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Deep learning reveals antibiotics in the archaeal proteome
Marcelo D T Torres1,2,3,4, Fangping Wan1,2,3,4, Cesar de la Fuente-Nunez5,6,7,8
1Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nature Microbiology
|August 12, 2025
Summary
Scientists discovered novel antibiotics called archaeasins from archaea, a previously untapped source. These compounds show high antimicrobial activity against resistant bacteria, with one candidate proving effective in animal models.
Area of Science:
- Microbiology
- Biotechnology
- Computational Biology
Background:
- Antimicrobial resistance (AMR) is a significant global health threat, necessitating the discovery of new antibiotics.
- Archaea represent an underexplored domain of life with potential for novel antimicrobial compound identification.
Purpose of the Study:
- To explore the archaeome for novel antibiotic candidates using deep learning.
- To identify and characterize antimicrobial peptides from archaea.
Main Methods:
- Deep learning models were employed to mine 233 archaeal proteomes.
- 12,623 potential antimicrobial molecules (archaeasins) were identified.
- 80 archaeasins were synthesized and tested in vitro and in vivo.
Main Results:
- 93% of synthesized archaeasins exhibited in vitro activity against key Gram-negative and Gram-positive pathogens.
- Archaeasin-73 demonstrated significant in vivo efficacy in reducing bacterial load in a mouse model.
- Archaeasins possess unique compositional features distinct from known antimicrobial peptides.
Conclusions:
- Archaea are a promising source for the discovery of next-generation antibiotics.
- Deep learning is an effective tool for exploring microbial dark matter for novel therapeutics.
- Archaeasins represent a new class of antimicrobial compounds with potential for clinical development.
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