Identification of novel SARS-CoV-2 RNA dependent RNA polymerase (RdRp) inhibitors: From in silico screening to experimentally validated inhibitory activity

被引:10
|
作者
Uengwetwanit, Tanaporn [1 ]
Chutiwitoonchai, Nopporn [1 ]
Wichapong, Kanin [2 ]
Karoonuthaisiri, Nitsara [1 ,3 ]
机构
[1] Natl Sci & Technol Dev Agcy, Natl Ctr Genet Engn & Biotechnol BIOTEC, Pathum Thani 12120, Thailand
[2] Maastricht Univ, Cardiovasc Res Inst Maastricht CARIM, Dept Biochem, NL-6200 MD Maastricht, Netherlands
[3] Queens Univ, Inst Global Food Secur, Biol Sci Bldg,19 Chlorine Gardens, Belfast BT9 5DL, Antrim, North Ireland
关键词
SARS-CoV-2; Coronavirus; Inhibitor; Molecular docking; Virtual screening; Drug discovery; CORONAVIRUS; SARS;
D O I
10.1016/j.csbj.2022.02.001
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has posed a serious threat to global health and the economy for over two years, prompting the need for development of antiviral inhibitors. Due to its vital role in viral replication, RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. Herein, we analyzed amino acid sequence conservation of RdRp across coronaviruses. The conserved amino acids at the catalytic binding site served as the ligand-contacting residues for in silico screening to elucidate possible resistant mutation. Molecular docking was employed to screen inhibitors of SARS-CoV-2 from the ZINC ChemDiv database. The top-ranked compounds selected from GOLD docking were further investigated for binding modes at the conserved residues of RdRp, and ten compounds were selected for experimental validation. Of which, three compounds exhibited promising antiviral activity. The most promising candidate showed a half-maximal effective concentration (EC50) of 5.04 mu M. Molecular dynamics simulations, binding free-energy calculation and hydrogen bond analysis were performed to elucidate the critical interactions providing a foundation for developing lead compounds effective against SARS-CoV-2. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
引用
收藏
页码:882 / 890
页数:9
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