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DOI 10.1093/bib/bbaa209

Медицинская библиотека · Briefings in bioinformatics · 2021

Potential inhibitors for the novel coronavirus (SARS-CoV-2).

Han Yanqiang, Wang Zhilong, Ren Jiahao, Wei Zhiyun, Li Jinjin

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

The lack of a vaccine or any effective treatment for the aggressive novel coronavirus disease (COVID-19) has created a sense of urgency for the discovery of effective drugs. Several repurposing pharmaceutical candidates have been reported or envisaged to inhibit the emerging infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but their binding sites, binding affinities and inhibitory mechanisms are still unavailable. In this study, we use the ligand-protein docking program and molecular dynamic simulation to ab initio investigate the binding mechanism and inhibitory ability of seven clinically approved drugs (Chloroquine, Hydroxychloroquine, Remdesivir, Ritonavir, Beclabuvir, Indinavir and Favipiravir) and a recently designed α-ketoamide inhibitor (13b) at the molecular level. The results suggest that Chloroquine has the strongest binding affinity with 3CL hydrolase (Mpro) among clinically approved drugs, indicating its effective inhibitory ability for SARS-CoV-2. However, the newly designed inhibitor 13b shows potentially improved inhibition efficiency with larger binding energy compared with Chloroquine. We further calculate the important binding site residues at the active site and demonstrate that the MET 165 and HIE 163 contribute the most for 13b, while the MET 165 and GLN 189 for Chloroquine, based on residual energy decomposition analysis. The proposed work offers a higher research priority for 13b to treat the infection of SARS-CoV-2 and provides theoretical basis for further design of effective drug molecules with stronger inhibition.

Для цитирования: Han Yanqiang, Wang Zhilong, Ren Jiahao, Wei Zhiyun, Li Jinjin. Potential inhibitors for the novel coronavirus (SARS-CoV-2).. Briefings in bioinformatics. 2021. DOI: 10.1093/bib/bbaa209.

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