Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

迫切需要新型抗生素来应对抗生素耐药性危机。我们提出了一种基于机器学习的方法来预测全球微生物组中的抗菌肽 (AMP)，并利用来自环境和宿主相关栖息地的 63,410 个宏基因组和 87,920 个原核基因组的庞大数据集来创建 AMPSphere，这是一个包含 863,498 个非-冗余肽，其中很少与现有数据库匹配。 AMPSphere 提供了对肽的进化起源的见解，包括通过较长序列的复制或基因截断，我们观察到 AMP 的产生因栖息地而异。为了验证我们的预测，我们在体外和体内合成并测试了 100 种 AMP，以对抗临床相关的耐药病原体和人类肠道共生体。共有 79 种肽具有活性，其中 63 种针对病原体。这些活性 AMP 通过破坏细菌膜而表现出抗菌活性。总之，我们的方法鉴定了近一百万个原核 AMP 序列，这是抗生素发现的开放获取资源。