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. 2006 Mar 20:7:163.
doi: 10.1186/1471-2105-7-163.

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

Yu Xue  1 , Ao Li, Lirong Wang, Huanqing Feng, Xuebiao Yao
Affiliations

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

Yu Xue et al. BMC Bioinformatics. .

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.

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Figures

Figure 1
Figure 1
The outline of the training and procedure of PPSP.
Figure 2
Figure 2
the distribution of risk difference of random and human proteome data set of PKA-specific site prediction is diagramed in Figure 2. A. Distribution of Risk Difference of random data set (serine) of PKA-specific site prediction. B. Distribution of Risk Difference of random data set (threonine) of PKA-specific site prediction. C. Distribution of Risk Difference of human proteome data set (serine) of PKA-specific site prediction. D. Distribution of Risk Difference of Human proteome data set (threonine) of PKA-specific site prediction.
Figure 3
Figure 3
The prediction results of Bluetongue virus (BTV) nonstructural protein 2 (NS2), Drosophila transcription factor GAGA and human Calmodulin protein with PPSP. Figure 3A – prediction results of NS2; Figure 3B – prediction results of GAGA; Figure 3C – prediction results of Calmodulin.
Figure 4
Figure 4
The diagram of potential phosphorylation sites of human RasGrf1 (Q13972) and TID1 (Q96EY1) by TRK.
See this image and copyright information in PMC

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