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使用基于DNN的虚拟查,分子动力学和生物评估来识别新型TACE抑制剂

Shahid Parwez^1,2, Pinaki Parsad Mahapatra¹, Shakil Ahmed¹

¹Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India.

Journal of biomolecular structure & dynamics

|June 29, 2023

概括

研究人员开发了一种深度神经网络模型,用于选类风湿性关节炎 (RA) 药物抑制剂. 三种化合物 (BTB10246,BTB10247,BTB10245) 显示出显著抑制TNF-α转化酶 (TACE),这是RA进展的关键标.

科学领域:

生物化学生物化学
计算化学计算化学
药理学药理学是指药理学的学科.

背景情况:

类风湿性关节炎 (RA) 是一种全球性自身免疫性疾病,影响约1%的成年人.
瘤坏死因子-α (TNF-α) 的过度表达驱动了RA的进展.
TNF-α转化酶 (TACE) 调节TNF-α分泌,是RA的治疗标.

研究的目的:

开发一个深度神经网络 (DNN) 工作流程,用于TACE抑制剂的虚拟查.
为了确定具有潜在抑制TACE治疗RA的潜在抑制活性的新型化合物.
为了验证DNN模型的有效性和已识别的化合物的生物活性.

主要方法:

利用深度神经网络 (DNN) 进行潜在TACE抑制剂的虚拟查.
采用分子对接,列出有前途的化合物.
进行生物评估以确认抑制活性和评估相互作用潜力.

主要成果:

三种化合物 (BTB10246,BTB10247,BTB10245) 在10μM和0.1μM度时表现出显著的TACE抑制.
这些化合物与TACE蛋白具有稳定和显著的相互作用.
DNN模型在识别潜在治疗剂方面被证明是有效的.

结论:

已识别的化合物 (BTB10246,BTB10247,BTB10245) 显示为开发改进的TACE抑制剂的新型支架.
这项研究验证了基于DNN的方法用于类风湿性关节炎的药物发现.
进一步的研究可以优化这些化合物,以提高对RA的治疗疗效.

关键词:

模拟MD的模拟方法类风湿性关节炎类风湿性关节炎类风湿性关节炎这是TACE的税率.深度神经网络是一个神经网络.停靠的对接方式

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