Details

Autor(en) / Beteiligte

• Porto, William F.
• Irazazabal, Luz
• Alves, Eliane S. F.
• Ribeiro, Suzana M.
• Matos, Carolina O.
• Pires, Állan S.
• Fensterseifer, Isabel C. M.
• Miranda, Vivian J.
• Haney, Evan F.
• Humblot, Vincent
• Torres, Marcelo D. T.
• Hancock, Robert E. W.
• Liao, Luciano M.
• Ladram, Ali
• Lu, Timothy K.
• de la Fuente-Nunez, Cesar
• Franco, Octavio L.

Titel

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

Ist Teil von

• Nature communications, 2018-04, Vol.9 (1), p.1490-12, Article 1490

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2018

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. Antimicrobial peptides are considered promising alternatives to antibiotics. Here the authors developed a computational algorithm that starts with peptides naturally occurring in plants and optimizes this starting material to yield new variants which are highly distinct from the parent peptide.