title: Novel method to identify the optimal antimicrobial peptide in a combination matrix, using anoplin as an example.
abstract: Microbial resistance is an increasing health concern and a true danger to human well-being. A worldwide search for new compounds is ongoing, and antimicrobial peptides are promising lead candidates for tomorrow's antibiotics. The decapeptide anoplin (GLLKRIKTLL-NH2) is an especially interesting candidate because of its small size as well as its antimicrobial and nonhemolytic properties. Optimization of the properties of an antimicrobial peptide such as anoplin requires multidimensional searching in a complex chemical space. Typically, such optimization is performed by labor-intensive and costly trial-and-error methods. In this study, we show the benefit of fractional factorial design for identification of the optimal antimicrobial peptide in a combination matrix. We synthesized and analyzed a training set of 12 anoplin analogs, representative of 64 analogs in total. Using MIC, hemolysis, and high-performance liquid chromatography retention time data, we constructed analysis-of-variance models that describe the relationship between these properties and the structural characteristics of the analogs. We show that the mathematical models derived from the training set data can be used to predict the properties of other analogs in the chemical space. Hence, this method provides an efficient means of identification of the optimal peptide in the searched chemical space.
authors.affiliation: Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
authors.forename: J K
authors.initials: JK
authors.lastname: Munk
authors.forename: C
authors.initials: C
authors.lastname: Ritz
authors.forename: F P
authors.initials: FP
authors.lastname: Fliedner
authors.forename: N
authors.initials: N
authors.lastname: Frimodt-Møller
authors.forename: P R
authors.initials: PR
authors.lastname: Hansen
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000900:Anti-Bacterial Agents / Q000138:chemical synthesis* / Q000494:pharmacology; D023181:Antimicrobial Cationic Peptides / Q000138:chemical synthesis* / Q000494:pharmacology; D002176:Candida albicans / Q000187:drug effects / Q000254:growth & development; D002851:Chromatography, High Pressure Liquid; D016984:Enterococcus faecium / Q000187:drug effects / Q000254:growth & development; D004912:Erythrocytes / Q000187:drug effects; D004926:Escherichia coli / Q000187:drug effects / Q000254:growth & development; D005163:Factor Analysis, Statistical; D006461:Hemolysis / Q000187:drug effects; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D008826:Microbial Sensitivity Tests; D008969:Molecular Sequence Data; D011550:Pseudomonas aeruginosa / Q000187:drug effects / Q000254:growth & development; D013211:Staphylococcus aureus / Q000187:drug effects / Q000254:growth & development; D014862:Wasp Venoms / Q000138:chemical synthesis* / Q000494:pharmacology
chemical_list: D000900:Anti-Bacterial Agents; D023181:Antimicrobial Cationic Peptides; D014862:Wasp Venoms; C443041:anoplin