Open AccessMultiple Classes of Antimicrobial Peptides in Amaranthus tricolor Revealed by Prediction, Proteomics, and Mass Spectrometric Characterization
Author(s) -
Tessa B. Moyer,
Jessie L Allen,
Lindsey N. Shaw,
Leslie M. Hicks
Publication year - 2021
Publication title -
journal of natural products
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.976
H-Index - 139
eISSN - 1520-6025
pISSN - 0163-3864
DOI - 10.1021/acs.jnatprod.0c01203
Subject(s) - proteomics , antimicrobial peptides , in silico , antimicrobial , biology , acinetobacter baumannii , enterococcus faecium , computational biology , enterococcus faecalis , peptide , microbiology and biotechnology , biochemistry , bacteria , staphylococcus aureus , antibiotics , pseudomonas aeruginosa , genetics , gene
Traditional medicinal plants are rich reservoirs of antimicrobial agents, including antimicrobial peptides (AMPs). Advances in genomic sequencing, in silico AMP predictions, and mass spectrometry-based peptidomics facilitate increasingly high-throughput bioactive peptide discovery. Herein, Amaranthus tricolor aerial tissue was profiled via MS-based proteomics/peptidomics, identifying AMPs predicted in silico. Bottom-up proteomics identified seven novel peptides spanning three AMP classes including lipid transfer proteins, snakins, and a defensin. Characterization via top-down peptidomic analysis of Atr-SN1, Atr-DEF1, and Atr-LTP1 revealed unexpected proteolytic processing and enumerated disulfide bonds. Bioactivity screening of isolated Atr-LTP1 showed activity against the high-risk ESKAPE bacterial pathogens ( Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumannii , and Enterobacter cloacae ). These results highlight the potential for integrating AMP prediction algorithms with complementary -omics approaches to accelerate characterization of biologically relevant AMP peptidoforms.