Computational study of interaction of calixarene with ebola virus structural proteins and its potential therapeutic implications

IF 3 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Journal of molecular graphics & modelling Pub Date : 2025-05-01 Epub Date: 2025-02-12 DOI:10.1016/j.jmgm.2025.108976
Qazi Mohammad Sajid Jamal , Mohammad Azam Ansari , Ali H. Alharbi , Mohammad N. Alomary , Yahya F. Jamous , Tanmoy Dutta , Atanu Maity , Varish Ahmad
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Abstract

Ebola virus (EBOV) is a negative-strand RNA virus that causes hemorrhagic fever and fatal illness in humans. According to WHO, the Ebola virus caused 28,646 fatal cases and 11,323 deaths in West Africa due to hemorrhagic fever and deadly disease in humans between 2013 and 2016. Between 1976 and 2022, approximately 15,409 fatalities caused by EBOV took place worldwide. Unfortunately, no effective vaccine or drugs are available to prevent this deadly disease. In the present study, State-of-the-art tools based on in-silico methods were used to elucidate the interaction pattern of calixarene (CAL) with seven EBOV structural proteins, i.e., GP1,2, nucleoprotein (NP), polymerase cofactor (VP35), (VP40), transcription activator (VP30), VP24, and RNA-dependent RNA polymerase (L). CAL is a cage-like compound with supramolecular features. The molecular docking lead analysis using AutoDock tool has been performed to find out the binding pattern of CAL with EBOV proteins. Obtained results revealed efficient inhibitory properties of calixarene (CAL) against seven Ebola virus structural proteins i.e., GP1,2, nucleoprotein (NP), polymerase cofactor (VP35), (VP40), transcription activator (VP30), VP24, and RNA-dependent RNA polymerase (L). Molecular docking analysis shows that the interaction of CAL with VP24 was highest with the total binding energy −12.47 kcal/mol and 26.90 nM inhibitions constant. Molecular Dynamics study has also quantified the efficiency of CAL against VP24. In conclusion, the present study suggests that CAL and its derivatives could be used as inhibitors to counter EBOV infection. Furthermore, in vitro and in vivo laboratory experimentation is required to establish CAL and its derivatives as a potential inhibitor against EBOV.

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杯芳烃与埃博拉病毒结构蛋白相互作用的计算研究及其潜在的治疗意义
埃博拉病毒(EBOV)是一种负链RNA病毒,可引起人类出血热和致命疾病。根据世卫组织的数据,2013年至2016年期间,埃博拉病毒在西非造成28646例死亡病例和11323例死亡,原因是出血热和致命的人类疾病。1976年至2022年间,全世界约有15409人死于EBOV。不幸的是,目前还没有有效的疫苗或药物来预防这种致命疾病。在本研究中,基于计算机方法的最新工具阐明了杯芳烃(CAL)与7种EBOV结构蛋白,即GP1,2,核蛋白(NP),聚合酶辅助因子(VP35), (VP40),转录激活因子(VP30), VP24和RNA依赖性RNA聚合酶(L)的相互作用模式。CAL是一种具有超分子特征的笼状化合物。利用AutoDock工具进行分子对接引线分析,找出CAL与EBOV蛋白的结合模式。结果表明,萼芳烃(CAL)对埃博拉病毒GP1、2、核蛋白(NP)、聚合酶辅助因子(VP35)、(VP40)、转录激活因子(VP30)、VP24和RNA依赖性RNA聚合酶(L)等7种结构蛋白均有良好的抑制作用,分子对接分析表明,CAL与VP24的相互作用最高,总结合能为- 12.47 kcal/mol,抑制常数为26.90 nM。分子动力学研究也量化了CAL对VP24的抑制效率。综上所述,本研究提示CAL及其衍生物可作为EBOV感染的抑制剂。此外,还需要体外和体内实验室实验来确定CAL及其衍生物作为EBOV的潜在抑制剂。
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来源期刊
Journal of molecular graphics & modelling
Journal of molecular graphics & modelling 生物-计算机:跨学科应用
CiteScore
5.50
自引率
6.90%
发文量
216
审稿时长
35 days
期刊介绍: The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.
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