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机器学习,网络药理学和分子动力学揭示了针对登革热病毒蛋白质的强有力的环类抑制剂

Mohammed A Imam^1,2, Thamir A Alandijany^2,3, Hashim R Felemban^2,3

¹Department of Medical Microbiology and Parasitology, Qunfudah Faculty of Medicine, Umm Al-Qura University, Al-Qunfudah, 21961, Saudi Arabia.

Molecular diversity

|September 3, 2024

概括

这项研究使用机器学习和分子动力学确定了登革热病毒蛋白质的强有力的环类抑制剂. NS5-CP03714复合体表现出卓越的稳定性和结合性,表明潜在的广泛抗病毒活性.

科学领域:

病毒学病毒学
计算化学计算化学
药物发现药物发现药物发现

背景情况:

登革热病毒对全球健康构成重大威胁,每年造成数百万例感染.
迫切需要有效的针对关键病毒蛋白的抗病毒疗法.

研究的目的:

鉴定和描述新型环类抑制剂对登革热病毒基本蛋白质 (E,NS1,NS3,NS5) 的特性.
通过计算方法评估已识别的抑制剂的结合亲和力,稳定性和潜在的宽谱活性.

主要方法:

使用基于机器学习的定量结构-活性关系 (QSAR) 模型,对5579个环酸对四种登革热病毒标蛋白进行查.
排名最高的环蛋白与它们各自的标蛋白进行分子对接.
100-ns分子动力学 (MD) 模拟以评估蛋白质-复合物的稳定性和结合相互作用.
分析MD模拟数据,包括根平均平方偏差 (RMSD),根平均平方偏差 (RMSF),主要组件分析 (PCA) 和自由能源景观.
针对特定的药物向网络分析.

主要成果:

与NS3-CP02054复合体相比,NS5-CP03714复合体显示出明显更高的结构稳定性和更强的结合相互作用.
MD模拟证实了NS5-CP03714的优越形状稳定性,其总结合能为-47.19 kcal/mol.
分析显示,酸CP02950和CP05582的潜在广谱活性针对多种病毒蛋白.

结论:

已识别的环,特别是NS5-CP03714,显示出作为登革热病毒蛋白质的潜在抑制剂的潜力.
计算结果表明,这些可以与病毒标形成稳定的复合体,这需要进一步的实验验证.
这项研究强调了计算方法在发现新型抗病毒药物对抗登革热病毒方面的潜力.

关键词:

循环类是一种循环类.登革热病毒感染者机器学习是机器学习.分子对接是分子对接.分子动力学模拟模型网络药理学网络药理学在QSAR中使用QSAR.

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