Altayb Hisham N, Alatawi Hanan Ali
Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Department of Biological Sciences, University Collage of Haqel, University of Tabuk, Tabuk 71491, Saudi Arabia.
Pharmaceuticals (Basel). 2024 Aug 15;17(8):1067. doi: 10.3390/ph17081067.
Zika virus infection is a mosquito-borne viral disease that has become a global health concern recently. Zika virus belongs to the Flavivirus genus and is primarily transmitted by Aedes mosquitoes. Prevention of Zika virus infection involves avoiding mosquito bites by using repellent, wearing protective clothing, and staying in screened areas, especially for pregnant women. Treatment focuses on managing symptoms with rest, fluids, and acetaminophen, with close monitoring for pregnant women. Currently, there is no specific antiviral treatment or vaccine for the Zika virus, highlighting the importance of prevention strategies to control its spread. Therefore, in this study, the Zika virus non-structural protein NS3 was targeted to inhibit Zika infection by identifying the novel inhibitor through an in silico approach. Here, 2864 natural compounds were screened using a machine learning-based QSAR model, and later docking was performed to select the potential target. Subsequently, Tanimoto similarity and clustering were performed to obtain the potential target. The three most potential compounds were obtained: (a) 5297, (b) 432449, and (c) 85137543. The protein-ligand complex's stability and flexibility were then investigated by dynamic modelling. The 300 ns simulation showed that 5297 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 5297 demonstrated a superior binding free energy (ΔG = -20.81 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 5297 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the NS2B-NS3 protease target implicated in Zika virus infection.
寨卡病毒感染是一种由蚊子传播的病毒性疾病，最近已成为全球健康关注的焦点。寨卡病毒属于黄病毒属，主要由伊蚊传播。预防寨卡病毒感染包括使用驱虫剂、穿着防护服以及待在有纱窗的区域来避免蚊虫叮咬，尤其是孕妇。治疗主要是通过休息、补充水分和使用对乙酰氨基酚来缓解症状，并对孕妇进行密切监测。目前，针对寨卡病毒尚无特效抗病毒治疗方法或疫苗，这凸显了预防策略对于控制其传播的重要性。因此，在本研究中，通过计算机模拟方法鉴定新型抑制剂，以寨卡病毒非结构蛋白NS3为靶点来抑制寨卡病毒感染。在此，使用基于机器学习的定量构效关系（QSAR）模型筛选了2864种天然化合物，随后进行对接以选择潜在靶点。接着进行了Tanimoto相似性分析和聚类以获得潜在靶点。得到了三种最具潜力的化合物：（a）5297、（b）432449和（c）85137543。然后通过动力学建模研究了蛋白质-配体复合物的稳定性和灵活性。300纳秒的模拟显示，5297表现出最稳定的偏差和持续形成的氢键。当使用MM/GBSA技术时，与其他化合物相比，5297与该蛋白质表现出更高的结合自由能（ΔG = -20.81千卡/摩尔）。该研究确定5297显示出显著的治疗潜力，有理由作为寨卡病毒感染中涉及的NS2B-NS3蛋白酶靶点的可能抑制剂进行进一步的实验研究。
