Global In-silico identification of General and Kinase-Specific Phosphorylation Sites
Year: 2010
Type: article
Abstract: RP-7 Reversible protein phosphorylation is one of the most pervasive posttranslational modifications, regulating diverse cellular processes in various organisms. High-throughput experimental studies using mass spectrometry have identified thousands of phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well-studied systems. Since mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time consuming, and are biased towards abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is an attractive alternative for whole proteome annotation. Due to various limitations, current phosphorylation-site prediction tools were not well-designed for comprehensive assessment of proteomes. Here, we present a novel software tool, MUSite, specifically designed for large-scale prediction of both general and kinase-specific phosphorylation sites. We collected high confidence phosphoproteomics data from multiple organisms and used these to train prediction models by a comprehensive machine learning approach. Application of MUSite on proteomes of Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, and Arabidopsis thaliana yielded tens of thousands of phosphorylation-site predictions with high stringency. Cross-validation tests show that MUSite significantly outperforms existing tools for predicting general phosphorylation sites and is at least comparable to those for predicting kinase-specific phosphorylation sites. Furthermore, MUSite provides several other unique functionalities such as customized model training and continuous stringency selection by users. With the user-friendly graphical user interface, MUSite provides a useful bioinformatics tool to biologists for predicting phosphorylation sites en masse and training prediction models from custom phosphorylation data. In addition, with its easily-extensible open-source application programming interface (API), MUSite is aimed at being an open platform for community-based development of machine-learning based phosphorylation-site prediction applications. MUSite is available at http://musite.sourceforge.net/.
Source: PubMed Central
Authors: Jay J. Thelen, Keith Dunker, Dong Xu, Jun Gao
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2114847849