复旦大学Luis Pedro Coelho和美国美国宾夕法尼亚大学Cesar de la Fuente-Nunez共同合作，近期取得重要工作进展。他们研究提出，科学家用机器学习法发现全球微生物组中的抗菌肽。相关研究成果2024年6月5日发表于《细胞》杂志上。
据介绍，迫切需要新型抗生素来应对抗生素耐药性危机。
研究人员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63410个宏基因组和87920个原核基因组的庞大数据集来创建AMPSphere，这是一个包括863498个非冗余肽的综合目录，其中很少有与现有数据库匹配的肽。AMPSphere提供了对肽演化起源的深入了解，包括通过复制或基因截短更长的序列，研究人员观察到AMP的产生因栖息地而异。
为了验证这一预测，研究人员在体外和体内合成并测试了100种抗临床相关耐药病原体和人类肠道共生菌的AMP。共有79种肽具有活性，其中63种靶向病原体。这些活性AMP通过破坏细菌膜而表现出抗菌活性。
总之，这一研究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。
附：英文原文
Title: Discovery of antimicrobial peptides in the global microbiome with machine learning
Author: Célio Dias Santos-Júnior, Marcelo D.T. Torres, Yiqian Duan, álvaro Rodríguez del Río, Thomas S.B. Schmidt, Hui Chong, Anthony Fullam, Michael Kuhn, Chengkai Zhu, Amy Houseman, Jelena Somborski, Anna Vines, Xing-Ming Zhao, Peer Bork, Jaime Huerta-Cepas, Cesar de la Fuente-Nunez, Luis Pedro Coelho
Issue&Volume: 2024-06-05
Abstract: 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.
DOI: 10.1016/j.cell.2024.05.013
Source: https://www.cell.com/cell/fulltext/S0092-8674(24)00522-1