Identification of Potential Inhibitors Against SARS-CoV-2 Using Computational Drug Repurposing Study

Title:Identification of Potential Inhibitors Against SARS-CoV-2 Using Computational Drug Repurposing Study

Volume: 16 Issue: 10

Author(s): Hasan Zulfiqar, Fu-Ying Dao, Hao Lv, Hui Yang, Peng Zhou, Wei Chen*Hao Lin*

Affiliation:

• Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Chengdu,China

• School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054,China

Keywords: Molecular docking, SARS-Cov-2, COVID-19, inhibitors, protein, drug.

Abstract:

Background: SARS-Cov-2 is a newly emerged coronavirus and causes a severe type of pneumonia in the host organism. So, it is an urgent need to find some inhibitors against SARS-Cov-2. Therefore, drug repurposing study is an effective strategy for treating pneumonia to find the inhibitors of SARS-Cov-2 proteins.

Methods: For this purpose, a library of 2500 verified drug chemical compounds was generated and the compounds were docked against Nucleocapsid, Membrane and Envelope protein structures of SARSCov- 2 to determine the binding affinity of the chemical compounds against targeting binding pockets. Moreover, cheminformatics properties and ADMET of these compounds were assessed to find the druglikeness behavior of compounds. The chemical compounds with the lowest S-score were identified as potential inhibitors.

Results: Our findings showed that the compound ids 1212, 1019 and 1992 could interact inside the active sites of membrane protein, nucleocapsid protein and envelope protein.

Conclusion: This in silico knowledge will be helpful for the design of novel, safe and less expensive drugs against the SARS-Cov-2.

Cite this article as:

Zulfiqar Hasan , Dao Fu-Ying , Lv Hao , Yang Hui , Zhou Peng, Chen Wei*, Lin Hao *, Identification of Potential Inhibitors Against SARS-CoV-2 Using Computational Drug Repurposing Study, Current Bioinformatics 2021; 16 (10) . https://dx.doi.org/10.2174/1574893616666210726155903