Predicting the protein SUMO modification sites based on Properties Sequential Forward Selection (PSFS)

Biochem Biophys Res Commun. 2007 Jun 22;358(1):136-9. doi: 10.1016/j.bbrc.2007.04.097. Epub 2007 Apr 23.


Protein SUMO modification is an important post-translational modification and the optimization of prediction methods remains a challenge. Here, by using Support Vector Machines algorithm (SVM), a novel computational method was developed for SUMO modification site prediction based on Sequential Forward Selection (SFS) of hundreds of amino acid properties, which are collected by Amino Acid Index database ( Our method also compares with the 0/1 system, in which the 20 amino acids are represented by 20-dimensional vectors (A = 00000000000000000001, C = 00000000000000000010 and so on). The overall accuracy of leave-one-out cross-validation for our method reaches 89.18%, which is higher than 0/1 system. It indicated that the SUMO modification prediction process is highly related to the amino acid property and this approach here provide a helpful tool for further investigation of the SUMO modification and identification of sumoylation sites in proteins. The software is available at

MeSH terms

  • Algorithms
  • Binding Sites
  • Computational Biology
  • Databases, Protein
  • Protein Processing, Post-Translational*
  • SUMO-1 Protein / chemistry*
  • Sequence Analysis, Protein
  • Software


  • SUMO-1 Protein