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. 2012 Feb 3:8:5.
doi: 10.1186/1746-4811-8-5.

PhosphoRice: a meta-predictor of rice-specific phosphorylation sites

Shufu Que #  1   2 , Kuan Li #  2 , Min Chen #  2 , Yongfei Wang  2 , Qiaobin Yang  2 , Wenfeng Zhang  1   2 , Baoqian Zhang  1   2 , Bangshu Xiong  3 , Huaqin He  1   2
Affiliations

PhosphoRice: a meta-predictor of rice-specific phosphorylation sites

Shufu Que et al. Plant Methods. .

Abstract

Background: As a result of the growing body of protein phosphorylation sites data, the number of phosphoprotein databases is constantly increasing, and dozens of tools are available for predicting protein phosphorylation sites to achieve fast automatic results. However, none of the existing tools has been developed to predict protein phosphorylation sites in rice.

Results: In this paper, the phosphorylation site predictors, NetPhos 2.0, NetPhosK, Kinasephos, Scansite, Disphos and Predphosphos, were integrated to construct meta-predictors of rice-specific phosphorylation sites using several methods, including unweighted voting, unreduced weighted voting, reduced unweighted voting and weighted voting strategies. PhosphoRice, the meta-predictor produced by using weighted voting strategy with parameters selected by restricted grid search and conditional random search, performed the best at predicting phosphorylation sites in rice. Its Matthew's Correlation Coefficient (MCC) and Accuracy (ACC) reached to 0.474 and 73.8%, respectively. Compared to the best individual element predictor (Disphos_default), PhosphoRice archieved a significant increase in MCC of 0.071 (P < 0.01), and an increase in ACC of 4.6%.

Conclusions: PhosphoRice is a powerful tool for predicting unidentified phosphorylation sites in rice. Compared to the existing methods, we found that our tool showed greater robustness in ACC and MCC. PhosphoRice is available to the public at http://bioinformatics.fafu.edu.cn/PhosphoRice.

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Figures

Figure 1
Figure 1
Receiver operating characteristics curves of the prediction performance of meta predictors in comparison to that of the best element predictor (Disphos_default). In the diagrams, improved classification performance is indicated for predictors with increased area under the ROC. The areas under the ROC curve were showed in Table 4. A: ROC curve of unweight-voting predictor in comparison to Disphos_default. B: ROC curve of restricted-grid predictor in comparison to Disphos_default. C: ROC curve of random-voting predictor in comparison to Disphos_default. D: ROC curve of unreduced-weight-voting predictor in comparison to Disphos_default (by ACC). E: ROC curve of unreduced- weight-voting predictor in comparison to Disphos_default (by MCC). F: ROC curve of reduced- weight-voting predictor in comparison to Disphos_default (by ACC). G: ROC curve of reduced- weight-voting predictor in comparison to Disphos_default (by MCC). * By ACC: the weights of meta-predictor were selected to result in the optimal ACC; By MCC: the weights of meta-predictor were selected to result in the optimal MCC.
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