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

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.

EcDBS1R6: A new broad-spectrum cationic antibacterial peptide derived from a signal peptide sequence

“…This composition, despite the novel arrangement, makes this peptide analogous to others (16). It is well known that the amino acid composition can influence the mechanism of action (7) and perhaps this is the reason why EcDBS1R6 behaved similarly to the positive control melittin, in the experiments probing the mechanism of action (Figures 2 and 3), causing membrane damage (Figure 4).…”

“…for a 20 mer peptide, there are 20 20 possibilities). Therefore a number of computational methods have been applied to design novel AMP sequences (7–10), as the initial step in the development of new antimicrobial compounds, followed by in-depth characterization of the lead molecules.…”

Structure‐guided identification of antimicrobial peptides in the spathe transcriptome of the non‐model plant, arum lily (Zantedeschia aethiopica)

“…Although the data presented here characterize an advance in the mining of transcriptomic data, in vitro tests with the identified peptide are still necessary. However, ZaLTP7 could be used as a structural scaffold for novel synthetic peptides (Porto et al., ). Moreover, threading can be applied in larger datasets, exploring the large amount of data in public databases as well as NGS data, turning the data into new knowledge to combat multidrug‐resistant bacteria.…”

AurH1: a new heptapeptide derived from Aurein1.2 antimicrobial peptide with specific and exclusive fungicidal activity

“…To surmount the limitations of natural peptides, introducing an in silico structure in AMP design would be a promising strategy which can reduce the production costs and the time required for the evaluation of activity, stability, and toxicity . Computer‐based design methods make it possible to detect the AMP sequence via machine learning algorithms . The utilization of machine learning algorithms is useful for the in silico design of AMPs.…”

“…13 Computer-based design methods make it possible to detect the AMP sequence via machine learning algorithms. 14 The utilization of machine learning algorithms is useful for the in silico design of AMPs. Machine learning algorithms, combined with random optimization methods, are adopted to correlate antimicrobial activity to certain structural characteristic so as to screen large virtual libraries.…”