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通过使用人工智能,从未表征的菌体的"暗物质"中发现了抗菌素lysins

Yue Zhang^1,2, Runze Li^1,2, Geng Zou^1,2

¹National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|June 20, 2024

概括

研究人员开发了DeepLysin,这是一种人工智能工具,用于发现称为菌素素的新抗菌蛋白. 这种方法成功地发现了具有强大抗细菌活性的新型 lysins,为抗生素耐药性提供了有前途的解决方案.

科学领域:

微生物学微生物学
生物信息学是一种生物信息学.
药物发现药物发现药物发现

背景情况:

抗生素耐药性是一个日益增长的全球健康威胁,新抗菌剂的缓慢发现加剧了这种威胁.
菌素是有希望的抗菌候选人,但实验性查是劳动密集型和低效的.
庞大的基因组数据 (
暗物质是什么?暗物质是什么?
) 在新型抗菌蛋白方面仍未得到充分研究.

研究的目的:

开发一个人工智能驱动的平台,DeepLysin,用于高效地挖掘新型抗菌菌素的基因组储库.
通过计算来选未经表征的金黄色葡萄球菌的假定 lysins.
通过实验验证计算识别的lysins,并评估它们的治疗潜力.

主要方法:

开发DeepLysin,一个统一的软件包,利用人工智能识别抗菌菌素.
从Staphylococcus aureus菌体基因组中对素的计算查.
实验验证17个选定的新型lysin候选物的实验验证.
在小鼠感染模型中进行体外抗菌测定和体内疗效研究.

主要成果:

DeepLysin成功地从基因组暗物质中识别出新的抗菌菌体 lysins.
在17个经过实验验证的候选药物中,有7种表现出显著的体外抗菌活性.
一种已识别的lysin,LLysSA9,与现有的同类最佳替代品相比,表现出更高的活性.
在血液和伤口感染的小鼠模型中,LLysSA9被证明是有效的.

结论:

由人工智能驱动的计算和实验方法的整合可以显著加速新型抗菌蛋白的发现.
DeepLysin提供了一种强大的策略,通过有效地识别有前途的菌素 lysin 候选人来打击抗菌素耐药性.
这项研究验证了LLysSA9作为治疗细菌感染的治疗剂的潜力.

关键词:

抗菌蛋白是一种抗菌蛋白.抗生素耐药性抗生素耐药性高通量选的高通量选传染病是一种传染性疾病.菌体氨酸的 lysin 是一个预言预言预言预言堆叠模型的堆叠模型

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