Antimicrobial peptides (AMPs) are short sequences (here defined as 10-100 amino acids) able to disrupt microbial growth. AMPs perform important ecological roles across all domains of life and are a potential new class of antibiotics (antibiotics being urgently needed to combat the antibiotic-resistance crisis).

We developed a machine learning-based approach to predict genomically-encoded AMPs from prokaryotic genomes or metagenomes and applied it to a well-curated dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown.

We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident, suggesting that AMPs may be a mechanism for niche competition. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides.

To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.