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使用图形卷积网络,分子对接,分子动力学模拟和生物评估发现新型TACE抑制剂

Muhammad Yasir¹, Jinyoung Park¹, Eun-Taek Han²

¹Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.

|December 27, 2024

概括

深度学习确定了沃里诺斯塔特作为瘤坏死因子-α转化酶 (TACE) 的新型抑制剂. 这种重新设计的癌症药物在治疗诸如类风湿性关节炎等炎症性疾病方面表现有前途.

科学领域:

计算机化药物发现.
药理学药理学是指药理学的学科.
生物化学生物化学

背景情况:

瘤坏死因子-α转化酶 (TACE),或ADAM17,是炎症反应的关键调解者.
向TACE是治疗炎症性疾病,包括类风湿性关节炎的一个有希望的策略.
药物重新定位提供了一种有效的方法来识别新型治疗剂.

研究的目的:

通过使用深度学习模型,从FDA批准的药物中识别新型TACE抑制剂.
通过计算和实验方法验证已识别的候选药物的抑制潜力.
为了探索沃里诺斯塔特在炎症状况中的重新用途.

主要方法:

使用DUD-E数据集开发了一个集成的深度学习模型 (GraphConvMol).
该模型预测了FDA批准的药物库的TACE抑制活性.
分子对接,分子动力学模拟和基于细胞的测试被用于验证.

主要成果:

沃里诺斯塔特被确定为一种潜在的TACE抑制剂.
计算分析显示,沃里诺斯塔特和TACE之间存在强烈的结合相互作用.
实验评估证实了沃里诺斯塔特在RAW 264.7细胞中具有可比的TACE抑制活性.

结论:

深度学习模型有效地增强了用于药物发现的虚拟选.
沃里诺斯塔特显示出作为TACE抑制剂的显著潜力,用于炎症性疾病.
这项研究证实了沃里诺斯塔特在类风湿性关节炎和其他炎症状况中的重用.

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