Antimicrobial peptides (AMPs) can effectively kill a broad range of life threatening multidrug-resistant bacteria, a serious threat to public health worldwide. However, despite great hopes novel drugs based on AMPs are still rare. To accelerate drug development we studied different approaches to improve the antibacterial activity of short antimicrobial peptides. Short antimicrobial peptides seem to be ideal drug candidates since they can be synthesized quickly and easily, modified and optimized. In addition, manufacturing a short peptide drug will be more cost efficient than long and structured ones. In contrast to longer and structured peptides short AMPs seem hard to design and predict. Here, we designed, synthesized and screened five different peptide libraries, each consisting of 600 9-mer peptides, against Pseudomonas aeruginosa. Each library is presenting a different approach to investigate effectiveness of an optimization strategy. The data for the 3000 peptides were analyzed using models based on fuzzy logic bioinformatics and plausible descriptors. The rate of active or superior active peptides was improved from 31.0% in a semi-random library from a previous study to 97.8% in the best new designed library. This article is part of a Special Issue entitled: Antimicrobial peptides edited by Karl Lohner and Kai Hilpert.
Keywords: Antimicrobial peptides; New antimicrobials; Peptide libraries; Prediction of antimicrobials; Pseudomonas aeruginosa; Short peptides.
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