Dephosphorylation site detection software tools | Post-translational modification data analysis
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.
A web server for predicting substrate sites of three protein tyrosine phosphatases. Ptpset is based on the sequence features of manually collected dephosphorylation sites. The total of experimentally validated human substrate sites for PTP1B, SHP-1 and SHP-2 were 51, 42 and 44, respectively. With those reported substrate sites as positive samples and 1000 randomly selected negative samples, we utilized the k-NN algorithm to predict substrate sites for the above-mentioned PTPs. We scanned their possible dephosphorylation sites in the set of phosphorylation sites acquired by mass-spectrometer technique, which could provide numerous candidate substrates for traditional experiments.
Predicts the substrate dephosphorylation sites of three specific phosphatases, namely, PTP1B, SHP-1, and SHP-2. DephosSite provides two predictors, MGPS-DEPHOS and CKSAAP-DEPHOS. MGPS-DEPHOS is modified from Group-based Prediction System algorithm with an interpretable capability. CKSAAP-DEPHOS is built through the combination of support vector machine and the composition of k-spaced amino acid pairs encoding scheme. Benchmarking experiments using jackknife cross validation and 30-times 5-fold cross validation tests show that both methods outperformed the previous developed kNN algorithm.
Enables in silico identification of dephosphorylation. DephosSitePred provides a protein tyrosine phosphatases (PTP) prediction model built on bi-profile sequence features. It uses sequence based bi-profile Bayes feature extraction technique to detect phosphatases. This software aims to reveal physiological and pathological role of dephosphorylation protein.
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