AI Insight
Researchers developed a computational framework to mine bacterial proteomes for encrypted antimicrobial peptides (eAMPs), bioactive fragments hidden within larger proteins. From 265 bacterial genomes, they identified over 29 million peptide candidates, narrowed these to approximately 3.2 million using machine learning predictors, and prioritized 18 for detailed analysis. Three experimentally tested peptides showed concentration-dependent antimicrobial activity against E. coli and S. aureus, with the most potent candidate achieving over 2-log reduction in bacterial counts at 128 micromolar concentration.
Why it matters
This work provides a systematic approach to discover novel antimicrobial peptides from existing protein databases, potentially accelerating development of new antibiotics at a time when antimicrobial resistance is a growing global health threat. The validated computational workflow could be applied to other proteomes to expand the pipeline of antimicrobial candidates.
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⚠️ Preprint – Noch nicht peer-reviewed
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Encrypted antimicrobial peptides (eAMPs) are bioactive fragments embedded within larger proteins and represent an underexplored source of antimicrobial candidates. We developed a multi-layer proteome-mining framework to identify and prioritise eAMPs from 95%-identity-reduced protein sets derived from 265 high-quality bacterial genomes. Three complementary, layer-specific extraction strategies targeting protein termini, internal cleavage sites, and cationic hotspots yielded 29,251,180 unique peptide candidates. Dual AMP prediction with AMP-scanner v2 and Macrel reduced this space to 3,249,772 consensus candidates. Downstream prioritisation followed two complementary routes: a low-haemolysis branch focused on selectivity-oriented candidates and a high-activity branch that retained predicted haemolytic sequences as mechanistic comparators. Structure prediction and review were performed for 185 candidates, and 18 entered Tier-1 developability, novelty, and membrane-activity assessment. Three sequence-matched representatives were selected for experimental evaluation. Molecular-dynamics simulations supported water-phase stability of GEAMP_71c139393ac596b5 and deep anionic-membrane insertion by GEAMP_12ffb5d589c8cb1b. In replicated colony-count assays against Escherichia coli and Staphylococcus aureus, all three peptides showed concentration-dependent activity over 8-128 uM. GEAMP_12ffb5d589c8cb1b was the most active, producing 1.52- and 2.27-log10 reductions, respectively, at 128 uM relative to the matched 8 uM condition. Together, these results establish a sequence-traceable workflow linking proteome-scale eAMP discovery with structural prioritisation and experimental activity assessment.