Predicting Protein N-Terminal Signal Peptides Using Position-Specific Amino Acid Propensities and Conditional Random Fields

CORC > 天津工业生物技术研究所 > 结构生物信息学和整合系统生物学实验室宋江宁 > 期刊论文

	Predicting Protein N-Terminal Signal Peptides Using Position-Specific Amino Acid Propensities and Conditional Random Fields
	Fan, Yong-Xian 1; Song, Jiangning 2,3,4; Xu, Chen 5,6; Shen, Hong-Bin 1,6,7
刊名	CURRENT BIOINFORMATICS
	2013-04-01
卷号	8 期号:2 页码:183-192
关键词	Conditional random fields (CRFs) hydrophobicity alignment position-specific amino acid propensities secretory protein signal peptide
英文摘要	Protein signal peptides play a vital role in targeting and translocation of most secreted proteins and many integral membrane proteins in both prokaryotes and eukaryotes. Consequently, accurate prediction of signal peptides and their cleavage sites is an important task in molecular biology. In the present study, firstly, we develop a novel discriminative scoring method for classifying proteins with or without signal peptides. This method successfully captured the characteristics of signal peptides and non-signal peptides by integrating hydrophobicity alignment and position-specific amino acid propensities based on the highest average positions. As a result, this method is capable of discriminating proteins with signal peptides at the overall accuracies of 96.3%, 97.0% and 97.2% by leave-one-out jackknife tests on the constructed benchmark datasets for three different organisms, i.e. Eukaryotic, Gram-negative, and Gram-positive respectively. Secondly, we consider the prediction task of signal peptide cleavage sites as a sequence labeling problem and apply Conditional Random Fields (CRFs) algorithm to solve it. Experimental results demonstrate that the proposed CRFs-based cleavage site finding approach can achieve the prediction success rates of 80.8%, 89.4%, and 74.0% respectively, for the secretory proteins from three different organisms. An online tool, LnSignal, is established for labeling the N-terminal signal cleavage sites and is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/LnSignal.
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
类目[WOS]	Biochemical Research Methods ; Mathematical & Computational Biology
研究领域[WOS]	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
关键词[WOS]	COMBINED TRANSMEMBRANE TOPOLOGY ; SEQUENCE CLEAVAGE SITES ; SORTING SIGNALS ; SUBCELLULAR-LOCALIZATION ; NEURAL-NETWORK ; RECOGNITION ; IDENTIFICATION ; BACTERIA ; WINDOW
收录类别	SCI
语种	英语
WOS记录号	WOS:000320466700006
内容类型	期刊论文
源URL	[http://124.16.173.210/handle/834782/1234]
专题	天津工业生物技术研究所_结构生物信息学和整合系统生物学实验室宋江宁_期刊论文
作者单位	1.Shanghai Jiao Tong Univ, Dept Automat, Minist Educ China, Shanghai 200240, Peoples R China 2.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, State Engn Lab Ind Enzymes, Tianjin 300308, Peoples R China 3.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Tianjin 300308, Peoples R China 4.Monash Univ, Dept Biochem & Mol Biol, Fac Med, Melbourne, Vic 3800, Australia 5.Shanghai Jiao Tong Univ, Sch Med, Dept Histol & Embryol, Shanghai 200025, Peoples R China 6.Shanghai Key Lab Reprod Med, Shanghai 200025, Peoples R China 7.Shanghai Jiao Tong Univ, Shanghai Ctr Syst Biomed, Key Lab Syst Biomed, Minist Educ China, Shanghai 200240, Peoples R China
推荐引用方式 GB/T 7714	Fan, Yong-Xian,Song, Jiangning,Xu, Chen,et al. Predicting Protein N-Terminal Signal Peptides Using Position-Specific Amino Acid Propensities and Conditional Random Fields[J]. CURRENT BIOINFORMATICS,2013,8(2):183-192.
APA	Fan, Yong-Xian,Song, Jiangning,Xu, Chen,&Shen, Hong-Bin.(2013).Predicting Protein N-Terminal Signal Peptides Using Position-Specific Amino Acid Propensities and Conditional Random Fields.CURRENT BIOINFORMATICS,8(2),183-192.
MLA	Fan, Yong-Xian,et al."Predicting Protein N-Terminal Signal Peptides Using Position-Specific Amino Acid Propensities and Conditional Random Fields".CURRENT BIOINFORMATICS 8.2(2013):183-192.