通过基于结构的虚拟查和多变量分析,以组合化学驱动的In silico设计和对共价型SARS-CoV-2主要蛋白酶抑制剂的计算评估
Alessia Bono1, Gabriele La Monica1, Federica Alamia1
1Dipartimento di Scienze e Technologie Biologiche Chimiche e Farmaceutiche "STEBICEF" - University of Palermo, Viale delle Scienze - Ed. 17, 90128, Palermo, Italy.
Journal of molecular graphics & modelling
|June 4, 2025
概括
新的计算方法确定了针对SARS-CoV-2主蛋白酶 (MPRO) 的强有力的共价抑制剂,这对病毒复制至关重要. 这些模仿化合物显示出作为新型抗病毒疗法对抗COVID-19的承诺.
科学领域:
- 计算化学和药物发现
- 病毒学和传染病学.
- 药品化学 药品化学 是一个
背景情况:
- 随着COVID-19的流行,除了疫苗之外,还需要新的药理疗法.
- SARS-CoV-2 主蛋白酶 (MPRO) 是一种关键的病毒酶,也是一个有吸引力的药物标.
- 对MPRO的催化氨酸 (Cys145) 的共价抑制是一种有前途的治疗策略.
研究的目的:
- 开发一种创新的in silico协议,用于识别SARS-CoV-2 MPRO的新型共价抑制剂.
- 设计和评估用于MPRO抑制的带有基核弹头的二模化合物.
主要方法:
- 创建一个由450个二模化合物组成的组合图书馆.
- 在体选包括对接研究和分子动力学模拟 (200 ns).
- 基于结构的技术与基于联体的工具 (生物标预测工具) 和统计分析的整合.
主要成果:
- 根据对接研究,将图书馆改进为388个化合物.
- 与已知的抑制剂相比,识别出具有优越结合亲和力的9-14,16,20类化合物.
- 通过分子动力学模拟验证计算设计策略.
结论:
- 在Silico开发的协议有效地识别了强大的共价MPRO抑制剂.
- 精选的模仿化合物显示出作为抗SARS-CoV-2药物的显著潜力.
- 这种方法为加速抗病毒药物发现提供了有价值的策略.
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