Accurate identification of compound–protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug–target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.
通过计算机准确识别化合物-蛋白质相互作用(CPI)能够加深我们对药物作用机制的理解，从而促进药物发现与研发。基于相似度或者对接的传统CPI预测方法通常利用小规模标注数据集的信息而很少利用大量无标签化合物和蛋白质数据中的潜在特征信息。在本研究中，我们提出了一种新颖的CPI预测模型DeepCPI。 DeepCPI通过特征嵌入技术（一种表示学习方法），自动从大规模未标记数据中学习化合物与蛋白质特征表示，并进一步通过结合深度学习技术，精确并大规模的预测CPI。在大规模CPI数据集（ChEMBL，BindingDB和DrugBank）上的计算实验与评估表明，DeepCPI拥有优越的预测性能。除此之外，我们通过湿实验进一步验证了DeepCPI方法预测的多个小分子与三种GPCR蛋白（GLP-1R, GCGR和VIPR）之间的互作关系。综合以上结果，DeepCPI是一个对于药物发现和重定位有效的工具。





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