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Front. Mol. Biosci. | doi: 10.3389/fmolb.2021.661424

An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease Provisionally accepted The final, formatted version of the article will be published soon. Notify me

  • 1Villanova University, United States
  • 2University College London, United Kingdom

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, while the SARS-CoV2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue towards the therapeutic intervention of the viral replication. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. 288 potential 3CLpro inhibitors were identified from a half-million bioactive chemicals via a protein-ligand screening protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS-CoV-1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS-CoV-2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1'-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50’s of 19 ± 3 um and 38 ± 3 um, respectively. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro.

Keywords: SARS-CoV-2, 3CL protease inhibitors, Docking, QSAR, IC50

Received: 30 Jan 2021; Accepted: 29 Mar 2021.

Copyright: © 2021 Zhai, Zhang, Haider, Kraut and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Zuyi (Jacky) Huang, Villanova University, Villanova, 19085, Pennsylvania, United States,