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识别潜在的JNK3抑制剂:使用分子对接和基于深度学习的虚拟查的综合方法

Chenpeng Yao^1,2, Zheyuan Shen^2,3, Liteng Shen^2,3

¹Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Institute of Pharmacology and Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.

Pharmaceuticals (Basel, Switzerland)

|October 28, 2023

概括

研究人员使用虚拟选工作流程识别了一种有前途的化合物 (化合物6),以抑制JNK3. 这种分子显示出治疗神经炎症和神经退行性疾病 (如阿尔茨海默氏症) 的潜力.

科学领域:

生物化学和分子生物学
神经科学是一个神经科学.
计算化学计算化学

背景情况:

JNK3是一种由压力激活的基因激活蛋白激酶,与神经退行性疾病有关.
现有的JNK抑制剂尚未获得市场批准,突出显示了治疗缺口.
准JNK3为治疗亨廷顿病,帕金森病和阿尔茨海默病等疾病提供了潜力.

研究的目的:

开发和实施虚拟查工作流程,用于识别新型JNK3抑制剂.
评估潜在的小分子对高结合亲和力和针对JNK3.3的治疗疗效.

主要方法:

采用基于能源的虚拟选方法,并与rescoring技术集成 (滑翔对接,MM/GBSA,DeepDock,图形神经网络).
从生物活动评估的虚拟选中选择了十个候选分子.
利用结合姿势元动力学和分子动力学模拟来阐明化合物的结合模式.

主要成果:

确定了化合物6作为最有前途的JNK3抑制剂候选物.
化合物6表现出强大的激酶抑制活性 (IC50 = 130.1 nM) 并减少了巨细胞中TNF-α的释放.
详细的分子动力学模拟提供了关于化合物6与JNK3.3的结合相互作用的见解.

结论:

混合虚拟选工作流程有效识别了强大的JNK3抑制剂.
化合物6显示出作为神经炎症和神经退行性疾病治疗剂的显著潜力.
进一步优化化合物6的药物化学是药物开发的必要条件.

关键词:

在JNK3中,JNK3是最常见的.结合的姿势是元动力学.生物活性评估评估生物活性评估混合虚拟选混合虚拟选分子动力学分子动力学

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