Discovery of Antimicrobial Lysins from the Dark Matter of Uncharacterized Phages Using Artificial Intelligence
文献类型： 外文期刊
作者： Zhang, Yue;Li, Runze;Zou, Geng;Guo, Yating;Wu, Renwei;Zhou, Yang;Chen, Huanchun;Zhou, Rui;Lavigne, Rob;Bergen, Phillip J.;Li, Jian;Li, Jinquan
作者机构：
关键词： （0-1-6）antibacterial protein;antibiotic resistance;high-throughput screening;infectious diseases;phage lysin;prophage;stacking model
期刊名称： ADVANCED SCIENCE
ISSN：
年卷期： 2024 年
页码：
收录情况： SCIE（2024版） ; ; EI（2024版）
摘要： The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs (dark matter) for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance. DeepLysin is the first unified software that employs artificial intelligence to mine novel antibacterial proteins from the dark matter. The best-in-class antibacterial protein LLysSA9 is identified, which can effectively kill drug-resistant bacteria in vitro and in vivo, being a potent candidate with clinical application prospects. This software expedites the discovery of new antibacterial proteins for combating increasing antimicrobial resistance. image
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