机器学习识别了针对多药耐药细菌和皮肤感染的强效抗菌
Gizem Babuççu1, Nikitha Vavilthota1, Colin Bournez2
1Department of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Centre, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.
Antibiotics (Basel, Switzerland)
|November 27, 2025
概括
机器学习发现了针对耐药细菌有效的新型抗微生物 (AMP). 这些指导设计的智能治疗 (GDST) 对皮肤感染和生物膜表现出强烈的活性.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 抗生素耐药性是全球卫生危机,需要新的抗菌剂.
- 抗微生物 (AMP) 是对抗多药耐药 (MDR) 病原体的有希望的替代品.
- 机器学习 (ML) 加快AMP发现,克服传统方法的局限性.
研究的目的:
- 应用ML来识别针对MDR细菌和皮肤感染有效的新型AMP.
- 使用CalcAMP模型开发潜在治疗性的目录.
- 验证新型候选剂对特定细菌菌株和感染模型的疗效.
主要方法:
- 利用基于ML的CalcAMP模型来预测16,384个序列的抗菌活性.
- 创建了一个新的指导设计智能治疗 (GDST) 类目录.
- 在MDR细菌和皮肤感染模型中测试了GDST和它们的逆向 (RI) 变体.
主要成果:
- 具有RI变异的GDST-038和GDST-045显示出对Acinetobacter baumannii和黄金葡萄球菌的强有力的活性.
- 素迅速使细菌膜脱极化,显示出对ESKAPE病原体的广泛杀菌效应,血液溶解最小.
- RI变种有效地减少了A. baumannii生物膜,而所有GDST都显著减少了S. aureus生物膜;有效性在3D皮肤模型中得到证实.
结论:
- 在ML驱动的查中,成功地确定了两种新型候选AMP (GDST).
- 在治疗MDR细菌感染方面,GDST具有显著的治疗潜力.
- 该研究强调了ML在加速发现有效的抗微生物疗法的有效性.
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