通过分子动力学和机器学习预测药物耐药性突变对抑制剂功能的影响
Lauren E Intravaia1, Ala M Shaqra1, Somayeh Pirhadi1
1Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, Massachusetts 01605, United States.
The journal of physical chemistry. B
|October 15, 2025
概括
可以预测HIV-1蛋白酶等酶的药物耐药性突变. 使用基于物理学的特征的机器学习模型准确地预测了抑制剂结合功率的损失,即使对于新型突变.
科学领域:
- 生物化学和结构生物学
- 计算生物学和化学信息学
- 药物发现和开发 药物发现和开发
背景情况:
- 酶是关键的药物标,但突变可能导致耐药性,降低治疗疗效.
- 抵抗突变,特别是远端突变,可以显著降低抑制剂结合亲和力,这对药物设计构成挑战.
- 现有的基于酶抑制剂复合物的结构分析往往无法完全解释抗性机制.
研究的目的:
- 开发一种准确的计算方法,用于预测酶变体中的耐药性.
- 为了确定主要的分子特征,预测由于突变的抑制剂结合功率的损失.
- 创建一个强大的机器学习模型,用于预测针对HIV-1蛋白酶变体的达鲁纳维尔等强效抑制剂的耐药性.
主要方法:
- 用达鲁纳维尔对28种HIV-1蛋白酶变体的共晶结构进行重新细化,以确保准确的抑制物几何形状.
- 使用并行分子动力学模拟来捕捉酶动力学和相互作用.
- 使用特征选择技术与机器学习相结合,构建用于结合亲和力和抗性的预测模型.
主要成果:
- 机器学习模型结合了分子内相互作用的基于物理的特征,准确地预测了结合亲和力.
- 四个特定的特征,位于远离活性部位,足以预测结合亲和力在实验值的1kcal/mol范围内.
- 开发的模型在预测耐药性方面明显优于仅基于酶结构或序列的模型.
结论:
- 已经证明了一种可靠的策略,用于预测由于未见的突变而导致的耐药性.
- 基于物理学的特征,特别是与活性部位距离的分子内相互作用,对于预测抑制剂结合亲和力损失至关重要.
- 这种方法为预测和克服向酶治疗中的耐药性提供了一个强大的工具.
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