PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites

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	PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites
	Song, Jiangning 1,2,3; Tan, Hao 1; Perry, Andrew J.1; Akutsu, Tatsuya 4; Webb, Geoffrey I.5; Whisstock, James C.1,6; Pike, Robert N.1
刊名	PLOS ONE
	2012-11-29
卷号	7 期号:11 页码:e50300
英文摘要	The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.
WOS标题词	Science & Technology
类目[WOS]	Multidisciplinary Sciences
研究领域[WOS]	Science & Technology - Other Topics
关键词[WOS]	SUPPORT VECTOR REGRESSION ; GENE-EXPRESSION DATA ; SVM-BASED PREDICTION ; SECONDARY STRUCTURE ; EVOLUTIONARY INFORMATION ; DISULFIDE CONNECTIVITY ; UNSTRUCTURED PROTEINS ; REGULATORY NETWORKS ; WIDE IDENTIFICATION ; INTRINSIC DISORDER
收录类别	SCI
语种	英语
WOS记录号	WOS:000312104900037
公开日期	2013-01-11
内容类型	期刊论文
源URL	[http://124.16.173.210/handle/312001/301]
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.Monash Univ, Dept Biochem & Mol Biol, Melbourne, Vic 3004, Australia 2.Chinese Acad Sci, Natl Engn Lab Ind Enzymes, Tianjin, Peoples R China 3.Chinese Acad Sci, Key Lab Syst Microbial Biotechnol, Inst Ind Biotechnol, Tianjin, Peoples R China 4.Kyoto Univ, Bioinformat Ctr, Inst Chem Res, Uji, Kyoto, Japan 5.Monash Univ, Fac Informat Technol, Melbourne, Vic 3004, Australia 6.Monash Univ, ARC Ctr Excellence Struct & Funct Microbial Genom, Melbourne, Vic 3004, Australia
推荐引用方式 GB/T 7714	Song, Jiangning,Tan, Hao,Perry, Andrew J.,et al. PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites[J]. PLOS ONE,2012,7(11):e50300.
APA	Song, Jiangning.,Tan, Hao.,Perry, Andrew J..,Akutsu, Tatsuya.,Webb, Geoffrey I..,...&Pike, Robert N..(2012).PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites.PLOS ONE,7(11),e50300.
MLA	Song, Jiangning,et al."PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites".PLOS ONE 7.11(2012):e50300.