AI-driven discovery and optimization of antimicrobial peptides from extreme environments on global scale
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The escalating crisis of global antimicrobial resistance (AMR) necessitates the discovery of novel antibiotics. Antimicrobial peptides (AMPs), particularly those from under-explored extreme environments, represent a promising therapeutic class. Here, we introduce SEGMA (Structure-aware Extremophile Genome Mining for Antimicrobial peptides), a computational framework that integrates structure information to systematically mine AMPs from extremophile genomes on a global scale. By analyzing 60,461 extremophile metagenome-assembled genomes (MAGs) from diverse habitats, SEGMA identified 3,298 novel AMPs (termed “extremocins”), which exhibit unique amino acid profiles and physicochemical properties. Leveraging a beam search-guided optimization strategy, we further enhanced selected extremocins to achieve broad-spectrum antimicrobial activity. Experimental validation confirmed potent in vitro efficacy against clinically relevant pathogens. This study underscores the value of structure-aware mining and extremophile microbiomes in expanding the antibiotic arsenal against AMR.
Highlights
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SEGMA, a structure-aware deep learning framework, mines 3,298 novel antimicrobial peptides (extremocins) from 60,461 extremophile genomes on global scale.
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Extremocins exhibit unique sequence features, and expand known antibiotic space, few of which shows homology to existing AMP databases.
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A beam search-guided optimization strategy enhanced selected extremocins to achieve broad-spectrum activity against clinically relevant pathogens.
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Experimental validation confirmed that candidate extremocins exhibit potent in vitro and in vivo antimicrobial activity, highlighting their therapeutic potential.
