通过实施机器学习辅助的序列虚拟查技术,探讨编程死亡干-1抑制的起源
Shruthy Kuttappan1,2, Ratul Bhowmik1, C Gopi Mohan3
1Bioinformatics and Computational Biology Lab, Amrita School for Nanosciences and Molecular Medicine, Amrita Vishwa Vidyapeetham, Kochi, Kerala State, 682 041, India.
Molecular diversity
|July 20, 2023
概括
研究人员确定了新型小分子,阻止PD-L1 / PD-1通路,这对于癌症免疫治疗至关重要. 这些潜在的PD-L1检查点抑制剂显示出开发新癌症治疗方法的前景.
科学领域:
- 免疫学 免疫学 免疫学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 编程死亡配体1 (PD-L1) 是一个关键的免疫检查点目标,参与瘤免疫逃避.
- 阻止PD-L1/PD-1相互作用是癌症免疫治疗的一个有希望的策略,旨在恢复抗瘤免疫反应.
- 结构和突变性研究已经阐明了PD-L1/PD-1结合接口.
研究的目的:
- 通过基于结构的分子设计,识别抑制PD-L1/PD-1相互作用的新型小分子.
- 通过计算选和评估潜在的抑制剂,以确定它们的结合亲和力和抑制潜力.
主要方法:
- 基于结构的分子设计被用来识别向PD-L1/PD-1接口的抑制剂.
- 机器学习,分子对接和分子动力学模拟用于选SPECS数据库,识别了9个命中分子.
- 在机器学习和ChEMBL数据库的帮助下,使用定量结构-活动关系 (QSAR) 建模来预测已识别的命中分子的活动.
主要成果:
- 确定了9种潜在的小分子抑制剂,针对PD-L1/PD-1接口.
- 最好的化合物表现出对关键接口残留物强烈的结合,包括A121,M115,I116,S117,I54,Y56,D122和Y123.
- QSAR建模为这些分子的抑制潜力提供了洞察力.
结论:
- 已确定的计算线索代表了进一步临床前评估 (体外和体内) 的有希望的候选人.
- 这些分子具有开发新型PD-L1检查点抑制剂的潜力,用于治疗各种癌症.
- 这项研究强调了计算方法在加速发现向癌症疗法的有效性.
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