Abstract
Antimicrobial peptides are ubiquitous in nature where they play important roles in host defense and microbial control. More than 1,000 naturally occurring peptides have been described so far and those considered for pharmaceutical development have all been further optimized by rational approaches.
In recent years, high-throughput screening assays have been developed to specifically address optimization of AMPs. In addition to these cell-based in vivo systems, a range of computational in silico systems can be applied in order to predict the biological activity of AMPs for specific bacteria. Among them, quantitative structure–activity relationships (QSARs) method, which attempts to correlate chemical structure to biological measurement, has shown promising results in the optimization and discovery of peptide candidates. Therefore, this chapter is devoted to describe the QSAR method and recent progress applied in AMP.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zasloff, M. (2002) Antimicrobial peptides of multicellular organism. Nature 415, 389–395.
Mygind, P. H., Fischer, R. L., Schnorr, K. M., Hansen, M. T., Sonksen, C. P., Ludvigsen, S., Raventos, D., Buskov, S., Christensen, B., De Maria, L., Taboureau, O., Yaver, D., Elvig-Jørgensen, S. G., Sørensen, M. V., Christensen, B. E., Kjærulff, S., Frimodt-Moller, N., Lehrer, R. I., Zasloff, M., and Kristensen, H. H. (2005) Plectasin is a peptide antibiotic with therapeutic potential from a saprophytic fungus. Nature 437, 975–980.
Raventos, D., Taboureau, O., Mygind, P. H., Nielsen, J. D., Sonksen, C. P., and Kristensen, H. H. (2005) Improving on nature’s defenses: optimization and high throughput screening of antimicrobial peptides. CCHTS 8, 219–233.
Taboureau, O., Olsen, O. H., Nielsen, J. D., Raventos, D., Mygind, P. H., and Kristensen, H. H. (2005) Design of novispirin antimicrobial peptides by quantitative structure-activity relationship. Chem. Biol. Drug. Des. 68, 48–57.
Cherkasov, A. and Jankovic, B. (2004) Application of inductive QSAR descriptors for quantification of antibacterial activity of cationic polypeptides. Molecules 9, 1034–1052.
Fjell, C. D., Hancock, R. E. W., and Cherkasov, A. (2007) AMPer: A database and an automated discovery tool for antimicrobial peptides. Bioinformatics 23, 1148–1155.
Frecer, V., Ho, B., and Ding, J. L. (2004) De novo design of potent antimicrobial peptides. Antimicrob. Agents Chemother. 48, 3349–3357.
Friedrich, C. L., Moyles, D., Beveridge, T. J., and Hancock, R. E. (2000) Antibacterial action of structurally diverse cationic peptides on gram-positive bacteria. Antimicrob. Agents Chemother. 44, 208–209.
Tossi, A. (2005) Host defense peptides: roles and applications. Current Protein Pept. Sci. 6, 1–3.
Hellberg, S., Sjostrom, M., Skagerberg, B., and And Wold, S. (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J. Med. Chem. 30, 1126–1135.
Wold, S., Esbesen, K., and Geladi, P. (1987) Principal component analysis. Chemom. Intell. Lab. Syst. 2, 37–52.
Mee, R. P., Auton, T. R., and Morgan, P. J. (1997) Design of active analogues of a 15-residue peptide using D-optimal design, QSAR and a combinatorial search algorithm. J. Pept. Res. 49, 89–102.
Dudek, A. Z., Arodz, T., and Galvez, J. (2006) Computational methods in developing quantitative structure-activity relationships (QSAR): a review. CCHTS 9, 213–228.
Lejon, T., Strom, M. B., and Svendsen, J. S. (2001) Antibiotic activity of pentadecapeptides modeled from amino acid descriptors. J. Pept. Sci. 7, 74–81.
Lejon, T., Stiberg, T., Strom, M. B., and Svendsen, J. S. (2004) Prediction of antibiotic activity and synthesis of new pentadecapeptides based on lactoferricins. J. Pept. Sci. 10, 329–335.
Cherkasov, A., Hilpert, K., Jenssen, H., Fjell, C. D., Waldbrook, M., Mullaly, S. C., Volkmer, R., and Hancock, R. E. (2009) 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.
Lata, S., Sharma, B. K., and Raghava, G. P. (2007) Analysis and prediction of antibacterial peptides. BMC Bioinformatics 8, 263–273.
Gonzalez, M. P., Teran, C., Saiz-Urra, L., and Teijeira, M. (2008) Variable selection methods in QSAR: an overview. Curr. Top. Med. Chem. 8, 1606–1627.
Willett, P. (1995) Genetic algorithms in molecular recognition and design. Trends Biotechnol. 13, 516–521.
Montgomery, D. C. (2000) Design and Analysis of Experiments (5th ed.). New York: Wiley.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Taboureau, O. (2010). Methods for Building Quantitative Structure–Activity Relationship (QSAR) Descriptors and Predictive Models for Computer-Aided Design of Antimicrobial Peptides. In: Giuliani, A., Rinaldi, A. (eds) Antimicrobial Peptides. Methods in Molecular Biology, vol 618. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-594-1_6
Download citation
DOI: https://doi.org/10.1007/978-1-60761-594-1_6
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-60761-593-4
Online ISBN: 978-1-60761-594-1
eBook Packages: Springer Protocols