[show abstract][hide abstract] ABSTRACT: 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.
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%.
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.
[show abstract][hide abstract] ABSTRACT: High temperature is a critical abiotic stress that reduces crop yield and quality. Rice (Oryza sativa L.) plants remodel their proteomes in response to high temperature stress. Moreover, phosphorylation is the most common form of protein post-translational modification (PTM). However, the differential expression of phosphoproteins induced by heat in rice remains unexplored.
Phosphoprotein in the leaves of rice under heat stress were displayed using two-dimensional electrophoresis (2-DE) and Pro-Q Diamond dye. Differentially expressed phosphoproteins were identified by MALDI-TOF-TOF-MS/MS and confirmed by Western blotting.
Ten heat-phosphoproteins were identified from twelve protein spots, including ribulose bisphos-phate carboxylase large chain, 2-Cys peroxiredoxin BAS1, putative mRNA binding protein, Os01g0791600 protein, OSJNBa0076N16.12 protein, putative H(+)-transporting ATP synthase, ATP synthase subunit beta and three putative uncharacterized proteins. The identification of ATP synthase subunit beta was further validated by Western-blotting. Four phosphorylation site predictors were also used to predict the phosphorylation sites and the specific kinases for these 10 phosphoproteins.
Heat stress induced the dephosphorylation of RuBisCo and the phosphorylation of ATP-β, which decreased the activities of RuBisCo and ATP synthase. The observed dephosphorylation of the mRNA binding protein and 2-Cys peroxiredoxin may be involved in the transduction of heat-stress signaling, but the functional importance of other phosphoproteins, such as H+-ATPase, remains unknown.
[show abstract][hide abstract] ABSTRACT: A series of elegant phosphorylation site prediction methods have been developed, which are playing an increasingly important role in accelerating the experimental characterization of phosphorylation sites in phosphoproteins. In this study, we selected six recently published methods (DISPHOS, NetPhosK, PPSP, KinasePhos, Scansite and PredPhospho) to evaluate their performance. First, we compiled three testing datasets containing experimentally verified phosphorylation sites for mammalian, Arabidopsis and rice proteins. Then, we present the prediction performance of the tested methods on these three independent datasets. Rather than quantitatively ranking the performance of these methods, we focused on providing an understanding of the overall performance of the predictors. Based on this evaluation, we found the following results: i) current phosphorylation site predictors are not effective for practical use and there is substantial need to improve phosphorylation site prediction; ii) current predictors perform poorly when used to predict phosphorylation sites in plant phosphoproteins, suggesting that a rice-specific predictor will be required to obtain confident computational annotation of phosphorylation sites in rice proteomics research; and iii) the tested predictors are complementary to some extent, implying that establishment of a meta-server might be a promising approach to developing an improved prediction system.
Protein and Peptide Letters 12/2009; 17(1):64-9. · 1.99 Impact Factor