Prediction of antibiotic resistance proteins from sequence-derived properties irrespective of sequence similarity

Int J Antimicrob Agents. 2008 Sep;32(3):221-6. doi: 10.1016/j.ijantimicag.2008.03.006. Epub 2008 Jun 25.


Increasing antibiotic resistance has become a worldwide challenge to the clinical treatment of infectious diseases. The identification of antibiotic resistance proteins (ARPs) would be helpful in the discovery of new therapeutic targets and the design of novel drugs to control the potential spread of antibiotic resistance. In this work, a support vector machine (SVM)-based ARP prediction system was developed using 1308 ARPs and 15587 non-ARPs. Its performance was evaluated using 313 ARPs and 7156 non-ARPs. The computed prediction accuracy was 88.5% for ARPs and 99.2% for non-ARPs. A potential application of this method is the identification of ARPs non-homologous to proteins of known function. Further genome screening found that ca. 3.5% and 3.2% of proteins in Escherichia coli and Staphylococcus aureus, respectively, are potential ARPs. These results suggest the usefulness of SVMs for facilitating the identification of ARPs. The software can be accessed at SARPI (Server for Antibiotic Resistance Protein Identification).

Publication types

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

MeSH terms

  • Amino Acid Sequence
  • Bacterial Proteins / chemistry*
  • Bacterial Proteins / genetics
  • Bacterial Proteins / metabolism
  • Computational Biology / methods*
  • Databases, Protein
  • Drug Resistance, Bacterial*
  • Escherichia coli / drug effects*
  • Escherichia coli / genetics
  • Escherichia coli / metabolism
  • Genome, Bacterial
  • Humans
  • Molecular Sequence Data
  • Predictive Value of Tests
  • Sequence Analysis, Protein*
  • Staphylococcus aureus / drug effects*
  • Staphylococcus aureus / genetics
  • Staphylococcus aureus / metabolism


  • Bacterial Proteins