支持向量机识别小分子结合剂到SARS-CoV-2 Mpro下一代基于PROTAC的治疗方法的未研究的Allosteric站点
Enrico Mario Alessandro Fassi1, Nedra Mekni2, Marco Albani1
1Department of Pharmaceutical Sciences, Università degli Studi di Milano, Milano, Italy.
Archiv der Pharmazie
|December 13, 2025
概括
机器学习发现了潜在的SARS-CoV-2 Mpro 抑制剂. 生物物理和计算研究揭示了非竞争性结合,建议新的抗病毒策略,如用于SARS-CoV-2药物开发的PROTACs.
科学领域:
- 病毒学 病毒学
- 药物发现 药物发现 药物发现
- 计算化学计算化学
背景情况:
- 严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 需要新的抗病毒疗法.
- SARS-CoV-2 主蛋白酶 (Mpro) 是抑制病毒复制的关键目标.
研究的目的:
- 为了实验验证机器学习识别的SARS-CoV-2 Mpro抑制剂.
- 研究潜在的Mpro向化合物的结合和抑制机制.
主要方法:
- 基于机器学习的180种化合物的虚拟选.
- 微尺度热泳 (MST) 用于结合亲和度评估.
- 酶分析,对接和分子动力学 (MD) 模拟以阐明机制.
主要成果:
- 确定了三个有前途的化合物,其中一个显示高亲和度结合 (Kd = 2.8 ± 0.9 μM) 到SARS-CoV-2 Mpro.
- 酶测定表明,被击中化合物没有抑制Mpro活性,这表明非竞争性结合.
- 计算模拟确定了化合物的辅助结合点和稳定的相互作用.
结论:
- 已识别的化合物与SARS-CoV-2 Mpro上的辅助部位结合,而不是活性部位.
- 这些化合物可以作为发展新降解向策略的支架,例如PROTACs.
- 将ML与生物物理和计算方法相结合,可以加速抗病毒药物发现.
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