用Venomics人工智能对全球毒素进行计算探索,以发现抗菌药物
Changge Guan1,2,3,4, Marcelo D T Torres1,2,3,4, Sufen Li1,2,3,4
1Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nature communications
|July 11, 2025
概括
毒素为发现新的抗微生物来对抗耐药细菌提供了一个新的来源. 对毒素蛋白的计算分析确定了有希望的候选人,这些候选人有效地准并破坏细菌膜.
科学领域:
- 生物勘探是指生物勘探.
- 抗微生物发现的发现
- 药物开发 药物开发
背景情况:
- 抗生素耐药性是一个日益严重的全球健康危机,每年有数百万人死亡.
- 传统的抗生素发现方法已经放缓,需要新的治疗方法.
- 动物毒素是生物活性化合物的未经探索的来源,具有抗菌潜力.
研究的目的:
- 从毒药数据集中识别和描述新型抗微生物.
- 利用深度学习和毒理学来加速药物发现.
- 验证新的抗微生物候选药物的疗效和作用机制.
主要方法:
- 使用深度学习挖掘大规模的毒药数据集 (16,123种蛋白质).
- 产生和选数百万个毒液加密的.
- 结构分析,实验验证和机械分析.
- 在Acinetobacter baumannii感染的小鼠模型中的体内测试.
主要成果:
- 鉴定了386种与已知的抗微生物候选物不同的新型抗微生物候选物.
- 在58种测试的中,有53种表现出强烈的抗菌活性.
- 酸通过脱极化破坏细菌膜,类似于抗微生物酸 (AMP).
- 类在体内显著降低了细菌负担,没有观察到毒性.
结论:
- 毒素是发现新型抗微生物支架的宝贵储备.
- 将计算挖掘与实验验证相结合,可以加速抗生素的发现.
- 这种方法提供了一个有前途的策略,以解决对抗耐药病原体的新抗生素的迫切需求.
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