Exploring bioactive peptides from bacterial secretomes using PepSAVI‐MS: identification and characterization of Bac‐21 from Enterococcus faecalis pPD1

Abstract

SummaryAs current methods for antibiotic drug discovery are being outpaced by the rise of antimicrobial resistance, new methods and innovative technologies are necessary to replenish our dwindling arsenal of antimicrobial agents. To this end, we developed the PepSAVI‐MS pipeline to expedite the search for natural product bioactive peptides. Herein we demonstrate expansion of PepSAVI‐MS for the discovery of bacterial‐sourced bioactive peptides through identification of the bacteriocin Bac‐21 from Enterococcus faecalis pPD1. Minor pipeline modifications including implementation of bacteria‐infused agar diffusion assays and optional digestion of peptide libraries highlight the versatility and wide adaptability of the PepSAVI‐MS pipeline. Additionally, we have experimentally validated the primary protein sequence of the active, mature Bac‐21 peptide for the first time and have confirmed its identity with respect to primary sequence and post‐translational processing. Successful application of PepSAVI‐MS to bacterial secretomes as demonstrated herein establishes proof‐of‐principle for use in novel microbial bioactive peptide discovery.

Recent advances of bioactive proteins/polypeptides in the treatment of breast cancer

“…Besides, a PepSAVI-MS (statistically-guided bioactive peptides prioritized via mass spectrometry) pipeline is developed for bioactive peptide discovery (Kirkpatrick et al, 2017 ). Using this pipeline, some bioactive peptides are successfully identified from biological species such as Enterococcus faecalis (Kirkpatrick et al, 2018b ), Ustilago maydis (Kirkpatrick et al, 2018a ), Viola odorata (Parsley et al, 2018 ) and Amaranthus tricolor (Moyer et al, 2019 ).…”

PepSAVI-MS Reveals a Proline-rich Antimicrobial Peptide in Amaranthus tricolor

“…Profiling of A. tricolor peptide library fractions 19-46 via LC-MS/MS revealed 5,868 unique features with masses, charge states, and retention times (1,(0)(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)000 Da,(2)(3)(4)(5)(6)(7)(8)(9)respectively) in the range of typical AMPs, highlighting the complex nature of the peptide library. The resulting list was filtered so that the maximum abundance of each feature was above 100 and detected in fractions 29-37, with <5% maximum abundance outside of the defined bioactive region (fractions [29][30][31][32][33][34][35][36][37]. This filtered list was modeled against the bioactivity profile using an elastic net penalized linear regression to identify the top 20 peptidyl features most likely contributing to bioactivity (Figure 2B).…”

“…Background ions were eliminated by selecting for retention time (15-45 min), mass (1,000-15,000 Da), and charge-state (+2-9, inclusive). The resulting list was filtered so that the maximum abundance of each feature was above 100 and detected in fractions 28-38, with <5% maximum abundance outside of the defined bioactive region (29)(30)(31)(32)(33)(34)(35)(36)(37). 260 features meeting these filter criteria were modeled using the elastic net estimator with a quadratic penalty parameter specification of 0.01.…”

“…Herein, the PepSAVI-MS pipeline − was implemented for the identification of a new antimicrobial peptide from A. tricolor (Figure ). PepSAVI-MS employs crude fractionation of extracts to generate peptide libraries which are screened for biological activity and profiled with mass spectrometry.…”

Implementation of Microfluidics for Antimicrobial Susceptibility Assays: Issues and Optimization Requirements

“…To address this gap, PepSAVI-MS was developed to identify low abundance bioactive peptides in complex natural product extracts (Kirkpatrick et al, 2017 , 2018a , b ; Moyer et al, 2019 ; Parsley et al, 2019 ). PepSAVI-MS is a top-down peptidomics approach that leverages modern mass spectrometry and relies on traditional bioassays for bioactivity characterization (e.g., 96-well plate or disk-diffusion).…”