复杂Dnet:一种基于网络的战略,用于发现复杂疾病的关键目标和选活性化合物
Fei Pan1, Zhao-Min Xu1, Ze-Hui Liu1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
一个新的框架 - - ComplexDnet - - 确定了诸如代谢相关脂肪肝炎 (MASH) 这样的复杂疾病的关键目标. 它发现RORγt和一种潜在的药物,panaxatriol,加速治疗发现.
科学领域:
- 计算生物学
- 药物发现
- 基因组学
背景情况:
- 诸如代谢相关脂肪肝炎 (MASH) 这样的复杂疾病在确定治疗点方面存在重大挑战.
- 现有的方法往往难以有效地优先考虑与疾病相关的基因和途径.
研究的目的:
- 开发和验证ComplexDnet,这是一个用于优先考虑复杂疾病治疗目标的新计算框架.
- 确定MASH的关键调节剂和潜在的治疗化合物.
- 为研究界提供一个开源工具.
主要方法:
- 开发了复杂Dnet,将转录基因数据与生物网络分析结合起来.
- 对八种癌症类型应用了ComplexDnet,并与现有方法进行了比较.
- 使用ComplexDnet分析MASH,确定与视网膜相关的孤儿受体γt (RORγt) 是一个关键调节器.
- 进行基于网络的虚拟查以识别RORγt逆激应剂,随后进行实验验证,包括X射线结晶学和小鼠模型.
主要成果:
- 在不同类型的癌症中,ComplexDnet表现出优异的目标优先级,平均回忆率为77.63%.
- 确定了RORγt作为MASH相关炎症和纤维化的中心调节剂.
- 发现松醇 (PXT) 是一种具有结构和功能活性的强烈RORγt逆agonist.
- 在临床前的MASH模型中,PXT显著减弱了纤维化.
结论:
- ComplexDnet是一个有效的框架,用于在复杂疾病中发现功能和结构相关的治疗点.
- 该研究确定了RORγt和PXT作为MASH的有希望的点/治疗方法.
- 开源版本的ComplexDnet软件有助于在药物发现中得到更广泛的应用.
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