Computational discovery of small drug-like compounds as potential inhibitors of SARS-CoV-2 main protease

Andrianov, Alexander M.; Kornoushenko, Yuri V.; Karpenko, Anna D.; Bosko, Ivan P.; Tuzikov, Alexander V.

A computational approach to in silico drug discovery was carried out to identify small drug-like compounds able to show structural and functional mimicry of the high affinity ligand X77, potent non-covalent inhibitor of SARS-COV-2 main protease (M(Pro)). In doing so, the X77-mimetic candidates were predicted based on the crystal X77-M(Pro) structure by a public web-oriented virtual screening platform Pharmit. Models of these candidates bound to SARS-COV-2 M(Pro) were generated by molecular docking, quantum chemical calculations and molecular dynamics simulations. At the final point, analysis of the interaction modes of the identified compounds with M(Pro) and prediction of their binding affinity were carried out. Calculation revealed 5 top-ranking compounds that exhibited a high affinity to the active site of SARS-CoV-2 M(Pro). Insights into the ligand − M(Pro) models indicate that all identified compounds may effectively block the binding pocket of SARS-CoV-2 M(Pro), in line with the low values ​​of binding free energy and dissociation constant. Mechanism of binding of these compounds to M(Pro) is mainly provided by van der Waals interactions with the functionally important residues of the enzyme, such as His-41, Met-49, Cys-145, Met-165, and Gln-189 that play a role of the binding hot spots assisting the predicted molecules to effectively interact with the M(Pro) active site. The data obtained show that the identified X77-mimetic candidates may serve as good scaffolds for the design of novel antiviral agents able to target the active site of SARS-CoV-2 M(Pro). Communicated by Ramaswamy H. Sarma