坎珀:用于设计针对MRSA持续存在的类的机械化人工智能
Fadi Shehadeh1,2, Biswajit Mishra1,3, Raquel Ferrer-Espada4
1Department of Medicine, Houston Methodist Hospital, Houston, TX, USA.
Nature communications
|March 10, 2026
概括
我们开发了CAMPER,一种AI框架,用于创建针对抗生素耐药细菌的强效抗微生物. 这导致了WP-CAMPER1,在临床前模型中有效减少金黄色葡萄球菌感染.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 抗菌素耐药性 (AMR) 需要新的治疗策略.
- 短,稳定和强大的抗微生物 (AMP) 显示出对抗耐药细菌的承诺.
- 针对耐甲素耐药金黄色葡萄球菌 (MRSA) 等细菌的持久性和生物膜形式至关重要.
研究的目的:
- 开发一种人工智能 (AI) 框架,用于设计有效的抗微生物.
- 识别具有针对MRSA持久性和生物膜细胞活性的新型膜向.
- 在临床前感染模型中评估已识别的的疗效.
主要方法:
- 开发了CAMPER (限制驱动的AMP工程与排名),这是一个整合机器学习和生物物理排名的AI框架.
- 应用CAMPER识别和设计新型抗微生物.
- 测试了针对黄金葡萄球菌 (S. aureus) 菌株的效,包括MRSA.
- 利用小鼠模型进行预防性皮肤,建立生物膜和深部大腿感染.
- 通过高通量微流体使用单细胞分析来研究对细菌持久性的影响.
主要成果:
- 确定了WP-CAMPER1 (12mer),其最小抑制度 (MIC) 为4μg/mL,用于对抗S. aureus MW2.
- 在使用2%局部WP-CAMPER1配方的小鼠皮肤感染模型中,在S. aureus MW2负担 (2.5 log10) 中显著降低.
- 显示WP-CAMPER1-d在已建立的生物膜模型中减少了S. aureus MW2负担1.37 log10.
- 证实WP-CAMPER1-d降低了黄金色杆菌在体外和体外的指数和静止阶段持续存在 (1.6 log10减少大腿感染模型).
结论:
- CAMPER是一个有效的AI框架,用于设计强效的抗微生物.
- WP-CAMPER1及其D-enantiomer显示出作为治疗MRSA的显著潜力,包括持久性和生物膜形式.
- 这些发现为打击抗生素耐药性和持久性提供了一个有希望的策略.
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