School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
Phys Chem Chem Phys. 2022 May 4;24(17):10124-10133. doi: 10.1039/d1cp05558e.
Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.
评估蛋白质-配体结合亲和力是计算机辅助药物发现过程的重要组成部分。大多数提出的计算方法使用有限的全长蛋白质 3D 结构或简单的全长蛋白质序列作为输入特征来预测蛋白质-配体结合亲和力。因此，蛋白质-配体结合亲和力预测仍然是药物发现中的一个基本挑战。在这项研究中，我们提出了一种新的基于深度学习的方法 DLSSAffinity，用于准确预测蛋白质-配体结合亲和力。与现有方法不同，DLSSAffinity 使用口袋-配体结构对作为局部信息来预测短程直接相互作用。此外，DLSSAffinity 还使用全长蛋白质序列和配体 SMILES 作为全局信息来预测远程间接相互作用。我们在 PDBbind 基准上测试了 DLSSAffinity。结果表明，DLSSAffinity 在测试集上的 Pearson's = 0.79，RMSE = 1.40，SD = 1.35。与现有的基于深度学习的结合亲和力预测方法相比，DLSSAffinity 模型优于其他模型。这些结果表明，将全局序列和局部结构信息组合作为深度学习模型的输入特征可以提高蛋白质-配体结合亲和力预测的准确性。
