Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2

Abel, Renata; Paredes Ramos, María; Chen, Qiaofeng; Pérez-Sánchez, Horacio; Coluzzi, Flaminia; Rocco, Monica; Marchetti, Paolo; Mura, Cameron; Simmaco, Maurizio; Bourne, Philip E.; Preissner, Robert; Banerjee, Priyanka

The rapidly developing pandemic, known as coronavirus disease 2019 (COVID-19) and caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently spread across 213 countries and territories. This pandemic is a dire public health threat—particularly for those suffering from hypertension, cardiovascular diseases, pulmonary diseases, or diabetes; without approved treatments, it is likely to persist or recur. To facilitate the rapid discovery of inhibitors with clinical potential, we have applied ligand- and structure-based computational approaches to develop a virtual screening methodology that allows us to predict potential inhibitors. In this work, virtual screening was performed against two natural products databases, Super Natural II and Traditional Chinese Medicine. Additionally, we have used an integrated drug repurposing approach to computationally identify potential inhibitors of the main protease of SARS-CoV-2 in databases of drugs (both approved and withdrawn). Roughly 360,000 compounds were screened using various molecular fingerprints and molecular docking methods; of these, 80 docked compounds were evaluated in detail, and the 12 best hits from four datasets were further inspected via molecular dynamics simulations. Finally, toxicity and cytochrome inhibition profiles were computationally analyzed for the selected candidate compounds.