Skip to main content
padlock icon - secure page this page is secure

DephosSitePred: A High Accuracy Predictor for Protein Dephosphorylation Sites

Buy Article:

$68.00 + tax (Refund Policy)

Aim and 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.

Materials and Method: 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.

Conclusion: 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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Bi-profile; PTP; Protein tyrosine phosphatase; SVM; prediction; weight parameter

Document Type: Research Article

Publication date: February 1, 2017

More about this publication?
  • Combinatorial Chemistry & High Throughput Screening publishes full length original research articles and reviews describing various topics in combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) and/or high throughput screening (e.g. developmental, practical or theoretical). Ancillary subjects of key importance, such as robotics and informatics, will also be covered by the journal. In these respective subject areas, Combinatorial Chemistry & High Throughput Screening is intended to function as the most comprehensive and up-to-date medium available. The journal should be of value to individuals engaged in the process of drug discoveryand development, in the settings of industry, academia or government.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more