发现PPAR-γ激动剂:一种由机器学习驱动的综合计算方法
Sajjad Haider1, Muhammad Shafiq1, Ali Raza Siddiqui1
1H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
Journal of molecular graphics & modelling
|February 29, 2024
概括
这项研究开发了一种结合机器学习和in silico药物设计的计算方法,以确定用于糖尿病治疗的氧酶增殖器激活受体马 (PPAR-γ) 的选择性调节器,从而产生有前途的候选药物.
科学领域:
- 生物化学 生化学
- 计算化学的计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 过氧体增殖器激活受体玛 (PPAR-γ) 对于代谢调节和糖尿病胰岛素敏感性至关重要.
- 现有的PPAR-γ激动剂,如thiazolidinediones具有显著的副作用.
- 选择性PPAR-γ调节器提供了一个潜在的替代方案,具有更好的安全性.
研究的目的:
- 利用机器学习和in silico药物设计开发一个集成的计算策略,以发现新的选择性PPAR-γ调节器.
- 为了识别具有高亲和力和有利相互作用的化合物,在PPAR-γ联体结合部位内.
- 评估与PPAR-γ复合的已识别化合物的构造稳定性.
主要方法:
- 在已知的PPAR-γ调节器的化学和物理化学描述器上训练了一种机器学习分类模型.
- 使用机器学习模型对31,750种化合物进行虚拟选.
- 利用分子对接来评估选择的化合物与PPAR-γ的结合亲和力和相互作用.
- 分析了分子动力学模拟,以评估PPAR-γ-结合体复合体的稳定性和构造变化.
主要成果:
- 从虚拟查中确定了68个潜在的PPAR-γ调节器,并选择了四种化合物进行进一步分析.
- 顶级化合物的对接分数在 -8.0 到 -9.1 kcal/mol之间.
- 在PPAR-γ结合部位观察到关键的键相互作用与保存残留物 (His323, Leu330, Phe363, His449, Tyr473).
- 分子动力学模拟表明,在orthosteric网站中适度的形状变化,稳定性指数表明有利的相互作用.
结论:
- 计算策略成功地确定了新的潜在选择性PPAR-γ调节器.
- CHEMBL-3185642和CHEMBL-3554847表现出了出色的结果,突出了PPAR-γ Orthosteric部位内的稳定构造.
- 这些已识别的化合物代表了开发更安全,更有效的针对PPAR-γ的糖尿病治疗的有希望的线索.
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