Sánchez-Álvarez Axel A, Velasco-Velázquez Marco A, Cordova-Bahena Luis
School of Medicine, Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico.
Graduate Program in Chemical Sciences, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
Pharmaceuticals (Basel). 2025 May 17;18(5):741. doi: 10.3390/ph18050741.
Casein kinase 1 epsilon (CK1ε) plays a critical role in cancer progression by activating oncogenic signaling pathways, making it a target for cancer therapy. However, no inhibitors are currently available for clinical use, highlighting the need for novel therapeutic candidates. This study aimed to identify potential CK1ε inhibitors. To achieve this, a modified version of a previously reported pharmacophore model was applied to an ultra-large database of over 100 million compounds for virtual screening. Hits were filtered based on drug-likeness and pH-dependent pharmacophore compliance and then grouped according to their structural core. A representative compound from each structural group underwent molecular dynamic (MD) simulations and binding free energy calculations to predict its stability and affinity, allowing extrapolation of the results to the entire set of candidates. Pharmacophore matching initially identified 290 compounds. After energy minimization, and an assessment of drug-likeness and pharmacophore compliance, we selected 29 structurally related candidates. MD simulations showed that most of the compounds representative of structural groups had stable binding modes, favorable intermolecular interactions, and free energies comparable to those of previously reported CK1ε inhibitors. An analysis of additional members of the most promising structural group showed that two 2,4-diaminopyrimidine-based compounds likely inhibit CK1ε. These findings provide structural insights into the design of CK1ε inhibitors, supporting compound optimization and the eventual development of targeted cancer therapeutics.
酪蛋白激酶1ε(CK1ε)通过激活致癌信号通路在癌症进展中起关键作用,使其成为癌症治疗的一个靶点。然而,目前尚无临床可用的抑制剂,这凸显了对新型治疗候选物的需求。本研究旨在鉴定潜在的CK1ε抑制剂。为此,将先前报道的药效团模型的修改版本应用于一个超过1亿种化合物的超大型数据库进行虚拟筛选。根据类药性和pH依赖性药效团符合度对命中的化合物进行筛选,然后根据其结构核心进行分组。每个结构组的代表性化合物进行分子动力学(MD)模拟和结合自由能计算,以预测其稳定性和亲和力,从而将结果外推至整个候选物集合。药效团匹配最初鉴定出290种化合物。经过能量最小化以及对类药性和药效团符合度的评估后,我们选择了29种结构相关的候选物。MD模拟表明,大多数结构组的代表性化合物具有稳定的结合模式、良好的分子间相互作用以及与先前报道的CK1ε抑制剂相当的自由能。对最有前景的结构组的其他成员进行分析表明,两种基于2,4-二氨基嘧啶的化合物可能抑制CK1ε。这些发现为CK1ε抑制剂的设计提供了结构见解,支持化合物优化以及最终开发靶向癌症治疗药物。