,Wang, Hui,Li, Wen,et al. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks[J]. BMC BIOINFORMATICS,2021,22(1):17.
APA	Yang, Yingxi.,Wang, Hui.,Li, Wen.,Wang, Xiaobo.,Wei, Shizhao.,...&Xu, Yan.(2021).Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC BIOINFORMATICS,22(1),17.
MLA	Yang, Yingxi,et al."Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks".BMC BIOINFORMATICS 22.1(2021):17.