title: DephosSitePred: A High Accuracy Predictor for Protein Dephosphorylation Sites.
abstract: OBJECTIVE
  Protein tyrosine phosphatases (PTPs) are responsible for protein phosphorylation. Because the level of protein phosphorylation is correlated with tumor transformation, PTPs have been considered as candidate transformation suppressors. In this study, we developed a novel PTP site prediction model, DephosSitePred, based on bi-profile sequence features.
  
  
  METHODS
  A dataset which contains 63-, 50- and 51-positive samples, and 868-, 856-, and 731-negative samples with less than 70% sequence identity for the three phosphatases was constructed in this study. Based on the dataset, a predictor model DephosSitePred was constructed, by applying the sequence-based bi-profile Bayes feature extraction technique to identify three phosphatases, PTP1B, SHP-1, and SHP-2. Concerning the imbalance of datasets used in our study, the weight parameters (W1 and W-1) of the support vector machine (SVM) were selected according to jackknife cross-validation.
  
  
  RESULTS
  DephosSitePred yielded Matthews correlation coefficients of 0.686 for protein tyrosine phosphatase 1B (PTP1B), 0.668 for Src homology region 2 domain-containing phosphatase (SHP)-1, and 0.748 for SHP-2 substrate sites, which significantly outperformed other existing predictors. Moreover, 30 times of 5-fold cross-validations showed that DephosSitePred achieved average area under the curve values of 0.968, 0.968, and 0.982 for PTP1B, SHP-1 and SHP-2, respectively, which were 0.115, 0.105 and 0.105 higher than those of the second best model, MGPS-DEPHOS, respectively.
  
  
  CONCLUSIONS
  DephosSitePred is indeed an effective auxiliary tool for in silico identification of dephosphorylation sites and may help to reveal the physiological and pathological role of dephosphorylation protein.
authors.affiliation: Department of Mathematics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China.
authors.forename: Cangzhi
authors.initials: C
authors.lastname: Jia
authors.affiliation: Department of Mathematics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China.
authors.forename: Wenying
authors.initials: W
authors.lastname: He
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin Shi, China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001665:Binding Sites; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D010766:Phosphorylation; D011499:Protein Processing, Post-Translational; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1; D054592:Protein Tyrosine Phosphatase, Non-Receptor Type 11; D053824:Protein Tyrosine Phosphatase, Non-Receptor Type 6; D017027:Protein Tyrosine Phosphatases / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: 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; D017027:Protein Tyrosine Phosphatases
keywords: Bi-profile; PTP; Protein tyrosine phosphatase; SVM; prediction; weight parameter