Wei Tian-Hua, Zhou Shuang-Shuang, Jing Xiao-Long, Liu Jia-Chuan, Sun Meng, Zhao Zong-Hao, Li Qing-Qing, Wang Zi-Xuan, Yang Jin, Zhou Yun, Wang Xue, Ling Cheng-Xiao, Ding Ning, Xue Xin, Yu Yan-Cheng, Wang Xiao-Long, Yin Xiao-Ying, Sun Shan-Liang, Cao Peng, Li Nian-Guang, Shi Zhi-Hao
National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, Jiangsu 210023, China.
Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing 211198, China.
J Chem Inf Model. 2024 Dec 23;64(24):9528-9550. doi: 10.1021/acs.jcim.4c01830. Epub 2024 Dec 2.
Developing selective kinase inhibitors remains a formidable challenge in drug discovery because of the highly conserved structural information on adenosine triphosphate (ATP) binding sites across the kinase family. Tailoring docking protocols to identify promising kinase inhibitor candidates for optimization has long been a substantial obstacle to drug discovery. Therefore, we introduced "Kinase-Bench," a pioneering benchmark suite designed for an advanced virtual screen, to improve the selectivity and efficacy of kinase inhibitors. Our comprehensive data set includes 6875 selective ligands and 422,799 decoys for 75 kinases, using extensive bioactivity and structural data from the ChEMBL database and decoys generated by the Directory of Useful Decoys-Enhanced version. Our benchmarking sets and retrospective case studies were designed to provide useful guidance in discovering selective kinase inhibitors. We employed a Glide High-Throughput Virtual Screen and Standard Precision complemented by three scoring functions and customized protein-ligand interaction filters that target specific kinase residue interactions. These innovations were successfully implemented in our virtual screen efforts targeting JAK1 inhibitors, achieving selectivity against its family member, TYK2. Consequently, we identified novel potential hits: Compound (JAK1 IC: 980.5 nM, TYK2 IC: 4.5 μM) and the approved pan-AKT inhibitor Capivasertib (JAK1 IC: 275.9 nM, TYK2 IC: 10.9 μM). Using the Kinase-Bench protocol, both compounds demonstrated substantial JAK1 selectivity, making them strong candidates for further investigation. Our pharmaceutical results underscore the utility of tailored virtual screen protocols in identifying selective kinase inhibitors with substantial implications for rational drug design. Kinase-Bench offers a robust toolset for selective kinase drug discovery with the potential to effectively guide future therapeutic strategies effectively.
由于激酶家族中三磷酸腺苷（ATP）结合位点的结构信息高度保守，开发选择性激酶抑制剂在药物研发中仍然是一项艰巨的挑战。定制对接方案以识别有前景的激酶抑制剂候选物进行优化，长期以来一直是药物研发的重大障碍。因此，我们引入了“激酶基准测试（Kinase - Bench）”，这是一个为高级虚拟筛选设计的开创性基准测试套件，以提高激酶抑制剂的选择性和功效。我们的综合数据集包括针对75种激酶的6875种选择性配体和422,799种诱饵，使用了来自ChEMBL数据库的广泛生物活性和结构数据以及由有用诱饵目录增强版生成的诱饵。我们的基准测试集和回顾性案例研究旨在为发现选择性激酶抑制剂提供有用的指导。我们采用了Glide高通量虚拟筛选和标准精度，并辅以三种评分函数以及针对特定激酶残基相互作用的定制蛋白质 - 配体相互作用过滤器。这些创新成功应用于我们针对JAK1抑制剂的虚拟筛选工作中，实现了对其家族成员TYK2的选择性。因此，我们鉴定出了新的潜在命中物：化合物（JAK1 IC：980.5 nM，TYK2 IC：4.5 μM）和已获批的泛AKT抑制剂卡匹西利（JAK1 IC：275.9 nM，TYK2 IC：10.9 μM）。使用激酶基准测试方案，这两种化合物都表现出显著的JAK1选择性，使其成为进一步研究的有力候选物。我们的药物研发结果强调了定制虚拟筛选方案在识别选择性激酶抑制剂方面的实用性，这对合理药物设计具有重要意义。激酶基准测试为选择性激酶药物研发提供了一个强大的工具集，有可能有效地指导未来的治疗策略。
