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bioRxiv · 10.1101/2025.11.13.688364

Discovery and optimization of antimicrobial peptides from extreme environments on global scale

Kang, Z.·Zhang, H.·Zhou, Q.·Liu, J.·Zhou, K.·Chen, P.·Liu, B.-F.·Ning, K.
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Abstract

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. HighlightsO_LISEGMA, a structure-aware deep learning framework, mines 3,298 novel antimicrobial peptides (extremocins) from 60,461 extremophile genomes on global scale. C_LIO_LIExtremocins exhibit unique sequence features, and expand known antibiotic space, few of which shows homology to existing AMP databases. C_LIO_LIA beam search-guided optimization strategy enhanced selected extremocins to achieve broad-spectrum activity against clinically relevant pathogens. C_LIO_LIExperimental validation confirmed that candidate extremocins exhibit potent in vitro and in vivo antimicrobial activity, highlighting their therapeutic potential. C_LI

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Kang, Z., Zhang, H., Zhou, Q., Liu, J., Zhou, K., Chen, P., Liu, B.-F., Ning, K.. 2025-11-14. Discovery and optimization of antimicrobial peptides from extreme environments on global scale. https://doi.org/10.1101/2025.11.13.688364

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