探索用基于电子密度的几何深度学习来预测蛋白质 - 配体结合的亲和力
Clemens Isert1, Kenneth Atz1, Sereina Riniker1
1ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch +41 44 633 73 27.
RSC advances
|February 5, 2024
概括
这项研究探索了使用电子密度键关键点来预测蛋白质-连接体结合亲和力. 虽然显示出希望,但这种方法并没有显著超过现有的计算药物设计方法.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 基于结构的药物设计需要准确预测蛋白质-连接体结合亲和力.
- 当前的深度学习方法可能无法完全捕捉物理交互,或者可能具有偏见.
- 电子密度提供了分子相互作用的基本物理表征.
研究的目的:
- 研究来自电子密度的键关键点的实用性,用于预测结合亲和力.
- 通过使用这些点对现有方法进行几何深度学习模型的基准测试.
- 批判性地分析电子密度在药物设计的深度学习中的作用.
主要方法:
- 使用了几何深度学习模型.
- 从蛋白质 - 连接体复合体的电子密度获得的内置键关键点.
- 在PDBbind和PDE10A数据集上评估模型性能.
- 分析了电子密度和结合亲和力之间的相关性.
主要成果:
- 模型实现了1.4-1.8日志单位 (PDBbind) 和1.0-1.7日志单位 (PDE10A) 的根平均平方误差.
- 性能与基准方法相当,没有显著的优势.
- 对于一些目标,在电子密度和结合亲和力之间观察到皮尔森相关系数 (r > 0.7).
- 发现电子密度对深度学习模型的实用性取决于上下文.
结论:
- 电子密度键关键点为蛋白质-连接体相互作用分析提供了物理基础的方法.
- 在深度学习中直接应用绑定亲和力预测显示了取决于上下文的实用性.
- 需要进一步的研究,以优化电子密度特征的集成到计算药物设计模型中.
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