title: Methods for building quantitative structure-activity relationship (QSAR) descriptors and predictive models for computer-aided design of antimicrobial peptides.
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.
authors.affiliation: Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
authors.forename: Olivier
authors.initials: O
authors.lastname: Taboureau
mesh_terms: D000890:Anti-Infective Agents / Q000737:chemistry* / Q000494:pharmacology; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry* / Q000494:pharmacology; D001185:Artificial Intelligence; D017076:Computer-Aided Design; D015195:Drug Design; D008954:Models, Biological; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000890:Anti-Infective Agents; D023181:Antimicrobial Cationic Peptides