Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada.
School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Molecules. 2022 Aug 11;27(16):5114. doi: 10.3390/molecules27165114.
Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.
计算预测配体-靶相互作用是现代药物发现的一个关键部分，因为它有助于避免体外和体内筛选的高成本和劳动力需求。随着生物活性数据的积累，为开发具有更高预测能力的深度学习 (DL) 模型提供了机会。传统上，这些模型要么仅限于使用非常简化的蛋白质表示，要么对其 3D 结构进行无效的体素化。在此，我们提出了 PSG-BAR（蛋白质结构图-结合亲和力回归）方法的开发，该方法利用了蛋白质的 3D 结构信息以及配体的 2D 图形表示。该方法还引入了注意力分数，以有选择地加权对配体结合最重要的蛋白质区域。结果：所开发的方法在几个结合亲和力基准测试数据集上表现出了最先进的性能。基于注意力的蛋白质图池化能够识别表面残基作为蛋白质-配体结合的关键残基。最后，我们针对 SARS-CoV-2 冠状病毒的标志性靶标——病毒主蛋白酶 (Mpro) 上的实验测定对我们的模型预测进行了验证。
