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使用优化AlphaFold模型进行基于结构的比较虚拟选,识别了选择性的HDAC11抑制剂.

Fady Baselious1, Sebastian Hilscher1, Dina Robaa1

  • 1Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany.

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概括
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科学领域:

  • 生物化学 生物化学
  • 结构生物学 结构生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 基因组脱乙酶11 (HDAC11) 是一个IV类基因组脱乙酶,没有报告的晶体结构.
  • HDAC11的催化域的低序列同样性阻碍了传统的同样性建模.
  • AlphaFold提供了准确的蛋白质结构预测,即使对于新型结构,但需要优化药物设计应用.

研究的目的:

  • 使用优化的HDAC11 AlphaFold模型实现基于结构的虚拟选方法.
  • 为了确定新的和选择性的HDAC11抑制剂.
  • 为了验证优化AlphaFold模型在药物发现中的实用性.

主要方法:

  • 通过结合催化离子和使用已知的抑制剂进行最小化来优化HDAC11 AlphaFold模型.
  • 使用优化模型进行基于结构的比较虚拟选.
  • 试验室酶定量测试以评估已识别的被击中化合物.
  • 分子动力学模拟以证实绑定假设.

主要成果:

  • 一种虚拟选方法成功识别了一种新型的打击化合物.
  • 击中化合物证明了针对HDAC11.11的IC50为3.5μM.
  • 该化合物在10μM时选择性地抑制了HDAC11而不是其他HDAC亚型.
  • 分子动力学模拟支持了对接衍生的结合假设.

结论:

  • 这项研究验证了一种优化的AlphaFold模型方法,用于识别选择性HDAC11抑制剂.
  • 当适当优化时,AlphaFold模型是用于新兴抑制剂发现的适用工具.
  • 这项工作加强了人工智能驱动结构生物学在推动药物发现工作方面的潜力.