Last updated: July 2th, 2014
Introduction :
As one of the most important and ubiquitous post-translational modifications (PTMs), protein phosphorylation regulates a broad spectrum of biological processes not only in humans but also in plants. The identification of site-specific phosphorylated substrates is fundamental for understanding the regulatory molecular mechanisms of protein phosphorylation in controlling plant growth and development. Besides experimental approaches, prediction of potential candidates with computational methods has also attracted great attention for its convenience and fast-speed. In this review, we present a comprehensive but brief summarization of computational resources for protein phosphorylation in plants, including databases and predictors.
We apologized that the computational studies without any web links of databases or tools will not be included in this compendium, since it's not easy for experimentalists to use studies directly. We are grateful for user feedback. Please inform Han Cheng, Wankun Deng, Dr. Zexian Liu, or Dr. Yu Xue to add, remove or update one or multiple web links below.
Index:
<1> Plant Phosphorylation Databases
<2> Prediction of plant phosphorylation sites
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<1> Plant Phosphorylation Databases:
1. P3DB 3.0 : provides a database of protein phosphorylation data for 47,923 phosphosites in 16,477 phosphoproteins curated across nine plant organisms from 32 studies (Yao, et al., 2014 ).
2. ProMEX : a mass spectral reference database for proteins and protein phosphorylation sites, containing 30,483 spectra of 12,119 different Proteins and 25,311 different peptides to date (Wienkoop, et al., 2012).
3. TAIR: maintains a database of genetic and molecular data for Arabidopsis thaliana. Protein data available from TAIR includes the complete protein sequence along with phosphorylation site annotations (Lamesch, et al., 2012).
4. MPPD: a repository for Medicago truncatula phosphoprotein data which holds 3,404 non-redundant sites of phosphorylation on 829 proteins (Rose, et al., 2012).
5. PhosPhAt 4.0: contains information on Arabidopsis phosphorylation sites which were identified by mass spectrometry in large scale experiments from different research groups with 6,282 phosphopeptides (Heazlewood, et al., 2008 , Durek, et al., 2010 ).
6. PlantsP: contains more than 300 phosphorylation sites from Arabidopsis thaliana plasma membrane proteins (Nühse, et al., 2009).
<2> Prediction of plant phosphorylation sites:
1. PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine. The model trained by the animal phosphorylation sites was also applied to a plant phosphorylation site dataset as an independent test (Dou et al., 2014).
2. PHOSFER: a novel tool for predicting phosphorylation sites in soybean using known phosphorylation sites from both soybean and other organisms (Trost and Kusalik et al., 2013).
3. Musite: is a tool that was previously developed to predict phosphorylation sites based solely on protein sequence. Now, it also applied to plants which based on that Arabidopsis data only was trained and cross-organism testing was then performed on other plants (Yao et al., 2012).
4. PhosphoRice: a meta-predictor of rice-specific phosphorylation site by using weighted voting strategy with parameters selected by restricted grid search and conditional random search (Que et al., 2012).
5. MAP Kinase analyzer: is a tool which identifies the phosphorylation site, phosphorylation site consensus sequences and domain of the MAPK in plant genome altogether (Samantaray et al., 2011).
6. PlantPhos: is a web tool for predicting potential phosphorylation sites in plant proteins with various substrate motifs based on Hidden Markov Models (HMM) and Maximal Dependence Decomposition (MDD) (Lee et al., 2011).
7. PhosPhAt 4.0: they utilized a set of 802 experimentally validated serine phosphorylation sites as the training data set in their 2.2 version, while with additional 1,818 threonine phosphorylation sites and 676 tyrosine sites in Arabidopsis to develop their 3.0 predictor for phosphorylation sites in Arabidopsis (Durek, et al., 2010 ).
8. DISPHOS 1.3: uses position-specific amino acid frequencies and disorder information to improve the discrimination between phosphorylation and non-phosphorylation sites, and predicts serine, threonine and tyrosine phosphorylation sites in proteins. DisPhos also adapts to Arabidopsis (Iakoucheva et al., 2004).