An attractive drug target to combat COVID‐19 is the main protease (Mpro) because of its key role in the viral life cycle by processing the polyproteins translated from the viral RNA. Studying the crystal structures of the protease is important to enhance our understanding of its mechanism of action at the atomic‐level resolution, and consequently may provide crucial structural insights for structure‐based drug discovery. In the current study, we report a comparative structural analysis of the Mpro substrate binding site for both apo and holo forms to identify key interacting residues and conserved water molecules during the ligand‐binding process. It is shown that in addition to the catalytic dyad residues (His41 and Cys145), the oxyanion hole residues (Asn142–Ser144) and residues His164–Glu166 form essential parts of the substrate‐binding pocket of the protease in the binding process. Furthermore, we address the issue of the substrate‐binding pocket flexibility and show that two adjacent loops in the Mpro structures (residues Thr45–Met49 and Arg188–Ala191) with high flexibility can regulate the binding cavity’ accessibility for different ligand sizes. Moreover, we discuss in detail the various structural and functional roles of several important conserved and mobile water molecules within and around the binding site in the proper enzymatic function of Mpro. We also present a new docking protocol in the framework of the ensemble docking strategy. The performance of the docking protocol has been evaluated in predicting ligand binding pose and affinity ranking for two popular docking programs; AutoDock4 and AutoDock Vina. Our docking results suggest that the top‐ranked poses of the most populated clusters obtained by AutoDock Vina are the most important representative docking runs that show a very good performance in estimating experimental binding poses and affinity ranking.

In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6‐D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin‐dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry‐corrected heavy‐atom RMSD and the fraction of recovered ligand‐protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.

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