Computer-Based Analysis, Visualization, and Interpretation of Antimicrobial Peptide Activities

  • Ralf Mikut
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 618)

Abstract

This chapter describes a computer-based method for analyzing the quantitative structure–activity relationships (QSAR) of antimicrobial peptides. Quantitative or qualitative activity measurements and known peptide sequences are used as input for the analysis. The analysis steps consist of the preprocessing which specifically deals with dilution series from an assay with luminescent bacteria, transformation of quantitative activity values into activity classes, a feature extraction method using molecular descriptors for amino acids, feature selection methods, visualization strategies, the classifier model design for discrimination of active and inactive peptides, and the in silico design of promising new peptide candidates.

Key words

Antimicrobial peptides molecular descriptors QSAR data analysis 

References

  1. 1.
    Perkins, R., Fang, H., Tong, W., and Welsh, W. (2003) Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology. Environ. Toxicol. Chem. 22, 1666–1679.PubMedCrossRefGoogle Scholar
  2. 2.
    Hellberg, S., Sjostrom, M., Skagerberg, B., and Wold, S. (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J. Med. Chem. 30, 1126–1235.PubMedCrossRefGoogle Scholar
  3. 3.
    Cherkasov, A., Hilpert, K., Jenssen, H., Fjell, C. D., Waldbrook, M., Mullaly, S. C., Volkmer, R., and Hancock, R. E. (2008) Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS Chem. Biol., 4, 65–74.CrossRefGoogle Scholar
  4. 4.
    Frecer, V. (2006) QSAR analysis of antimicrobial and haemolytic effects of cyclic cationic antimicrobial peptides derived from protegrin-1. Bioorgan. Med. Chem. 14, 6065–6674.CrossRefGoogle Scholar
  5. 5.
    Hilpert, K., Winkler, D. F. H., and Hancock, R. E. W. (2007) Peptide arrays on cellulose support: SPOT synthesis – a time and cost efficient method for synthesis of large numbers of peptides in a parallel and addressable fashion. Nat. Protoc. 2, 1333–1349.PubMedCrossRefGoogle Scholar
  6. 6.
    Hilpert, K. (2009) High-throughput screening for antimicrobial peptides using the SPOT Technique. In Part II: Analysis, Properties, and Mechanisms of Antimicrobial Peptides (from this book).Google Scholar
  7. 7.
    Mikut, R. and Hilpert, K. (2009) Interpretable features for the activity prediction of short antimicrobial peptides using fuzzy logic. Int. J. Pept. Res. Ther. 15 (2), 129–137.CrossRefGoogle Scholar
  8. 8.
    Mikut, R., Burmeister, O., Braun, S., and Reischl, M. (2008a). The open source MATLAB toolbox Gait-CAD and its application to bioelectric signal processing. In Proceedings of DGBMT-Workshop Biosignal Analysis, Potsdam, 109–111.Google Scholar
  9. 9.
    Hildebrand, P. (2006). Zur Strukturvorhersage der Membranproteine. PhD thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät.Google Scholar
  10. 10.
    White, S. and Wimley, W. (1999) Membrane protein folding and stability: physical principles. Annu. Rev. Biophys. Biomol. Struct. 28, 319–365.PubMedCrossRefGoogle Scholar
  11. 11.
    Tatsuoka, M. M. (1988) Multivariate Analysis. New York: Mac Millan.Google Scholar
  12. 12.
    Mikut, R., Reischl, M., Ulrich, A., and Hilpert, K. (2008b). Data-based activity analysis and interpretation of small antibacterial peptides. In Proceedings of the 18th Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 189–203.Google Scholar
  13. 13.
    DeLean, A. (1978) Simultaneous analysis of families of sigmoidal curves: application to bioassay, radioligand assay, and physiological dose-response curves. Am. J. Physiol. Gastr. L. Physiol. 235, 97–102.Google Scholar
  14. 14.
    Sandberg, M., Eriksson, L., Jonsson, J., Sjostrom, M., and Wold, S. (1998) New chemical descriptors relevant for the design of biologically active peptides: a multivariate characterization of 87 amino acids. J. Med. Chem. 41, 2481–2491.PubMedCrossRefGoogle Scholar
  15. 15.
    Mekenyan, O., Nikolova, N., and Schmieder, P. (2003) Dynamic 3D QSAR techniques: applications in toxicology. J. Mol. Struct. THEOCHEM 622, 147–165.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ralf Mikut
    • 1
  1. 1.Institute for Applied Computer Science (IAI), Forschungszentrum Karlsruhe GmbHKarlsruheGermany

Personalised recommendations