用利科哈尔康D向STING的寡合化改善了STING驱动的炎症性疾病
Yinghui Zhang1,2, Yadan Liu1,3, Bing Jiang3
1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
使用人工智能模型发现了一种新的STING抑制剂Licochalcone D (LicoD). 利科德 (LicoD) 协同修改了STING,在炎症性疾病和癌症的小鼠模型中减少了炎症.
科学领域:
- 免疫学 免疫学 免疫学
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 刺痛通路的激活对天生的免疫力至关重要,但驱动着炎症性疾病.
- 由于复杂的激活步骤和传统药物查局限性,开发STING抑制剂面临挑战.
- 向STING为各种炎症状况提供了治疗策略.
研究的目的:
- 使用基于序列的AI模型识别新型STING抑制剂.
- 为了研究Licochalcone D (LicoD) 作为STING抑制剂的作用机制.
- 在STING相关疾病的临床前模型中评估LicoD的治疗潜力.
主要方法:
- 利用变压器CPI模型对结构独立的化合物进行针对STING的选.
- 鉴定了利可沙龙D (LicoD) 作为一种强大的STING抑制剂.
- 研究了LicoD在Cys148处对STING的共价变异及其对信号通路的下游影响.
- 在Trex1-1-小鼠,DSS诱导的大肠炎和AOM/DSS诱导的大肠炎相关结肠癌模型中评估了LicoD的疗效.
主要成果:
- 通过对Cys148进行共聚性修饰,LicoD作为一种强效和选择性的STING抑制剂,独立于经典的带结合口袋.
- 利科德抑制了STING的寡合化,TBK1的招募和IRF3/NF-κB的核转移.
- 在小鼠模型中,LicoD治疗显著改善了炎症表型,包括结肠炎和结肠炎相关的癌症.
结论:
- 利科哈尔科恩D是一种有前途的治疗药物,可用于治疗由STING驱动的炎症性疾病.
- 由人工智能驱动的TransformerCPI模型有效地识别了超越传统结合位点的全抑制剂.
- 这项研究突出了针对STING相关病理的新型治疗方法和发现平台.
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