Kinase-Bench: Comprehensive Benchmarking Tools and Guidance for Achieving Selectivity in Kinase Drug Discovery.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL
Tian-Hua Wei, Shuang-Shuang Zhou, Xiao-Long Jing, Jia-Chuan Liu, Meng Sun, Zong-Hao Zhao, Qing-Qing Li, Zi-Xuan Wang, Jin Yang, Yun Zhou, Xue Wang, Cheng-Xiao Ling, Ning Ding, Xin Xue, Yan-Cheng Yu, Xiao-Long Wang, Xiao-Ying Yin, Shan-Liang Sun, Peng Cao, Nian-Guang Li, Zhi-Hao Shi
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引用次数: 0

Abstract

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 2 (JAK1 IC50: 980.5 nM, TYK2 IC50: 4.5 μM) and the approved pan-AKT inhibitor Capivasertib (JAK1 IC50: 275.9 nM, TYK2 IC50: 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.

激酶平台:全面的基准工具和指导,实现激酶药物发现的选择性。
由于整个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仍然是药物发现中的一个艰巨挑战。定制对接方案,以确定有前途的激酶抑制剂候选物进行优化,长期以来一直是药物发现的重大障碍。因此,我们推出了“激酶- bench”,这是一种开创性的基准套件,专为先进的虚拟筛选而设计,以提高激酶抑制剂的选择性和有效性。我们的综合数据集包括6875个选择性配体和75个激酶的422,799个诱饵,使用ChEMBL数据库中广泛的生物活性和结构数据以及有用诱饵目录-增强版生成的诱饵。我们的基准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,辅以三个评分功能和定制的蛋白质-配体相互作用过滤器,针对特定的激酶残基相互作用。这些创新成功地应用于我们针对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,我们确定了新的潜在靶点:化合物2 (JAK1 IC50: 980.5 nM, TYK2 IC50: 4.5 μM)和已批准的泛akt抑制剂Capivasertib (JAK1 IC50: 275.9 nM, TYK2 IC50: 10.9 μM)。使用激酶- bench协议,这两种化合物都显示出大量的JAK1选择性,使它们成为进一步研究的强有力的候选者。我们的药物研究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效用,这对合理的药物设计具有重大意义。激酶- bench为选择性激酶药物发现提供了一个强大的工具集,具有有效指导未来治疗策略的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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