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Published on: July 29, 2021
Molecular Insights into Structural Dynamics and Binding Interactions of Selected Inhibitors Targeting SARS-CoV-2 Main
Yuanyuan Wang1, Yulin Zhou1, Faez Iqbal Khan1
1Department of Biosciences and Bioinformatics, School of Science, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou 215123, China.
Abstract:
The SARS-CoV-2 main protease (Mpro, also known as 3CLpro) is a key target for antiviral therapy due to its critical role in viral replication and maturation. This study investigated the inhibitory effects of Bofutrelvir, Nirmatrelvir, and Selinexor on 3CLpro through molecular docking, molecular dynamics (MD) simulations, and free energy calculations. Nirmatrelvir exhibited the strongest binding affinity across docking tools (AutoDock Vina: -8.3 kcal/mol; DiffDock: -7.75 kcal/mol; DynamicBound: 7.59 to 7.89 kcal/mol), outperforming Selinexor and Bofutrelvir. Triplicate 300 ns MD simulations revealed that the Nirmatrelvir-3CLpro complex displayed high conformational stability, reduced root mean square deviation (RMSD), and a modest decrease in solvent-accessible surface area (SASA), indicating enhanced structural rigidity. Gibbs free energy analysis highlighted greater flexibility in unbound 3CLpro, stabilized by Nirmatrelvir binding, supported by stable hydrogen bonds. MolProphet prediction tools, targeting the Cys145 residue, confirmed that Nirmatrelvir exhibited the strongest binding, forming multiple hydrophobic, hydrogen, and π-stacking interactions with key residues, and had the lowest predicted IC50/EC50 (9.18 × 10-8 mol/L), indicating its superior potency. Bofutrelvir and Selinexor showed weaker interactions and higher IC50/EC50 values. MM/PBSA analysis calculated a binding free energy of -100.664 ± 0.691 kJ/mol for the Nirmatrelvir-3CLpro complex, further supporting its stability and binding potency. These results underscore Nirmatrelvir's potential as a promising therapeutic agent for SARS-CoV-2 and provide novel insights into dynamic stabilizing interactions through AI-based docking and long-term MD simulations.
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