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针对基孔肯雅病毒nsP3宏结构域的计算机虚拟筛选命中物的发现。

Discovery of in silico hits targeting the nsP3 macro domain of chikungunya virus.

作者信息

Nguyen Phuong T V, Yu Haibo, Keller Paul A

机构信息

School of Chemistry, University of Wollongong, Wollongong, 2522, Australia.

出版信息

J Mol Model. 2014 May;20(5):2216. doi: 10.1007/s00894-014-2216-6. Epub 2014 Apr 23.

Abstract

The recent emergence and re-emergence of alphaviruses, in particular the chikungunya virus (CHIKV), in numerous countries has invoked a worldwide threat to human health, while simultaneously generating an economic burden on affected countries. There are currently no vaccines or effective drugs available for the treatment of the CHIKV, and with few lead compounds reported, the vital medicinal chemistry is significantly more challenging. This study reports on the discovery of potential inhibitors for the nsP3 macro domain of CHIKV using molecular docking, virtual screening, and molecular dynamics simulations, as well as work done to evaluate and confirm the active site of nsP3. Virtual screening was carried out based on blind docking as well as focused docking, using the database of 1541 compounds from NCI Diversity Set II, to identify hit compounds for nsP3. The top hit compounds were further subjected to molecular dynamic simulations, yielding a greater understanding of the dynamic behavior of nsP3 and its complexes with various ligands, concurrently confirming the outcomes of docking, and establishing in silico lead compounds which target the CHIKV nsP3 enzyme.

摘要

近期,甲病毒,尤其是基孔肯雅病毒(CHIKV)在许多国家出现和再次出现,对人类健康构成了全球威胁,同时给受影响国家带来了经济负担。目前尚无治疗CHIKV的疫苗或有效药物,且报道的先导化合物很少,重要的药物化学研究面临更大挑战。本研究报告了利用分子对接、虚拟筛选和分子动力学模拟发现CHIKV nsP3宏结构域潜在抑制剂的过程,以及评估和确认nsP3活性位点所做的工作。基于盲对接和聚焦对接,使用美国国立癌症研究所多样性集II的1541种化合物数据库进行虚拟筛选,以识别nsP3的命中化合物。对排名靠前的命中化合物进一步进行分子动力学模拟,更深入地了解nsP3及其与各种配体复合物的动态行为,同时确认对接结果,并确定靶向CHIKV nsP3酶的计算机辅助先导化合物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/939b/7088235/2a27f74c0ea0/894_2014_2216_Figa_HTML.jpg

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