Computational Assessment of Clinical Drugs against SARS-CoV-2: Foreseeing Molecular Mechanisms and Potent Mpro Inhibitors.

Abstract

The emergence of new SARS-CoV-2 variants of concern (VOC) is a propulsion for accelerated potential therapeutic discovery. SARS-CoV-2's main protease (Mpro), essential for host cell viral replication, is a pre-eminent druggable protein target. Here, we perform extensive drug re-profiling of the comprehensive Excelra database, which compiles various under-trial drug candidates for COVID-19 treatment. For mechanistic understanding, the most promising screened-out molecules with targets are subjected to molecular dynamics (MD) simulations. Post-MD analyses demonstrate Darunavir, Ponatinib, and Tomivosertib forming a stable complex with Mpro, characterized by less fluctuation of Cα atoms, smooth and stable root-mean-square deviation (RMSD), and robust contact with the active site residues. Likewise, they all have lower binding free energy with Mpro, demonstrating strong affinity. In free energy landscape profiles, the distances from His41 and Cys145 exhibit a single energy minima basin, implying their preponderance in proximity to Mpro's catalytic dyad. Overall, the computational assessment earmarks promising candidates from the Excelra database, emphasizing on carrying out exhaustive biochemical experiments along with clinical trials. The work lays the foundation for potential therapeutic interventions in treating COVID-19.