Identification of potential antiviral lead inhibitors against SARS-CoV-2 main protease: Structure-guided virtual screening, docking, ADME, and MD Simulation based approach - Goverdhan Lanka; Revanth Bathula; Balaram Ghosh; Sarita Rajender Potlapally (Artificial Intelligence Chemistry, 2023)
The novel coronavirus disease (COVID-19) was caused by a new strain of the virus SARS-CoV-2 in December 2019 emerged as deadly pandemic that affected millions of people worldwide. Factors such as lack of effective drugs, vaccine resistance, gene mutations, and cost of repurposed drugs demand new potential inhibitors. The main protease (Mpro) of SARS-CoV-2 has a key role in viral replication and transcription and is considered as drug target for new lead identification. In this present work, structure-based virtual screening, docking, MM/GBSA, AutoDock, ADME, and MD simulations-based optimization was proposed for the identification of new potential inhibitors against Mpro of SARS-CoV-2. The ligand molecules M1, M3, and M6 were identified as potential leads from lead optimization. Induced fit docking was performed for the identification of the best poses of lead molecules. The best docked poses of potential leads M1 and M3 were subject to 100 ns MD simulations for the evaluation of stability and interaction analysis into Mpro active site. The structures of the top two leads M1 and M3 were optimized based on MD simulation conformational changes and isoster scanning, designed as new leads M7 and M8. The MD simulation trajectories RMSD, RMSF, protein-ligand, ligand-protein interaction plots, and ligand torsion profiles were analyzed for stability interpretation. The docked complexes of M7 and M8 of Mpro exhibited equilibrated and converged plots in 100 ns simulation. The lead molecules M1, M3, M7, and M8 were identified as potential SARS-CoV-2 inhibitors for COVID-19 disease. A comparative docking study was carried out using FDA-approved drugs to support the potential binding affinities of newly identified lead inhibitors.