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表儿茶素类似物可能通过抑制血凝素神经氨酸酶蛋白和阻止细胞进入来阻碍人类副流感病毒感染。

Epicatechin analogues may hinder human parainfluenza virus infection by inhibition of hemagglutinin neuraminidase protein and prevention of cellular entry.

机构信息

Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Rd, New Delhi, 110042, India.

出版信息

J Mol Model. 2022 Sep 16;28(10):319. doi: 10.1007/s00894-022-05310-9.

DOI:10.1007/s00894-022-05310-9
PMID:36109366
Abstract

Human parainfluenza viruses (HPIVs) are ( -)ssRNA viruses belonging to Paramyoviridaie family. They are one of the leading causes of mortality in infants and young children and can cause ailments like croup, bronchitis, and pneumonia. Currently, no antiviral medications or vaccines are available to effectively treat parainfluenza. This necessitates the search for a novel and effective treatment. Computer-aided drug design (CADD) methodology can be utilized to discover target-based inhibitors with high accuracy in less time. A library of 45 phytocompounds with immunomodulatory properties was prepared. Thereafter, molecular docking studies were conducted to characterize the binding behavior of ligand in the binding pocket of HPIV3 HN protein. The physicochemical properties for screened compounds were computed, and the top hits from docking studies were further analyzed and validated using molecular dynamics simulation studies using the Desmond module of Schrodinger Suite 2021-1, followed by MM/GBSA analysis. The compounds CID:72276 (1) and CID:107905 (2) emerged as lead compounds of our in silico investigation. Further in vitro studies will be required to prove the efficacy of lead compounds as inhibitors and to determine the exact mechanism of their inhibition. Computational studies predict three natural flavonoids to inhibit the HN protein of HPIV3.

摘要

人副流感病毒(HPIVs)属于副黏液病毒科的(-)ssRNA 病毒。它们是导致婴儿和幼儿死亡的主要原因之一,可引起诸如哮吼、支气管炎和肺炎等疾病。目前,尚无有效的抗病毒药物或疫苗可有效治疗副流感。因此,需要寻找新型有效的治疗方法。计算机辅助药物设计(CADD)方法可以用于在更短的时间内以高精度发现基于靶标的抑制剂。我们制备了具有免疫调节特性的 45 种植物化合物库。然后,进行分子对接研究以表征配体在 HPIV3 HN 蛋白结合口袋中的结合行为。计算了筛选化合物的物理化学性质,并使用 Schrödinger Suite 2021-1 中的 Desmond 模块对来自对接研究的顶级命中物进行进一步分析和验证,然后进行 MM/GBSA 分析。化合物 CID:72276(1)和 CID:107905(2)成为我们计算机研究的先导化合物。需要进一步的体外研究来证明先导化合物作为抑制剂的功效,并确定其抑制的确切机制。计算研究预测三种天然类黄酮可抑制 HPIV3 的 HN 蛋白。

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