Discovery of Natural Compounds as SARS-CoV-2’s Main Protease Inhibitors by Docking-based Virtual Screening
Keywords:
SARS-CoV-2, main protease, molecular docking, virtual screening, natural compounds, COVID-19Abstract
Introduction: The novel coronavirus disease (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2). The development of antiviral drugs has enhaced treatment of COVID-19. SARS-CoV-2 main protease (Mpro) is a key enzyme responsible for viral replica-tion and transcription. This study aimed to identify new natural structures for the design of SARS-CoV-2 Mpro inhibitors.
Methods: In this present work, The CDOCKER protocol and scoring functions were validated. The vali-dated docking-based virtual screening approach was then employed to search the in-house database of natural compounds for potential lead compounds as SARS-CoV-2 Mpro inhibitors. The top 3 compounds were further biologically evaluated in vitro.
Results and Discussion: Docking studies of the known ligand GC-376 led to results consistent with co-crystallized data (PDB ID: 7D1M). Additionally, the effectiveness of docking scoring functions was vali-dated by using the training set consisting of 15 active compounds and 15 inactive compounds. Then, the in-house database of natural compounds (overall 34,439 natural compounds) was subjected to docking-based virtual screening resulting in the identification of the top 100 compounds having relatively better docking scores. Among them, the highest ranking 3 compounds (W-1, W-2, and W-3) were biologically evaluated in vitro for their inhibitory activity against SARS-CoV-2 Mpro, and compound W-1 was identi-fied as the most potent SARS-CoV-2 Mpro inhibitor with an IC50 value of 63 ± 3 μM. Interestingly, it ap-peared that the in vitro activities of compounds W-1, W-2, and W-3 were in agreement with their molecu-lar modeling data.
Conclusion: Our results provided a useful reference for the discovery of novel natural SARS-CoV-2 Mpro inhibitors by virtual screening.
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Copyright (c) 2024 Jing Wang, Yu Jiang, Yingnan Wu, Yuheng Ma, Hui Yu, Zhanli Wang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.