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科学领域:

  • 病毒学 病毒学
  • 药用化学 医学化学
  • 计算生物学 计算生物学

背景情况:

  • 病毒蛋白酶对于病毒复制至关重要,并且是抗病毒药物的关键标.
  • 病毒蛋白酶中的药物耐药性突变,如SARS-CoV-2主要蛋白酶 (Mpro),挑战了现有疗法的有效性.
  • 开发下一代抗病毒药物需要克服或预防耐药性的策略.

研究的目的:

  • 制定一项全面的策略来打击抗病毒药物耐药性,重点关注SARS-CoV-2主要蛋白酶 (Mpro).
  • 设计和合成针对Mpro的新型抑制剂和降解剂,包括对抗耐药变体有效的新型抑制剂和降解剂.
  • 采用先进的计算和人工智能方法来指导抗病毒药物的设计和评估.

主要方法:

  • 对α-胺胺库进行选,以确定初始抑制剂.
  • 使用计算和结构洞察力设计和合成共价Mpro抑制剂 (例如,H135,H102).
  • 开发第一个针对Mpro进行蛋白酶降解的PROTAC分子 (HP211206).
  • 计算化学的应用 (PDLD/S-LRA/β框架,QM计算) 用于具有约束力的自由能量评估.
  • 使用活力策略来评估抑制剂结合和酶催化效率.
  • 使用动态模拟来建模时间依赖的抑制.
  • 实施人工智能 (D2Screen) 用于对非共价抑制剂进行虚拟查.

主要成果:

  • 化合物17显示出对SARS-CoV-2的初始抑制活性.
  • 协同抑制剂H135和H102对Mpro和各种SARS-CoV-2变体表现出强大的活性.
  • H102诱导了Mpro催化二极管 (His41) 的结构变化,增强了抗阻力特征.
  • HP211206有效降解了耐药的Mpro突变体.
  • 计算方法提供了准确的结合性自由能量计算,并预测了易受阻力影响的地点.
  • D2Screen识别了基于类的抑制剂,具有针对Mpro E166V突变的抗药性活性.

结论:

  • 结合合成化学,结构生物学,计算建模和人工智能的多方方法在对抗病毒蛋白酶耐药性的有效.
  • 新型共价抑制剂和向蛋白解的奇默分子 (PROTAC) 为下一代抗病毒药物提供了有前途的途径.
  • 计算和人工智能工具对于加速设计强效和逃避抵抗的抗病毒剂至关重要.