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


CONCLUSIONS
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.
Xue
Yu
Y
School of Life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. yxue@mail.ustc.edu.cn
Li
Ao
A
Wang
Lirong
L
Feng
Huanqing
H
Yao
Xuebiao
X
D000595:Amino Acid Sequence; D001499:Bayes Theorem; D001665:Binding Sites; D008969:Molecular Sequence Data; D010766:Phosphorylation; D011485:Protein Binding; D011494:Protein Kinases / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D011494:Protein Kinases