PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory.

@article{Xue2006, abstract = {BACKGROUND: As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated. RESULTS: In this work, we present a novel, versatile and comprehensive program, PPSP (Prediction of PK-specific Phosphorylation site), deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for approximately 70 PK (Protein Kinase) groups. Compared with four existing tools Scansite, NetPhosK, KinasePhos and GPS, PPSP is more accurate and powerful than these tools. Moreover, PPSP also provides the prediction for many novel PKs, say, TRK, mTOR, SyK and MET/RON, etc. The accuracy of these novel PKs are also satisfying. CONCLUSION: Taken together, we propose that PPSP could be a potentially powerful tool for the experimentalists who are focusing on phosphorylation substrates with their PK-specific sites identification. Moreover, the BDT strategy could also be a ubiquitous approach for PTMs, such as sumoylation and ubiquitination, etc.}, author = {Xue, Yu and Li, Ao and Wang, Lirong and Feng, Huanqing and Yao, Xuebiao }, citeulike-article-id = {2054460}, doi = {10.1186/1471-2105-7-163}, journal = {BMC Bioinformatics}, keywords = {alignment, amino-acid, analysis, article-nar, article-predikin, bayes, binding, data, kinase, molecular, phosphorylation, protein, sequence, sites, software, theorem}, posted-at = {2007-12-04 03:22:14}, priority = {2}, title = {PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory.}, url = {10.1186/1471-2105-7-163}, volume = {7}, year = {2006} }

TY - JOUR ID - Xue2006 L3 - citeulike-article-id:2054460 TI - PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory. JF - BMC Bioinformatics VL - 7 N2 - BACKGROUND: As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated. RESULTS: In this work, we present a novel, versatile and comprehensive program, PPSP (Prediction of PK-specific Phosphorylation site), deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for approximately 70 PK (Protein Kinase) groups. Compared with four existing tools Scansite, NetPhosK, KinasePhos and GPS, PPSP is more accurate and powerful than these tools. Moreover, PPSP also provides the prediction for many novel PKs, say, TRK, mTOR, SyK and MET/RON, etc. The accuracy of these novel PKs are also satisfying. CONCLUSION: Taken together, we propose that PPSP could be a potentially powerful tool for the experimentalists who are focusing on phosphorylation substrates with their PK-specific sites identification. Moreover, the BDT strategy could also be a ubiquitous approach for PTMs, such as sumoylation and ubiquitination, etc. KW - alignment KW - amino-acid KW - analysis KW - article-nar KW - article-predikin KW - bayes KW - binding KW - data KW - kinase KW - molecular KW - phosphorylation KW - protein KW - sequence KW - sites KW - software KW - theorem AU - Xue, Yu AU - Li, Ao AU - Wang, Lirong AU - Feng, Huanqing AU - Yao, Xuebiao PY - 2006/// UR - http://dx.doi.org/10.1186/1471-2105-7-163 ER -