用水网增强的两态模型来预测蛋白质 - 配体结合的亲和力
Xiaoyang Qu1, Lina Dong1, Ding Luo1
1State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.
Journal of chemical information and modeling
|July 11, 2023
概括
这项研究引入了一种新的深度学习模型,该模型解释了蛋白质-连接体结合过程中水网络的变化. 这种方法提高了药物发现的评分函数的准确性,特别是在具有挑战性的绑定口袋中.
科学领域:
- 计算化学的计算化学
- 结构生物学 结构生物学
- 机器学习在药物发现中的作用
背景情况:
- 蛋白质 - 配体相互作用对于药物发现至关重要.
- 现有的机器学习评分功能往往忽视了水网的动态作用.
- 水分子在未结合和结合状态之间的重新排列显著影响结合亲和力.
研究的目的:
- 开发一种深度学习模型,将来自连接体无结合状态和连接体结合状态的水网络信息纳入其中.
- 提高基于机器学习的评分函数的准确性和稳定性.
- 加强虚拟查和药物设计过程.
主要方法:
- 将扩展连接互动功能集成到图形表示中.
- 图形变压器操作员用于特征提取的应用.
- 开发一个水网增强的双状态模型 (ECIF图::HM-Holo-Apo).
主要成果:
- ECIFGraph::HM-Holo-Apo模型在CASF-2016基准上的评分,排名,对接和选任务中表现出强的表现.
- 该模型在使用DEKOIS2.0数据集的大规模虚拟选测试中取得了卓越的结果.
- 包括水网信息在内显著提高了模型的准确性.
结论:
- 用水网增强的两态模型是增强机器学习评分功能的有效策略.
- 这种方法提高了评分函数的稳定性和适用性,特别是对于具有含水性或暴露于溶剂的结合口袋的目标.
- 开发的模型提供了更现实的蛋白质 - 配体结合相互作用的表现.
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