整合深度学习和分子动力学模拟来发现FXR对手
Yueying Yang1, Yuxin Huang1, Hanxiao Shen1
1Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China.
Molecular diversity
|April 2, 2025
概括
深度学习模型从人类代谢物中确定了潜在的Farnesoid X受体 (FXR) 反对者. 两个化合物,HMDB0253354和HMDB0242367,显示出治疗代谢疾病的希望.
科学领域:
- 生物化学 生物化学
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
背景情况:
- 法内索伊德X受体 (FXR) 对于胆酸,脂质和葡萄糖平衡至关重要.
- FXR对抗剂为胆固醇,代谢障碍和癌症提供治疗潜力.
- 由于缺乏已批准的FXR抗剂,因此需要新的药物发现方法.
研究的目的:
- 开发深度学习模型来预测FXR对抗活性和毒性.
- 选人类代谢物潜在的FXR抗剂.
- 确定新型FXR抗候选人用于代谢性疾病治疗.
主要方法:
- 开发了深度学习模型来预测FXR对抗活性 (ANTCL) 和毒性 (TOXCL).
- 从HMDB数据库中选了217,345种化合物.
- 利用分子动力学模拟和结合自由能计算.
主要成果:
- 确定了11种具有显著FXR结合潜力的人类代谢物候选物.
- 与参考Gly-MCA相比,五种复合物显示出增强的稳定性.
- HMDB0253354 (Fulvestrant) 和HMDB0242367 (ZM 189154) 呈现出有利的结合的自由能量.
- 涉及MET328,PHE329和ALA291的疏水相互作用是复杂稳定性的关键.
结论:
- 深度学习对于发现FXR对手是有效的.
- HMDB0253354和HMDB0242367是新陈代谢疾病治疗的有希望的候选者.
- 对这些化合物的进一步研究可能会导致新的治疗方法.
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