Prediction of phosphorylation sites using SVMs

Bioinformatics. 2004 Nov 22;20(17):3179-84. doi: 10.1093/bioinformatics/bth382. Epub 2004 Jul 1.


Motivation: 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.

Results: 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.

Publication types

  • Comparative Study
  • Evaluation Study
  • Validation Study

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Binding Sites
  • Computer Simulation
  • Models, Chemical*
  • Models, Molecular*
  • Phosphorylation*
  • Phosphotransferases / chemistry*
  • Proteins / chemistry*
  • Sequence Alignment
  • Sequence Analysis, Protein / methods*
  • Structure-Activity Relationship


  • Proteins
  • Phosphotransferases