PLMCA:用于口袋识别和绑定亲和力预测的通用多模式蛋白质-连接体交叉注意力框架
Yi He1, Minghao Liu1, Haohao Wang1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin Road 2699, Changchun 130012, China.
Journal of medicinal chemistry
|January 29, 2026
概括
预测药物标结合亲和力 (DTA) 对药物发现至关重要. 作为一个多式联络框架,PLMCA集成了各种数据,用于准确的DTA预测和蛋白质结合口袋识别.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 准确的药物标结合亲和力 (DTA) 预测对于有效的药物发现至关重要.
- 整合不同的数据类型 (序列,结构,物理化学性质) 仍然是一个挑战.
- 实验测定条件可以引入噪声和批量效应.
研究的目的:
- 开发一个多式联运框架,PLMCA,用于准确的DTA预测.
- 统一各种数据表示,包括蛋白质序列嵌入,3D几何特征,物理化学描述符和连接体分子图.
- 纳入实验测试条件以提高预测的稳定性.
主要方法:
- 在PLMCA中,使用的是多模式的蛋白质-连接体交叉注意力机制.
- 它整合了来自两个语言模型的蛋白质序列嵌入.
- 它包含3D几何特征,物理化学描述符,联体分子图和ChEMBL实验测试条件.
主要成果:
- 与PDBbind21数据集上的最新方法相比,PLMCA在各种分支中实现了Kd和Ki预测的竞争性或优异的性能.
- 在ChEMBL_mini数据集上,PLMCA的R2值为0.531 (IC50),0.635 (Kd) 和0.519 (Ki).
- PLMCA证明了强大的蛋白质结合口袋预测,在看不见的蛋白质环境中达到高达0.655的AUPR.
结论:
- PLMCA有效地整合了多式联运数据,用于增强DTA预测.
- 该框架在亲和力预测和绑定口袋识别方面都显示出前景.
- PLMCA提供了一种强大的方法来应对计算药物发现的挑战.
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