PDF(Pubmed)
Abstract：
Serine/threonine protein kinases (CK2, PIM-1, RIO1) are constitutively active, highly conserved, pleiotropic, and multifunctional kinases, which control several signaling pathways and regulate many cellular functions, such as cell activity, survival, proliferation, and apoptosis. Over the past decades, they have gained increasing attention as potential therapeutic targets, ranging from various cancers and neurological, inflammation, and autoimmune disorders to viral diseases, including COVID-19. Despite the accumulation of a vast amount of experimental data, there is still no \"recipe\" that would facilitate the search for new effective kinase inhibitors. The aim of our study was to develop an effective screening method that would be useful for this purpose. A combination of Density Functional Theory calculations and molecular docking, supplemented with newly developed quantitative methods for the comparison of the binding modes, provided deep insight into the set of desirable properties responsible for their inhibition. The mathematical metrics helped assess the distance between the binding modes, while heatmaps revealed the locations in the ligand that should be modified according to binding site requirements. The Structure-Binding Affinity Index and Structural-Binding Affinity Landscape proposed in this paper helped to measure the extent to which binding affinity is gained or lost in response to a relatively small change in the ligand\'s structure. The combination of the physico-chemical profile with the aforementioned factors enabled the identification of both \"dead\" and \"promising\" search directions. Tests carried out on experimental data have validated and demonstrated the high efficiency of the proposed innovative approach. Our method for quantifying differences between the ligands and their binding capabilities holds promise for guiding future research on new anti-cancer agents.
摘要：
丝氨酸/苏氨酸蛋白激酶（CK2，PIM-1，RIO1）具有组成活性，高度保守,多效性,和多功能激酶，控制几个信号通路并调节许多细胞功能，如细胞活动，生存,扩散,和凋亡。在过去的几十年里，它们作为潜在的治疗靶点越来越受到关注，从各种癌症和神经系统，炎症,自身免疫性疾病和病毒性疾病，包括COVID-19。尽管积累了大量的实验数据,仍然没有“配方”可以促进寻找新的有效激酶抑制剂。我们研究的目的是开发一种有效的筛选方法，可用于此目的。结合密度泛函理论计算和分子对接，补充了新开发的定量方法，用于比较结合模式，提供了对一组导致其抑制作用的理想特性的深入了解。数学度量有助于评估结合模式之间的距离，而热图揭示了配体中应该根据结合位点要求进行修饰的位置。本文提出的结构结合亲和力指数和结构结合亲和力景观有助于测量响应于配体结构中相对较小的变化而获得或失去结合亲和力的程度。物理化学概况与上述因素的结合使得能够识别“死亡”和“有希望”搜索方向。对实验数据进行的测试已经验证并证明了所提出的创新方法的高效率。我们量化配体之间的差异及其结合能力的方法有望指导未来对新抗癌剂的研究。
