A deep learning method to more accurately recall known lysine acetylation sites

doi:10.1186/s12859-019-2632-9

	A deep learning method to more accurately recall known lysine acetylation sites
	Wu, Meiqi 1; Yang, Yingxi 1; Wang, Hui 2; Xu, Yan 1,3
	2019-01-23
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	20 页码:11
摘要	BackgroundLysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data.ResultsIn this work, a novel predictor named DeepAcet was developed to predict acetylation sites. Six encoding schemes were adopted, including a one-hot, BLOSUM62 matrix, a composition of K-space amino acid pairs, information gain, physicochemical properties, and a position specific scoring matrix to represent the modified residues. A multilayer perceptron (MLP) was utilized to construct a model to predict lysine acetylation sites in proteins with many different features. We also integrated all features and implemented the feature selection method to select a feature set that contained 2199 features. As a result, the best prediction achieved 84.95% accuracy, 83.45% specificity, 86.44% sensitivity, 0.8540 AUC, and 0.6993 MCC in a 10-fold cross-validation. For an independent test set, the prediction achieved 84.87% accuracy, 83.46% specificity, 86.28% sensitivity, 0.8407 AUC, and 0.6977 MCC.ConclusionThe predictive performance of our DeepAcet is better than that of other existing methods. DeepAcet can be freely downloaded from https://github.com/Sunmile/DeepAcet.
关键词	Lysine acetylation PTMs Deep learning
DOI	10.1186/s12859-019-2632-9
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[11671032] ; Fundamental Research Funds for the Central Universities[FRF-TP-17-024A2] ; 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:000456522700001
出版者	BMC
引用统计	被引频次：49[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/2XEOYT63/3473
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	Wu, Meiqi,Yang, Yingxi,Wang, Hui,et al. A deep learning method to more accurately recall known lysine acetylation sites[J]. BMC BIOINFORMATICS,2019,20:11.
APA	Wu, Meiqi,Yang, Yingxi,Wang, Hui,&Xu, Yan.(2019).A deep learning method to more accurately recall known lysine acetylation sites.BMC BIOINFORMATICS,20,11.
MLA	Wu, Meiqi,et al."A deep learning method to more accurately recall known lysine acetylation sites".BMC BIOINFORMATICS 20(2019):11.