DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites
文献类型：期刊论文
| 作者 | Wang, Xiaofeng1; Yan, Renxiang2; Song, Jiangning3,4,5,6,7 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2016-03-22 |
| 卷号 | 6 |
| 英文摘要 | Protein dephosphorylation, which is an inverse process of phosphorylation, plays a crucial role in a myriad of cellular processes, including mitotic cycle, proliferation, differentiation, and cell growth. Compared with tyrosine kinase substrate and phosphorylation site prediction, there is a paucity of studies focusing on computational methods of predicting protein tyrosine phosphatase substrates and dephosphorylation sites. In this work, we developed two elegant models for predicting the substrate dephosphorylation sites of three specific phosphatases, namely, PTP1B, SHP-1, and SHP-2. The first predictor is called MGPS-DEPHOS, which is modified from the GPS (Group-based Prediction System) algorithm with an interpretable capability. The second predictor is called CKSAAP-DEPHOS, which is built through the combination of support vector machine (SVM) and the composition of k-spaced amino acid pairs (CKSAAP) encoding scheme. Benchmarking experiments using jackknife cross validation and 30 repeats of 5-fold cross validation tests show that MGPS-DEPHOS and CKSAAP-DEPHOS achieved AUC values of 0.921, 0.914 and 0.912, for predicting dephosphorylation sites of the three phosphatases PTP1B, SHP-1, and SHP-2, respectively. Both methods outperformed the previously developed kNN-DEPHOS algorithm. In addition, a web server implementing our algorithms is publicly available at http://genomics.fzu.edu.cn/dephossite/for the research community. |
| WOS标题词 | Science & Technology |
| 类目[WOS] | Multidisciplinary Sciences |
| 研究领域[WOS] | Science & Technology - Other Topics |
| 关键词[WOS] | REMOTE HOMOLOGY DETECTION ; BINDING PROTEIN IDENTIFICATION ; AMINO-ACID-COMPOSITION ; PHOSPHORYLATION SITES ; COMPUTATIONAL PREDICTION ; TYROSINE-PHOSPHATASES ; UBIQUITINATION SITES ; MICRORNA PRECURSOR ; WEB SERVER ; K-TUPLE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000372574800002 |
| 源URL | [http://124.16.173.210/handle/834782/1531]  |
| 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, Sch Math & Comp Sci, Linfen 041004, Peoples R China 2.Fuzhou Univ, Inst Appl Genom, Sch Biol Sci & Engn, Fuzhou 350002, Peoples R China 3.Monash Univ, Infect & Immun Program, Clayton, Vic 3800, Australia 4.Monash Univ, Dept Biochem & Mol Biol, Biomed Discovery Inst, Clayton, Vic 3800, Australia 5.Monash Univ, Fac Informat Technol, Monash Ctr Data Sci, Clayton, Vic 3800, Australia 6.Chinese Acad Sci, Natl Engn Lab Ind Enzymes, Tianjin 300308, Peoples R China 7.Chinese Acad Sci, Key Lab Syst Microbial Biotechnol, Tianjin Inst Ind Biotechnol, Tianjin 300308, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Xiaofeng,Yan, Renxiang,Song, Jiangning. DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites[J]. SCIENTIFIC REPORTS,2016,6. |
| APA | Wang, Xiaofeng,Yan, Renxiang,&Song, Jiangning.(2016).DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.SCIENTIFIC REPORTS,6. |
| MLA | Wang, Xiaofeng,et al."DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites".SCIENTIFIC REPORTS 6(2016). |
入库方式： OAI收割
来源：天津工业生物技术研究所
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