图纳:一个针对目标的统一网络,通过多模式特征集成来预测蛋白质 - 连接物结合的亲和力
IEEE journal of biomedical and health informatics
|December 12, 2025
概括
我们开发了TUNA,这是一个深度学习模型,用于预测蛋白质 - 配体结合亲和力. 图纳集成了蛋白质序列,结合口袋信息和连接体特征,提高了准确性,特别是对于缺乏已知的结构的目标.
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:
- 准确的蛋白质 - 配体结合亲和力预测对于药物发现至关重要.
- 基于序列的深度学习模型是可扩展的,但往往忽视了局部绑定站点上下文.
- 结构预测和口袋检测方面的进步允许将绑定站点信息集成到基于序列的模型中.
研究的目的:
- 介绍 TUNA,一种用于增强结合亲和力预测的新型深度学习模型.
- 整合多模式特征,包括全球蛋白序列,局部口袋表示和连接体特征.
- 提高预测准确度,特别是对于没有实验确定结构的蛋白质标.
主要方法:
- 图纳集成了全球蛋白序列,口袋表示和连接体特征 (SMILES和分子图).
- 对于缺乏实验结构的蛋白质,使用了3D结构推断和口袋检测工具.
- 采用预先训练的嵌入和对联体的融合策略编码了多模式特征.
主要成果:
- 图纳在PDBbind和BindingDB数据集上表现出与现有的基于序列的模型相比的持续改进.
- 该模型与PDBbind数据集上的基于结构的方法保持竞争力.
- 图纳的可解释的交叉模式注意力机制有助于识别潜在的结合点.
结论:
- 图纳 (Tuna) 提供了一种有效的方法来预测蛋白质 - 配体结合的亲和力.
- 该模型擅长预测缺乏已知的3D结构的目标的亲和力.
- 整合多模数据增强了基于序列的深度学习模型的预测能力.
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