Wu Yifan, Gao Min, Zeng Min, Zhang Jie, Li Min
SenseTime Research, Shanghai 200233, China.
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics. 2022 Apr 28;38(9):2571-2578. doi: 10.1093/bioinformatics/btac155.
Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug-protein association network and predict DPIs by the information of its associated proteins or drugs, called 'guilt-by-association' principle. However, the 'guilt-by-association' principle is not always true because sometimes similar proteins cannot interact with similar drugs. Recently, learning-based methods learn molecule properties underlying DPIs by utilizing existing databases of characterized interactions but neglect the network-level information.
We propose a novel method, namely BridgeDPI. We devise a class of virtual nodes to bridge the gap between drugs and proteins and construct a learnable drug-protein association network. The network is optimized based on the supervised signals from the downstream task-the DPI prediction. Through information passing on this drug-protein association network, a Graph Neural Network can capture the network-level information among diverse drugs and proteins. By combining the network-level information and the learning-based method, BridgeDPI achieves significant improvement in three real-world DPI datasets. Moreover, the case study further verifies the effectiveness and reliability of BridgeDPI.
The source code of BridgeDPI can be accessed at https://github.com/SenseTime-Knowledge-Mining/BridgeDPI. The source data used in this study is available on the https://github.com/IBM/InterpretableDTIP (for the BindingDB dataset), https://github.com/masashitsubaki/CPI_prediction (for the C.ELEGANS and HUMAN) datasets, http://dude.docking.org/ (for the DUD-E dataset), repectively.
探索药物 - 蛋白质相互作用（DPI）为协助实验室实验发现新药提供了一种快速且精确的方法。基于网络的方法通常利用药物 - 蛋白质关联网络，并通过其相关蛋白质或药物的信息来预测DPI，这被称为“关联有罪”原则。然而，“关联有罪”原则并非总是成立，因为有时相似的蛋白质无法与相似的药物相互作用。最近，基于学习的方法通过利用已有的特征相互作用数据库来学习DPI背后的分子特性，但忽略了网络层面的信息。
我们提出了一种新颖的方法，即BridgeDPI。我们设计了一类虚拟节点来弥合药物和蛋白质之间的差距，并构建一个可学习的药物 - 蛋白质关联网络。该网络基于来自下游任务——DPI预测的监督信号进行优化。通过在这个药物 - 蛋白质关联网络上传递信息，图神经网络可以捕捉不同药物和蛋白质之间的网络层面信息。通过结合网络层面信息和基于学习的方法，BridgeDPI在三个真实世界的DPI数据集上取得了显著改进。此外，案例研究进一步验证了BridgeDPI的有效性和可靠性。
BridgeDPI的源代码可在https://github.com/SenseTime-Knowledge-Mining/BridgeDPI获取。本研究中使用的源数据分别可在https://github.com/IBM/InterpretableDTIP（用于BindingDB数据集）、https://github.com/masashitsubaki/CPI_prediction（用于秀丽隐杆线虫和人类数据集）、http://dude.docking.org/（用于DUD - E数据集）获取。
