Phosphorylation sites using SVMs
Phosphorylation sites using SVMs
Received on November 26, 2003; revised on June 19, 2004; accepted on June 22, 2004Advance Access Publication July 1, 2004
Phosphorylation is involved in diverse signal transduction pathways. By predicting phosphorylation sites and their kinases from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Using support vector machines, we attempted to predict phosphorylation sites and the type of kinase that acts at each site.
Our prediction system was limited to phosphorylation sites catalyzed by four protein kinase families and four protein kinase groups. The accuracy of the predictions ranged from 83 to 95% at the kinase family level, and 76–91% at the kinase group level. The prediction system used—PredPhospho—can be applied to the functional study of proteins, and can help predict the changes in phosphorylation sites caused by amino acid variations at intra- and interspecies levels. SVMs usage with phosphorylation.
Received on November 26, 2003; revised on June 19, 2004; accepted on June 22, 2004Advance Access Publication July 1, 2004
Phosphorylation is involved in diverse signal transduction pathways. By predicting phosphorylation sites and their kinases from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Using support vector machines, we attempted to predict phosphorylation sites and the type of kinase that acts at each site.
Our prediction system was limited to phosphorylation sites catalyzed by four protein kinase families and four protein kinase groups. The accuracy of the predictions ranged from 83 to 95% at the kinase family level, and 76–91% at the kinase group level. The prediction system used—PredPhospho—can be applied to the functional study of proteins, and can help predict the changes in phosphorylation sites caused by amino acid variations at intra- and interspecies levels. SVMs usage with phosphorylation.
