Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2-P3 α-ketoamide derivative and Pentagastrin: an in-silico drug discovery approach

Abstract

The SARS-CoV-2 main protease (Mpro) is an attractive target towards discovery of drugs to treat COVID-19 because of its key role in virus replication. The atomic structure of Mpro in complex with an α-ketoamide inhibitor (Lig13b) is available (PDB ID:6Y2G). Using 6Y2G and the prior knowledge that protease inhibitors could eradicate COVID-19, we designed a computational study aimed at identifying FDA-approved drugs that could interact with Mpro. We searched the DrugBank and PubChem for analogs and built a virtual library containing ~33000 conformers. Using high-throughput virtual screening and ligand docking, we identified Isavuconazonium, a ketoamide inhibitor (α-KI) and Pentagastrin as the top three molecules (Lig13b as the benchmark) based on docking energy. The ΔGbind of Lig13b, Isavuconazonium, α-KI, Pentagastrin was -117.68, -191.11, -187.36, -145.76 kJ/mol, respectively. Molecular dynamics simulation revealed that these ligands are stable within the Mpro active site. Binding of these ligands is driven by a variety of non-bonded interaction, including polar bonds, H-bonds, van der Waals and salt bridges. The overall conformational dynamics of the complexed-Mpro was slightly altered relative to apo-Mpro. This study demonstrates that three distinct classes molecules, Isavuconazonium (triazole), α-KI (ketoamide) and Pentagastrin (peptide) could serve as potential drugs to treat patients with COVID-19.

Benchmarking the Ability of Common Docking Programs to Correctly Reproduce and Score Binding Modes in SARS-CoV-2 Protease Mpro

“… 67 Current challenges for docking methods include protein flexibility, 68 ligand solvation, and binding-site hydration. 47 , 69 Thus far, several docking approaches have been employed to screen M pro for potential drugs in virtual screening and drug repurposing campaigns, including Glide, 7 , 10 , 13 , 17 , 18 , 70 − 72 Autodock, 11 , 13 , 73 , 74 Autodock Vina, 11 , 13 , 14 , 19 , 71 , 73 , 75 , 76 Surflex, 77 PLANT, 78 DockThor, 76 fast pulling of ligands, 14 deep docking, 70 algebraic topology and deep learning, 79 and virtual reality-based docking. 16 However, to the best of our knowledge, no rigorous benchmark study addressing the ability of such docking tools to reproduce and correctly rank known ligand binding modes has been published, in spite of the known inherent challenges in docking.…”

An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease

“…Such a QSAR model allows for the quantitative prediction of pharmacological activities of congeneric unknown compounds so that it can be used to direct the design of novel derivatives with enhanced activity (Totrov, 2008). While hundreds of compounds were screened by their binding affinity to the 3CLpro through automated molecular docking (Sirois et al, 2004;Achilonu et al, 2020), the resulting docking scores had a limited ability to accurately predict inhibitor efficacy (Kitchen et al, 2004). It is thus necessary to further implement the 3D QSAR model to evaluate physicochemical properties and potential inhibitory effectiveness of those compounds identified through the molecular docking.…”