分子动力学驱动的分层几何深度学习框架用于蛋白质-连接体相互作用
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
本研究介绍了Dynamics-PLI,这是一个深度学习框架,使用SO(3) -equivariant层次图神经网络 (EHGNNs) 进行蛋白质-连接体结合预测. 它通过结合残留水平信息和分子动态数据,显著提高了准确性.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 准确的蛋白质 - 配体 (PL) 结合预测对于基于结构的药物设计至关重要.
- 现有的等价图神经网络 (EGNN) 方法往往忽略了PL复合体中基本的残留水平信息.
- 了解结合机制需要结合原子层和残留层结构和能量数据.
研究的目的:
- 开发一种新的SO(3) -等价的层次图神经网络 (EHGNN),以捕捉生物分子结构层次.
- 提出Dynamics-PLI,一个集成分子动力学和能量指导的深度学习框架,用于增强PL相互作用预测.
- 为了提高蛋白质-连接体结合亲和力和疗效预测的准确性和可解释性.
主要方法:
- 开发一个SO(3) -EHGNN模型来处理分层生物分子数据.
- 在深度学习框架 (Dynamics-PLI) 中整合分子动力学 (MD) 轨迹和能量信息.
- 使用已确定的指标,评估模型在结合亲和力和连接体效率预测任务上的表现.
主要成果:
- 动力PLI在绑定亲和力预测中实现了4.03%的RMSE降低.
- 该框架显示,AUROC和AUPRC对连接物有效性预测的平均增加为3.95%.
- SO(3) -EHGNN组件在不需要预先培训的情况下表现出强的性能,突出了其固有的分析能力.
结论:
- 拟议的Dynamics-PLI框架显著优于用于蛋白质-联结体相互作用预测的最新方法.
- 通过EHGNN将残留水平信息和MD数据纳入其中,可以提高对约束机制的理解.
- SO(3) -EHGNN为药物设计中分析复杂的生物分子相互作用提供了一种强大而稳健的方法.
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