Lysine Malonylation Identification in E-coli with Multiple Features

doi:10.2174/1570164615666181005104614

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Lysine Malonylation Identification in E-coli with Multiple Features
	Shao, Yuanhai 1; Wang, Hui 2; Yang, Yingxi 4; Xu, Yan 3,4
刊名	CURRENT PROTEOMICS
	2019
卷号	16 期号:3 页码:166-174
关键词	Malonylation support vector machine post translational modification E. coli Receive Operating Character (ROC) Prokaryotes
ISSN号	1570-1646
DOI	10.2174/1570164615666181005104614
英文摘要	Motivation: Lysine malonylation in eukaryote proteins had been found in 2011 through high-throughput proteomic analysis. However, it was poorly understood in prokaryotes. Recent researches have shown that maonylation in E. colt was significantly enriched in protein translation, energy metabolism pathways and fatty acid biosynthesis. Results: In this work we proposed a predictor to identify the lysine malonylation sites in E. coli through physicochemical properties, binary code and sequence frequency by support vector machine algorithm. The experimentally determined lysine malonylation sites were retrieved from the first and largest malonylome dataset in prokaryotes up to date. The physicochemical properties plus position specific amino acid sequence propensity features got the best results with AUC (the area under the Receive Operating Character curve) 0.7994, MCC (Mathew correlation coefficient) 0.4335 in 10-fold cross-validation. Meanwhile the AUC values were 0.7800, 0.7851 and 0.8050 in 6-fold, 8-fold and LOO (leave-one-out) cross-validation, respectively. All the ROC curves were close to each other which illustrated the robustness and performance of the proposed predictor. We also analyzed the sequence propensities through TwoSampleLogo and found some peptides differences with t-test p<0.01. The predictor had shown better results than those of other methods K-Nearest Neighbors, C4.5 decision tree, Naive Bayes and Random Forest. Functional analysis showed that malonylated proteins were involved in many transcription activities and diverse biological processes. Meanwhile we also developed an online package which could be freely downloaded https://github.com/Sunmile/Malonylation E.coli.
资助项目	Natural Science Foundation of China[11671032] ; Fundamental Research Funds for the Central Universities[FRF-TP-17-024A2] ; Natural Science Foundation of Hainan Province[118QN181] ; Scientific Research Foundation of Hainan University[(sk)1804]
WOS研究方向	Biochemistry & Molecular Biology
语种	英语
出版者	BENTHAM SCIENCE PUBL LTD
WOS记录号	WOS:000458617700001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/3402]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shao, Yuanhai
作者单位	1.Hainan Univ, Sch Econ & Management, Haikou 570228, Hainan, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China 3.Univ Sci & Technol Beijing, Beijing Key Lab Magnetophotoelect Composites & In, Beijing 100083, Peoples R China 4.Univ Sci & Technol Beijing, Dept Informat & Comp Sci, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Shao, Yuanhai,Wang, Hui,Yang, Yingxi,et al. Lysine Malonylation Identification in E-coli with Multiple Features[J]. CURRENT PROTEOMICS,2019,16(3):166-174.
APA	Shao, Yuanhai,Wang, Hui,Yang, Yingxi,&Xu, Yan.(2019).Lysine Malonylation Identification in E-coli with Multiple Features.CURRENT PROTEOMICS,16(3),166-174.
MLA	Shao, Yuanhai,et al."Lysine Malonylation Identification in E-coli with Multiple Features".CURRENT PROTEOMICS 16.3(2019):166-174.