探索药物耐药性的进化轨迹
Linfeng Hu1, Aoxuan Zhang1, Arieh Warshel1
1Department of Chemistry, University of Southern California, Los Angeles, CA 90089.
概括
预测耐药性是开发新疗法的关键. 这项研究表明,像HCV蛋白酶一样,有限的突变空间允许更好地预测耐药性进化和药物设计.
科学领域:
- 病毒学 病毒学
- 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:
- 耐药性是全球主要的健康威胁,需要新的治疗策略.
- 预测耐药突变和设计针对不断变化的标有效的药物仍然是一个重大挑战.
- 以前使用活力值评估耐药性的方法由于对突变倾向和蛋白质稳定性的数据不足,缺乏准确性.
研究的目的:
- 研究药物耐药性和蛋白质能量景观之间的相关性.
- 确定一个适合的模型系统来预测药物耐药性的演变.
- 引导开发针对耐药病毒的强大的治疗候选药物.
主要方法:
- 利用斯坦福大学的HIV耐药性数据库来分析耐药性模式.
- 相对应的耐药性 (量化为[公式:见文本]) 与最大 entropy 能量 ([公式:见文本]) 和突变的数量.
- 检查了肝炎C病毒 (HCV) 蛋白酶的突变格局,作为具有有限突变发生的模型系统.
主要成果:
- 在HIV病毒中,药物耐药性和最大力能量之间观察到正相关性.
- 药物耐药性和活力都与突变数量相关,这表明耐药性和病毒功能之间存在权衡.
- 在HCV蛋白酶中,发现抗性替代物在低[公式:参见文本]值时聚集在一起,表明受限的突变空间.
结论:
- 在HCV蛋白酶的有限突变格局使得能够预测药物耐药性的进化途径.
- 最大 Entropy 能量 ([公式:见文本]) 可以作为在受约束系统中抵抗的进化路径的预测器.
- 这种方法可以指导识别治疗候选药物,这些候选药物对不断发展的耐药性保持有效性.
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