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Nat Commun. 2018 Apr 16;9(1):1490. doi: 10.1038/s41467-018-03746-3.

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

Author information

1
Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia Universidade Católica de Brasília, Brasília, 70790-160, DF, Brazil.
2
S-Inova Biotech, Pós-graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, 79117-010, MS, Brazil.
3
Porto Reports, Brasília, 72236-011, DF, Brazil.
4
Molecular Pathology Post-graduate Program, University of Brasília, Brasília, 70.910-900, DF, Brazil.
5
Laboratório de RMN, Instituto de Química, Universidade Federal de Goiás, Goiânia, 74690-900, GO, Brazil.
6
Centre for Microbial Diseases and Immunity Research, University of British Columbia, 2259 Lower Mall Research Station, Vancouver, BC, V6T 1Z4, Canada.
7
Sorbonne Université, CNRS, Laboratoire de Réactivité de Surface (LRS), UPMC Univ Paris 06, UMR 7197, Paris, F-75005, France.
8
Synthetic Biology Group, MIT Synthetic Biology Center; The Center for Microbiome Informatics and Therapeutics; Research Laboratory of Electronics, Department of Biological Engineering, and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA.
9
Broad Institute of MIT and Harvard, Cambridge, 02139, MA, USA.
10
Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, São Paulo, 09210-580, Brazil.
11
Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Biogenèse des Signaux Peptidiques (BIOSIPE), UPMC Univ Paris 06, Paris, F-75005, France.
12
Synthetic Biology Group, MIT Synthetic Biology Center; The Center for Microbiome Informatics and Therapeutics; Research Laboratory of Electronics, Department of Biological Engineering, and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA. cfuente@mit.edu.
13
Broad Institute of MIT and Harvard, Cambridge, 02139, MA, USA. cfuente@mit.edu.
14
Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia Universidade Católica de Brasília, Brasília, 70790-160, DF, Brazil. ocfranco@gmail.com.
15
S-Inova Biotech, Pós-graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, 79117-010, MS, Brazil. ocfranco@gmail.com.
16
Molecular Pathology Post-graduate Program, University of Brasília, Brasília, 70.910-900, DF, Brazil. ocfranco@gmail.com.

Abstract

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.

PMID:
29662055
PMCID:
PMC5902452
DOI:
10.1038/s41467-018-03746-3
[Indexed for MEDLINE]
Free PMC Article

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