统一的蛋白质-小分子图 神经网络用于结合部位预测
bioRxiv : the preprint server for biology
|September 18, 2025
概括
一个新的图形神经网络YuelPocket准确地预测了蛋白质上的小分子结合点. 这种方法捕获了长距离的相互作用,通过识别关键的连接体结合口袋来改善药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 药物发现 药物发现
背景情况:
- 预测蛋白质上的小分子结合点对于基于结构的药物发现至关重要.
- 现有的图形神经网络方法由于有限的数据集和局部特征焦点,往往忽视了远程交互.
- AlphaFold3具有先进的蛋白质结构预测,但没有连接体结合位点识别.
研究的目的:
- 开发一个先进的图形神经网络模型,YuelPocket,用于准确预测小分子结合点.
- 通过结合全球蛋白质-联结体相互作用来解决当前方法的局限性.
- 在药物发现中提高识别功能结合口袋的效率和准确性.
主要方法:
- 开发了YuelPocket,这是一个带有全球图形设计的图形神经网络,使用虚拟联合节点.
- 虚拟联合节点连接所有蛋白质残留物和小分子原子,以建模远程相互作用.
- 在全面的MOAD数据集上训练了YuelPocket,其中包括超过38,000个蛋白质小分子复合体.
主要成果:
- 在预测小分子结合部位方面,YuelPocket表现出了卓越的性能.
- 在COACH420数据集上实现了0.85的接收器运行特征曲线 (AUC-ROC) 下的面积.
- 在Holo4k数据集上获得了0.89的AUC-ROC,优于现有方法.
结论:
- YuelPocket有效地模拟了长距离的蛋白质-连接体相互作用,以精确预测结合部位.
- 创新的全球图形设计提供了一个计算效率高的解决方案.
- YuelPocket代表了计算机药物发现的重大进步,用于识别潜在的药物标.
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