结合SARS-CoV-2PLpro的候选药物的亲属性使用通用整体方法进行评估
Masashi Muramoto1, Simon Hikiri2, Suzuka Saito1
1Graduate School of Life Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan.
Scientific reports
|January 11, 2026
概括
我们开发了一种新的计算方法,即多维虚拟系统合分子动力学 (mD-VcMD),用于选抑制蛋白质结合的化合物. 这种方法准确地预测了化合物对SARS-CoV-2帕帕因类蛋白酶 (PLpro) 的有效性.
科学领域:
- 计算化学是一种计算化学.
- 分子动力学分子动力学
- 药物发现 药物发现
背景情况:
- 联体受体结合对于生物过程和药物开发至关重要.
- 需要精确的选方法来确定有效的抑制剂.
研究的目的:
- 建立和验证一种新的计算程序,用于选抑制联体受体结合的化合物.
- 评估该方法在识别SARS-CoV-2帕帕因类蛋白酶 (PLpro) 抑制剂方面的有效性.
主要方法:
- 采用了多维虚拟系统合分子动力学 (mD-VcMD),一种通用集体方法.
- 在明确溶剂中模拟灵活的受体和化合物,以产生自由能量景观.
- 分析了四种SARS-CoV-2 PLpro 抑制剂的自由能量盆和结合模式.
主要成果:
- 对于所有测试的化合物,mD-VcMD方法产生了类似漏斗的自由能量景观.
- 自由能量盆地的概率与测量的解离常数相对应得很好.
- 结构聚类揭示了与酶抑制活性相关的结合模式.
结论:
- 拟议的mD-VcMD程序是有效的选化合物和选择潜在的抑制剂.
- 这种方法提供了一种可靠的方法来识别针对SARS-CoV-2PLpro.pro等病毒蛋白酶的候选药物.
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