A novel method for high accuracy sumoylation site prediction from protein sequences

BMC Bioinformatics. 2008 Jan 8;9:8. doi: 10.1186/1471-2105-9-8.


Background: Protein sumoylation is an essential dynamic, reversible post translational modification that plays a role in dozens of cellular activities, especially the regulation of gene expression and the maintenance of genomic stability. Currently, the complexities of sumoylation mechanism can not be perfectly solved by experimental approaches. In this regard, computational approaches might represent a promising method to direct experimental identification of sumoylation sites and shed light on the understanding of the reaction mechanism.

Results: Here we presented a statistical method for sumoylation site prediction. A 5-fold cross validation test over the experimentally identified sumoylation sites yielded excellent prediction performance with correlation coefficient, specificity, sensitivity and accuracy equal to 0.6364, 97.67%, 73.96% and 96.71% respectively. Additionally, the predictor performance is maintained when high level homologs are removed.

Conclusion: By using a statistical method, we have developed a new SUMO site prediction method - SUMOpre, which has shown its great accuracy with correlation coefficient, specificity, sensitivity and accuracy.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Amino Acid Sequence
  • Binding Sites
  • Molecular Sequence Data
  • Protein Binding
  • Protein Processing, Post-Translational / physiology*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sequence Analysis, Protein / methods*
  • Small Ubiquitin-Related Modifier Proteins / chemistry*
  • Small Ubiquitin-Related Modifier Proteins / metabolism*
  • Structure-Activity Relationship


  • Small Ubiquitin-Related Modifier Proteins