从绝对约束的自由能量计算中预测连接物选择性
Matteo Aldeghi1, Alexander Heifetz2, Michael J Bodkin2
1Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford , South Parks Road, Oxford OX1 3QU, U.K.
Journal of the American Chemical Society
|December 24, 2016
概括
预测药物结合选择性对于安全的药物开发至关重要. 使用分子动力学的自由能量计算显示出准确预测多个蛋白质标的抑制剂结合亲和力.
科学领域:
- 计算化学
- 药物发现
- 结构生物学
背景情况:
- 药物选择性对于安全性和有效性至关重要.
- 通过计算预测绑定选择性仍然是一个重大挑战.
- 目前的方法在药物设计中缺乏普遍适用性.
研究的目的:
- 评估基于分子动态的自由能量计算的性能,以预测结合选择性.
- 评估不同域家族的计算准确性.
- 将计算预测与实验等热度定位热量计数据进行比较.
主要方法:
- 使用化学途径进行绝对结合的自由能量计算.
- 分子动力学模拟以估计配体-蛋白的亲和力.
- 在多个标中分析代体抑制剂的结合特征.
主要成果:
- 在第一个案例研究中,与七种类素的实验数据有很好的一致性 (0.81 kcal/ mol误差,0.75相关性).
- 精确估计首选的配体结合方向.
- 适度精度 (1.76 kcal/ mol误差,0. 48相关性) 对于22个代体的宽谱抑制剂,通过重组改进.
结论:
- 自由能量计算为预测药物结合选择性提供了严格的框架.
- 方法表明合理的药物设计和目标验证的巨大潜力.
- 修复对称化策略可以提高计算预测的准确性.
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