title: DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.
abstract: 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.
authors.affiliation: School of Mathematics and Computer Science, Shanxi Normal University, Linfen 041004, China.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Wang
authors.affiliation: Institute of Applied Genomics, School of Biological Sciences and Engineering, Fuzhou University, Fuzhou 350002, China.
authors.forename: Renxiang
authors.initials: R
authors.lastname: Yan
authors.affiliation: Infection and Immunity Program and The Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.|National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
authors.forename: Jiangning
authors.initials: J
authors.lastname: Song
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D010766:Phosphorylation; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1 / Q000737:chemistry*; D054592:Protein Tyrosine Phosphatase, Non-Receptor Type 11 / Q000737:chemistry*; D053824:Protein Tyrosine Phosphatase, Non-Receptor Type 6 / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; C516607:PTPN1 protein, human; C516724:PTPN11 protein, human; C509249:PTPN6 protein, human; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1; D054592:Protein Tyrosine Phosphatase, Non-Receptor Type 11; D053824:Protein Tyrosine Phosphatase, Non-Receptor Type 6