Musite is a free and open source, easy to use bioinformatics tool for predicting both general and kinase-specific protein phosphorylation sites.
Musite contains pre-trained prediction models for 6 eukaryotic organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, and Arabidopsis thaliana.
It is advisable to train your own prediction models from your specific training data, using the customized model training tool in Musite.
Here are some key features of "Musite":
· To address the various limitations of current tools when applying to proteomes and to better utilize the large magnitude of experimentally verified phosphorylation sites, we developed a unique standalone application system Musite, specifically designed for large-scale prediction of both general and kinase-specific phosphorylation sites.
· Musite utilized local sequence similarity patterns (KNN scores) and generic features (disorder scores and amino acid frequencies) of phosphorylation sites, and employed a comprehensive machine learning approach to make predictions.
· Musite is the first tool that provides utility for training a phosphorylation-site prediction model from users` own data and supports continuous adjustment of stringency levels.
· Musite provides a user-friendly graphic user interface, which makes it easy for biologists to perform predictions in an automated fashion.
· Applications of Musite on six proteomes yielded tens of thousands of putative phosphorylation sites with high stringency. These predictions provide useful hypotheses for experimental validations.
· 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.
· Moreover, as an open-source software, Musite can be also served as an open platform for building machine learning application for phosphorylation-site prediction.
Requirements:
· Java 1.5 or later
