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Article Dans Une Revue Nature Communications Année : 2018

In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design

William Porto
  • Fonction : Auteur
Luz Lu
  • Fonction : Auteur
Eliane Alves
  • Fonction : Auteur
Suzana Ribeiro
  • Fonction : Auteur
Állan Pires
  • Fonction : Auteur
Isabel Fensterseifer
  • Fonction : Auteur
Vivian Miranda
  • Fonction : Auteur
Evan Haney
  • Fonction : Auteur
Vincent Humblot
Marcelo Torres
  • Fonction : Auteur
Luciano Lu
  • Fonction : Auteur
Timothy Lu
  • Fonction : Auteur
Cesar de La Fuente-Nunez
  • Fonction : Auteur
Octavio Franco
  • Fonction : Auteur

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.

Domaines

Chimie

Dates et versions

hal-01767730 , version 1 (16-04-2018)

Identifiants

Citer

William Porto, Luz Lu, Eliane Alves, Suzana Ribeiro, Carolina Matos, et al.. In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design. Nature Communications, 2018, 9 (1), ⟨10.1038/s41467-018-03746-3⟩. ⟨hal-01767730⟩
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