通过机器学习辅助的自然化合物的识别,这些化合物针对具有潜在抗病毒活性的马尔堡病毒VP40
Ahmed M Hassan1, Leena H Bajrai1,2, Mai M El-Daly1,3
1Special Infectious Agents Unit - BSL3, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.
Journal of biomolecular structure & dynamics
|December 4, 2024
概括
这项研究使用in-silico方法选天然化合物与马尔堡病毒VP40蛋白. NPL130已成为抑制马尔堡病毒生长的有希望的候选药物.
科学领域:
- 计算化学和病毒学.
- 药物的发现和设计.
- 生物信息学和机器学习
背景情况:
- 马尔堡病毒感染是一种严重的人类健康威胁,死亡率高.
- 体内药物设计对于通过向必要的病毒蛋白来识别抗病毒剂至关重要.
- 马尔堡病毒矩阵蛋白VP40是抑制病毒复制的关键目标.
研究的目的:
- 选自然化合物的图书馆对马尔堡病毒的潜在抗病毒活性.
- 使用计算方法识别针对VP40矩阵蛋白的新型抑制剂.
- 通过分子动力学模拟来评估顶级化合物的结合稳定性和有效性.
主要方法:
- 使用分子对接,对2569种天然化合物进行了对抗马尔堡病毒VP40的选.
- 使用基于神经网络的DeepPurpose架构与摩根和CNN编码用于IC50预测.
- 进行了分子动力学模拟和MM/GBSA计算,以评估结合稳定性和能量.
主要成果:
- 确定了138种化合物,其分子对接结合得分为-8kcal/mol.
- 根据DeepPurpose模型预测,选择了前三种化合物:NPL130,NPL313和NPL371.
- NPL130证明了与VP40形成稳定的复合物,与对照化合物尼洛丁尼可比,具有有利的结合自由能.
结论:
- 建议NPL130作为一种潜在的打击化合物,用于向马尔堡病毒VP40.
- 这项研究突出了针对抗病毒药物发现的集成in-silico方法的有效性.
- 需要进一步的实验验证,以确认NPL130对马尔堡病毒的治疗潜力.
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