Discovery of antibiotics in the archaeome using deep learning

Abstract

Antimicrobial resistance (AMR) is one of the greatest threats facing humanity, making the need for new antibiotics more critical than ever. While most antibiotics have traditionally been derived from bacteria and fungi, archaea--a distinct and underexplored domain of life--offer a largely untapped reservoir for antibiotic discovery. In this study, we leveraged deep learning to systematically explore the archaeome, uncovering promising new candidates for combating AMR. By mining 233 archaeal proteomes, we identified 12,623 molecules with potential antimicrobial activity. These newly discovered peptide compounds, termed archaeasins, exhibit unique compositional features that differentiate them from traditional antimicrobial peptides, including a distinct amino acid profile. We synthesized 80 archaeasins, 93% of which demonstrated antimicrobial activity in vitro. Notably, in vivo validation identified archaeasin-73 as a lead candidate, significantly reducing bacterial loads in mouse infection models, with effectiveness comparable to established antibiotics like polymyxin B. Our findings highlight the immense potential of archaea as a resource for developing next-generation antibiotics.