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通过对接、分子动力学和基于共识评分的虚拟筛选鉴定潜在的寨卡病毒 NS2B-NS3 蛋白酶抑制剂。

Identification of potential Zika virus NS2B-NS3 protease inhibitors via docking, molecular dynamics and consensus scoring-based virtual screening.

机构信息

California Polytechnic State University, 1 Grand Avenue, San Luis Obispo, CA, 93407, USA.

San Francisco State University, 1600 Holloway Avenue, San Francisco, CA, 94132, USA.

出版信息

J Mol Model. 2019 Jun 17;25(7):194. doi: 10.1007/s00894-019-4076-6.

Abstract

The Zika virus has recently become a subject of acute interest after the discovery of the link between viral infection and microcephaly in infants. Though a number of treatments are under active investigation, there are currently no approved treatments for the disease. To address this critical need, we screened more than 7 million compounds targeting the NS2B-NS3 protease in an attempt to identify promising inhibitor candidates. Starting with commercially and freely available compounds, we identified six hits utilizing an exhaustive consensus screening protocol, followed by molecular dynamics simulation and binding energy estimation using MM/GBSA and MM/PBSA methods. These compounds feature a variety of cores and functionalities, and all are predicted to have good pharmacokinetic profiles, making them promising candidates for screening assays. Graphical abstract Virtual screen of potential Zika virus NS2B-NS3 protease inhibitors.

摘要

寨卡病毒(Zika virus)在发现病毒感染与婴儿小头畸形之间的关联后,最近成为一个研究热点。尽管有许多治疗方法正在积极研究中,但目前尚无针对该疾病的批准治疗方法。为了满足这一迫切需求,我们筛选了超过 700 万个针对 NS2B-NS3 蛋白酶的化合物,试图确定有前途的抑制剂候选物。我们从商业上和免费获得的化合物开始,使用详尽的共识筛选方案确定了六个命中物,然后使用 MM/GBSA 和 MM/PBSA 方法进行分子动力学模拟和结合能估计。这些化合物具有多种核心和功能,并且都被预测具有良好的药代动力学特征,使它们成为筛选试验的有希望的候选物。

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