Abstract：
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.
摘要：
抗生素耐药性的迅速上升和新抗生素的缓慢发现已经威胁到全球健康。虽然新的噬菌体溶素已经成为潜在的抗菌剂，由于工作量巨大，新型溶素的实验筛选方法提出了重大挑战。这里,第一个统一软件包，即DeepLysin,开发的目的是利用人工智能来挖掘巨大的基因组库(“暗物质”)以寻找新型抗菌噬菌体溶素。从未表征的金黄色葡萄球菌噬菌体中计算筛选推定的溶素，并随机选择17种新型溶素进行实验验证。七个候选物表现出优异的体外抗菌活性，LLysSA9超过了同类最佳的替代品。LLysSA9的功效在小鼠血流和伤口感染模型中得到进一步证明。因此,这项研究证明了整合计算和实验方法的潜力，以加快发现新的抗菌蛋白，以对抗日益增长的抗菌素耐药性。
