Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors

将基于机器学习的虚拟筛选与多种蛋白质结构和生物测定评估相结合,以发现新型 GSK3β 抑制剂

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作者:Jingyu Zhu, Yuanqing Wu, Man Wang, Kan Li, Lei Xu, Yun Chen, Yanfei Cai, Jian Jin

Abstract

Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some "hot residues" critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors.

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