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

Abstract

BackgroundAs 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.ResultsIn 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%.ConclusionsPhosphoRice 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.

Rice Functional Genomics Research: Past Decade and Future

“…Combination of PolyMAC and TiO 2 technologies has successfully identified 2000 phosphoproteins from mature stigma and embryonic tissues, which will greatly facilitate the studies of the development and pollination of rice stigma (Wang et al, 2014b). To further develop proteomics and integrate the available data, some databases of proteomics have been constructed, including PhosphoRice, a meta-predictor of ricespecific phosphorylation sites (http://bioinformatics.fafu.edu.cn/ PhosphoRice; Que et al, 2012), Oryza PG-DB, a rice proteome database on shotgun proteogenomics (http://oryzapg.iab.keio. ac.jp/; Helmy et al, 2011), and PRIN, a predicted rice interactome network (http://bis.zju.edu.cn/prin/; Gu et al, 2011).…”

Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites

“…Phosphorylation is one of the most important protein post-translation modification (PTMs) in eukaryotes. It plays essential roles in the majority of biological pathways, regulating cellular processes like metabolism, proliferation, differentiation and apoptosis 1 . More than 30% of all eukaryotic proteins are estimated to undergo reversible phosphorylation 2 .…”

“…Most computational phosphorylation site predictors are not organism-specific predictors. However, with the increases in experimentally verified protein phosphorylation sites for different organisms, an increasing goal is to develop organism-specific phosphorylation predictors, such has occurred for yeast 12 , Arabidopsis 13 and rice 1 . The yeast-specific predictor, NetPhosYeast, outperforms existing generic predictors in the identification of phosphorylation sites in yeast 12 .…”

“…Phosphorylation proteins have been identified in rice treated with various hormones 17 and under different environmental conditions, including high salinity 18 , drought 19 and high temperature 20 . Many phosphorylation sites in rice were identified by Nakagami et al 21 However, current predictors perform poorly when individually used to predict phosphorylation sites in rice phosphoproteins 1 . We have therefore established a meta-predictor for rice-specific phosphorylation sites 1 .…”

“…Many phosphorylation sites in rice were identified by Nakagami et al 21 However, current predictors perform poorly when individually used to predict phosphorylation sites in rice phosphoproteins 1 . We have therefore established a meta-predictor for rice-specific phosphorylation sites 1 . However, this rice-specific predictor was not trained directly by the rice phosphorylation sites data, but was developed by integrating six newly predicting programs, including NetPhosK, NetPhos2.0, KinasePhos, PrePhospho 1.0, Scansite and DISPHOS.…”