革命性的GPCR-联体预测:DeepGPCR具有高精度药物发现实验验证
Haiping Zhang1, Hongjie Fan2, Jixia Wang2,3
1Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China.
新的深度学习模型,DeepGPCR_BC和DeepGPCR_RG,使用非结构数据准确预测与G蛋白合受体 (GPCRs) 的药物相互作用. 这些模型允许有效的虚拟选用于针对GPCRs的药物发现.
科学领域:
- 计算化学和结构生物学
- 药物发现和药物化学
背景情况:
- G蛋白结合受体 (GPCR) 是重要的药物标,但它们的膜结合性质使结构确定复杂化,并阻碍了传统的药物相互作用建模.
- 现有的计算模型因有限,低质量的结构数据和训练在可溶性蛋白质上的通用模型不足而与GPCRs扎.
研究的目的:
- 利用非结构性数据开发新的计算模型,用于预测G蛋白合受体 (GPCR) - 连接体相互作用.
- 为了实现高效和准确的大规模虚拟查,用于GPCR向药物发现.
主要方法:
- 开发了两个模型,DeepGPCR_BC (二进制分类) 和DeepGPCR_RG (亲和度预测),利用图形卷积网络和mol2vec.
- 以图形形式表示GPCR结合口袋和配体,处理非结构性相互作用数据.
- 采用基于图形的深度学习来捕获物理化学和空间信息,用于预测建模.
主要成果:
- DeepGPCR_BC实现了0.72的AUC,0.68的精度和0.73的TPR,超过了标准对接工具的性能.
- DeepGPCR_RG证明了亲和力预测的皮尔森相关性为0.39和RMSE为1.34.
- 成功选了GPR35的候选药物,并确定了GLP-1R的活性抑制剂.
结论:
- 开发的针对GPCR的深度学习模型为虚拟选提供了一种高效和准确的方法.
- 这些模型可以显著加速针对G蛋白合受体 (GPCRs) 的药物发现工作.
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