Evaluating different descriptors for model design of antimicrobial peptides with enhanced activity toward P. aeruginosa

Håvard Jenssen, Tore Lejon, Kai Hilpert, Christopher D. Fjell, Artem Cherkasov, Robert Hancock

Research output: Contribution to journalArticle

58 Citations (Scopus)


The number of isolated drug-resistant pathogenic microbes has increased drastically over the past decades, demonstrating an urgent need for new therapeutic interventions. Antimicrobial peptides have for a long time been looked upon as an interesting template for drug optimization. However, the process of optimizing peptide antimicrobial activity and specificity, using large peptide libraries is both tedious and expensive. Here, we describe the construction of a mathematical model for prediction, prior to synthesis, of peptide antibacterial activity toward Pseudomonas aeruginosa. By use of novel descriptors quantifying the contact energy between neighboring amino acids in addition to a set of inductive and conventional quantitative structure-activity relationship descriptors, we are able to model the peptides antibacterial activity. Cross-correlation and optimization of the implemented descriptor values have enabled us to build a model (Bac2a- #2) that was able to correctly predict the activity of 84% of the tested peptides, within a twofold deviation window of the corresponding IC50 values, measured earlier. The predictive power, is an average of 10 submodels, each predicting the activity of 20 randomly excluded peptides, with a predictive success of 16.7 ± 1.6 peptides. The model has also been proven significantly more accurate than a simpler model (Bac2a- #1), where the inductive and conventional quantitative structure-activity relationship descriptors were excluded.

Original languageEnglish
Pages (from-to)134-142
Number of pages9
JournalChemical Biology and Drug Design
Issue number2
Publication statusPublished - 1 Aug 2007
Externally publishedYes


  • Antimicrobial peptides
  • Partial least square projections to latent structures
  • Prediction of activity
  • Pseudomonas aeruginosa
  • Quantitative structure-activity relationships
  • Screening libraries

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Pharmacology
  • Drug Discovery
  • Organic Chemistry

Cite this