Structure-Based Virtual Screening and Functional Validation of Potential Hit Molecules Targeting the SARS-CoV-2 Main Protease

The recent global health emergency caused by the coronavirus disease 2019 (COVID-19) pandemic has taken a heavy toll, both in terms of lives and economies. Vaccines against the disease have been developed, but the efficiency of vaccination campaigns worldwide has been variable due to challenges regarding production, logistics, distribution and vaccine hesitancy. Furthermore, vaccines are less effective against new variants of the SARS-CoV-2 virus and vaccination-induced immunity fades over time. These challenges and the vaccines' ineffectiveness for the infected population necessitate improved treatment options, including the inhibition of the SARS-CoV-2 main protease (M^pro). Drug repurposing to achieve inhibition could provide an immediate solution for disease management. Here, we used structure-based virtual screening (SBVS) to identify natural products (from NP-lib) and FDA-approved drugs (from e-Drug3D-lib and Drugs-lib) which bind to the M^pro active site with high-affinity and therefore could be designated as potential inhibitors. We prioritized nine candidate inhibitors (e-Drug3D-lib: Ciclesonide, Losartan and Telmisartan; Drugs-lib: Flezelastine, Hesperidin and Niceverine; NP-lib: three natural products) and predicted their half maximum inhibitory concentration using DeepPurpose, a deep learning tool for drug-target interactions. Finally, we experimentally validated Losartan and two of the natural products as in vitro M^pro inhibitors, using a bioluminescence resonance energy transfer (BRET)-based M^pro sensor. Our study suggests that existing drugs and natural products could be explored for the treatment of COVID-19.

BRET; COVID-19; FDA-approved drugs; SARS-CoV-2 main protease; deep learning; molecular docking; natural products; structure-based virtual screening.