DeepBindGCN:将分子向量表示与图形卷积神经网络集成,用于预测蛋白质-连接体相互作用
Haiping Zhang1, Konda Mani Saravanan2, John Z H Zhang1,3,4
1Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Molecules (Basel, Switzerland)
|June 28, 2023
概括
DeepBindGCN准确地预测了蛋白质 - 连接体结合亲和力,而不需要对接构造. 这种新型的图形卷积网络模型通过识别高 afinity 化合物来提高药物虚拟查效率.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 大规模的药物虚拟查需要从庞大的分子图书馆中准确识别高亲和度结合剂.
- 蛋白质口袋,连接体空间信息和残留物/原子类型是影响结合亲和力的关键因素.
研究的目的:
- 开发一种新的计算模型,以准确高效地预测蛋白质 - 配体结合亲缘关系.
- 创建一个选管道,整合新的模型来识别强效药物候选者.
主要方法:
- 使用口袋残留物或连接体原子作为节点,根据邻近的信息构建边缘以表示分子结构.
- 采用了一个图形卷积网络 (GCN) 模型,DeepBindGCN,结合预训练的分子向量来增强表示.
- 开发了一个选管道,将DeepBindGCN与其他方法集成在一起,使用TIPE3和PD-L1二元体示例进行验证.
主要成果:
- 在PDBbind v.2016核心集上,DeepBindGCN实现了1.4190的根平均平方误差 (RMSE) 和0.7584的Pearson r值.
- 证明了与依赖3D复杂结构的最先进模型相对应的预测能力.
- 该模型独立于对接构造,保留空间和物理化学特征.
结论:
- DeepBindGCN提供了一种强大的,不依赖复杂性的工具,用于预测蛋白质 - 连接体相互作用.
- 该模型显著提高了大规模虚拟药物查的效率和准确性.
- 这种方法对药物发现和开发中的各种应用具有前景.
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