Support vector machine approach for protein subcellular localization prediction

Bioinformatics. 2001 Aug;17(8):721-8. doi: 10.1093/bioinformatics/17.8.721.


Motivation: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences.

Results: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals.

Availability: A web server implementing the prediction method is available at

Supplementary information: Supplementary material is available at

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amino Acid Sequence
  • Amino Acids / analysis
  • Computational Biology
  • Computer Simulation*
  • Databases, Protein
  • Internet
  • Models, Biological*
  • Proteins / chemistry
  • Proteins / metabolism*
  • Software
  • Subcellular Fractions / metabolism*


  • Amino Acids
  • Proteins