关键词: 蛋白激酶, 激酶抑制剂, 相互作用预测, 同源模建, 分子对接

Abstract:

Protein kinases play critical roles in many biological processes, including signal transduction, gene transcription, and protein translation, and are therefore closely associated with various disease states. The screening of kinase inhibitors has become an important aspect of anti-tumor drug development, and has been refined to allow high-throughput, multi-target screening based on the entire human kinome. To reduce the experimental costs of large-scale inhibitor screening and to increase the success rate, our group has designed a multi-target molecular docking systemcapable of predicting kinase-inhibitor interactions. In this work we initially used homology modeling to construct three-dimensional (3D) models for approximately 500 catalytic domains of human kinase variants. We subsequently performed molecular docking to calculate the binding affinities of kinase-inhibitor pairs, employing the 3D models as receptors and kinase inhibitors as ligands. The results show that our multi-target docking system accurately predicts the interactions between known inhibitors and kinase variants, and that the calculated binding affinities are highly correlated with the experimental values. Thus, this molecular docking system could be used for computational screening of multi-target kinase inhibitors, thereby providing a theoretical basis for the development of kinase inhibitors and the design of anti-tumor drugs.

Key words: Protein kinase, Kinase inhibitor, Interaction prediction, Homology modeling, Molecular docking