Deep learning reveals antibiotics in the archaeal proteome

Abstract Antimicrobial resistance is one of the greatest threats facing humanity, making the need for new antibiotics more critical than ever. While most antibiotics originate from bacteria and fungi, archaea offer a largely untapped reservoir for antibiotic discovery. In this study, we leveraged deep learning to systematically explore the archaeome, uncovering promising candidates for combating antimicrobial resistance. By mining 233 archaeal proteomes, we identified 12,623 molecules with potential antimicrobial activity. These peptide compounds, termed archaeasins, have unique compositional features that differentiate them from traditional antimicrobial peptides, including a distinct amino acid profile. We synthesized 80 archaeasins, 93% of which showed antimicrobial activity in vitro against Acinetobacter baumannii , Escherichia coli , Klebsiella pneumoniae , Pseudomonas aeruginosa , Staphylococcus aureus and Enterococcus spp. Notably, in vivo validation identified archaeasin-73 as a lead candidate, significantly reducing A. baumannii loads in mouse infection models, with effectiveness comparable to that of established antibiotics such as polymyxin B. Our findings highlight the potential of archaea as a resource for developing next-generation antibiotics.