SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites
文献类型：期刊论文
| 作者 | Wang, Xiaofeng1; Yan, Renxiang2; Li, Jinyan3,4; Song, Jiangning5,6,7,8,9 |
| 刊名 | MOLECULAR BIOSYSTEMS
 |
| 出版日期 | 2016 |
| 卷号 | 12期号:9页码:2849-2858 |
| 英文摘要 | Protein S-sulfenylation (SOH) is a type of post-translational modification through the oxidation of cysteine thiols to sulfenic acids. It acts as a redox switch to modulate versatile cellular processes and plays important roles in signal transduction, protein folding and enzymatic catalysis. Reversible SOH is also a key component for maintaining redox homeostasis and has been implicated in a variety of human diseases, such as cancer, diabetes, and atherosclerosis, due to redox imbalance. Despite its significance, the in situ trapping of the entire 'sulfenome' remains a major challenge. Yang et al. have recently experimentally identified about 1000 SOH sites, providing an enriched benchmark SOH dataset. In this work, we developed a new ensemble learning tool SOHPRED for identifying protein SOH sites based on the compositions of enriched amino acids and the physicochemical properties of residues surrounding SOH sites. SOHPRED was built based on four complementary predictors, i.e. a naive Bayesian predictor, a random forest predictor and two support vector machine predictors, whose training features are, respectively, amino acid occurrences, physicochemical properties, frequencies of k-spaced amino acid pairs and sequence profiles. Benchmarking experiments on the 5-fold cross validation and independent tests show that SOHPRED achieved AUC values of 0.784 and 0.799, respectively, which outperforms several previously developed tools. As a real application of SOHPRED, we predicted potential SOH sites for 193 S-sulfenylated substrates, which had been experimentally detected through a global sulfenome profiling in living cells, though the actual SOH sites were not determined. The web server of SOHPRED has been made publicly available at http://genomics.fzu.edu.cn/SOHPRED for the wider research community. The source codes and the benchmark datasets can be downloaded from the website. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Biochemistry & Molecular Biology |
| 研究领域[WOS] | Biochemistry & Molecular Biology |
| 关键词[WOS] | MAXIMAL DEPENDENCE DECOMPOSITION ; AMINO-ACID-COMPOSITION ; TRIMERIC COILED-COILS ; OXIDATIVE STRESS ; UBIQUITINATION SITES ; SECONDARY STRUCTURE ; MEMBRANE-PROTEINS ; REDOX REGULATION ; SEQUENCES ; FEATURES |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000382253100016 |
| 源URL | [http://124.16.173.210/handle/834782/2922]  |
| 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.Shanxi Normal Univ, Coll Math & Comp Sci, Linfen 041004, Peoples R China 2.Fuzhou Univ, Sch Biol Sci & Engn, Inst Appl Genom, Fuzhou 350002, Peoples R China 3.Univ Technol Sydney, Adv Analyt Inst, Ultimo, NSW 2007, Australia 4.Univ Technol Sydney, Ctr Hlth Technol, Ultimo, NSW 2007, Australia 5.Monash Univ, Biomed Discovery Inst, Infect & Immun Program, Clayton, Vic 3800, Australia 6.Monash Univ, Dept Biochem & Mol Biol, Clayton, Vic 3800, Australia 7.Monash Univ, Fac Informat Technol, Monash Ctr Data Sci, Clayton, Vic 3800, Australia 8.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Natl Engn Lab Ind Enzymes, Tianjin 300308, Peoples R China 9.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Key Lab Syst Microbial Biotechnol, Tianjin 300308, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Xiaofeng,Yan, Renxiang,Li, Jinyan,et al. SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites[J]. MOLECULAR BIOSYSTEMS,2016,12(9):2849-2858. |
| APA | Wang, Xiaofeng,Yan, Renxiang,Li, Jinyan,&Song, Jiangning.(2016).SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites.MOLECULAR BIOSYSTEMS,12(9),2849-2858. |
| MLA | Wang, Xiaofeng,et al."SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites".MOLECULAR BIOSYSTEMS 12.9(2016):2849-2858. |
入库方式： OAI收割
来源：天津工业生物技术研究所
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