深度突变扫描和机器学习用于分析驱动膜选择性的抗微生物特征
Justin R Randall1, Luiz C Vieira2, Claus O Wilke2
1Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
Nature biomedical engineering
|July 31, 2024
概括
研究人员开发了一种高通量方法来设计更安全的抗微生物. 这种方法确定了Protegrin-1变体的毒性降低,并保持了抗菌活性,为新的基于的药物铺平了道路.
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 药物发现 药物发现 药物发现
背景情况:
- 抗微生物 (AMP) 具有治疗潜力,但通常会损害宿主细胞膜.
- 了解AMP膜选择性对于安全的治疗应用至关重要.
- 的庞大的序列空间阻碍了选择性AMP的设计.
研究的目的:
- 开发一种高通量方法来评估抗微生物 (AMP) 膜选择性.
- 确定控制AMP选择性的序列结构功能关系.
- 为了设计更安全,更有效的AMP变体.
主要方法:
- 开发了一种新型检测方法,可以同时测量数千种变异的抗微生物活性损失或维持.
- 用Protegrin-1,一个强大的AMP,来探索残留物的重要性和序列灵活性.
- 在实验数据上训练机器学习模型,以预测膜特异性活动.
主要成果:
- 细菌选择性变体保持了二次结构,并避免了芳香残留物和氨酸对.
- 一个机器学习模型准确地预测了超过570万个Protegrin-1变体的活性.
- 一种工程变异在小鼠感染模型中显示了降低的毒性和保留的活性.
结论:
- 高通量方法方便阐明AMP序列结构功能关系.
- 这种方法可以指导基于的新型合成药物的合理设计,从而提高选择性.
- 工程化AMP显示出开发更安全的抗菌疗法的前景.
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