Industrial Engineering and Management Systems, University of Central Florida, Street, 32816, 4000 Central Florida Blvd. Orlando, USA.
College of Medicine, Bionix Cluster, University of Central Florida, 4000 Central Florida Blvd. Orlando, 32816, Florida, USA.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac272.
In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug-target interaction prediction. Our proposed model is inspired by sentence classification models in the field of Natural Language Processing, where the drug-target complex is treated as a sentence with relational meaning between its biochemical entities a.k.a. protein pockets and drug molecule. AttentionSiteDTI enables interpretability by identifying the protein binding sites that contribute the most toward the drug-target interaction. Results on three benchmark datasets show improved performance compared with the current state-of-the-art models. More significantly, unlike previous studies, our model shows superior performance, when tested on new proteins (i.e. high generalizability). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict the binding interactions between some candidate compounds and a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally predicted and experimentally observed (measured) drug-target interactions illustrates the potential of our method as an effective pre-screening tool in drug repurposing applications.
在这项研究中，我们引入了一种可解释的基于图的深度学习预测模型 AttentionSiteDTI，该模型利用蛋白质结合位点和自注意力机制来解决药物-靶标相互作用预测的问题。我们提出的模型受到自然语言处理领域中句子分类模型的启发，将药物-靶标复合物视为具有关系意义的句子，其生化实体（即蛋白质口袋）和药物分子之间存在关系意义。AttentionSiteDTI 通过识别对药物-靶标相互作用贡献最大的蛋白质结合位点来实现可解释性。在三个基准数据集上的结果表明，与当前最先进的模型相比，性能得到了提高。更重要的是，与之前的研究不同，我们的模型在测试新蛋白质时（即高泛化能力）表现出了优异的性能。通过多学科合作，我们进一步实验评估了我们提出的方法的实际潜力。为了实现这一目标，我们首先在计算上预测了一些候选化合物与靶蛋白之间的结合相互作用，然后在实验室中对这些化合物对的结合相互作用进行了实验验证。计算预测和实验观察（测量）的药物-靶标相互作用之间的高度一致性说明了我们的方法作为药物重新利用应用中的有效预筛选工具的潜力。
