DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins

doi:10.1186/s12859-019-2677-9

	DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins
	Fu, Hongli 1; Yang, Yingxi 1; Wang, Xiaobo 1; Wang, Hui 2; Xu, Yan 1,3
	2019-02-18
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	20 页码:10
摘要	BackgroundProtein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eukaryotes. Experimental and clinical studies have shown that ubiquitination plays a key role in several human diseases, and recent advances in proteomic technology have spurred interest in identifying ubiquitination sites. However, most current computing tools for predicting target sites are based on small-scale data and shallow machine learning algorithms.ResultsAs more experimentally validated ubiquitination sites emerge, we need to design a predictor that can identify lysine ubiquitination sites in large-scale proteome data. In this work, we propose a deep learning predictor, DeepUbi, based on convolutional neural networks. Four different features are adopted from the sequences and physicochemical properties. In a 10-fold cross validation, DeepUbi obtains an AUC (area under the Receiver Operating Characteristic curve) of 0.9, and the accuracy, sensitivity and specificity exceeded 85%. The more comprehensive indicator, MCC, reaches 0.78. We also develop a software package that can be freely downloaded from https://github.com/Sunmile/DeepUbi.ConclusionOur results show that DeepUbi has excellent performance in predicting ubiquitination based on large data.
关键词	Ubiquitination Deep learning Convolutional neural networks
DOI	10.1186/s12859-019-2677-9
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[11671032] ; National Traditional Medicine Clinical Research Base Business Construction Special Topics[JDZX2015299]
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:000459116200003
出版者	BMC
引用统计	被引频次：52[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/2XEOYT63/3411
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 100190, Peoples R China 3.Univ Sci & Technol Beijing, Beijing Key Lab Magnetophotoelect Composite & Int, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Fu, Hongli,Yang, Yingxi,Wang, Xiaobo,et al. DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins[J]. BMC BIOINFORMATICS,2019,20:10.
APA	Fu, Hongli,Yang, Yingxi,Wang, Xiaobo,Wang, Hui,&Xu, Yan.(2019).DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.BMC BIOINFORMATICS,20,10.
MLA	Fu, Hongli,et al."DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins".BMC BIOINFORMATICS 20(2019):10.