In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Résumé
Plants are extensively used in traditional medicine, and several plant antimicrobial peptides
have been described as potential alternatives to conventional antibiotics. However,
after more than four decades of research no plant antimicrobial peptide is currently used for
treating bacterial infections, due to their length, post-translational modifications or high
dose requirement for a therapeutic effect . Here we report the design of antimicrobial peptides
derived from a guava glycine-rich peptide using a genetic algorithm. This approach
yields guavanin peptides, arginine-rich α-helical peptides that possess an unusual hydrophobic
counterpart mainly composed of tyrosine residues. Guavanin 2 is characterized as a
prototype peptide in terms of structure and activity. Nuclear magnetic resonance analysis
indicates that the peptide adopts an α-helical structure in hydrophobic environments. Guavanin
2 is bactericidal at low concentrations, causing membrane disruption and triggering
hyperpolarization. This computational approach for the exploration of natural products could
be used to design effective peptide antibiotics.