Design of non-covalent dual-acting inhibitors for proteases MPRO and PLPRO of coronavirus SARS-CoV-2 through evolutionary library generation, pharmacophore profile matching, and molecular docking calculations

The proteases of the SARS-CoV-2 coronavirus are crucial for the virus's life cycle, making them a prime target for developing antiviral drugs to combat COVID-19. Currently, there is a priority to develop new antiviral drugs that can target multiple viral proteins at once. In this study, we analyze the molecular mechanisms of how non-covalent inhibitors interact with the main protease (Mpro) and papain-like (PLpro) protease of SARS-CoV-2 to create a computer modelling algorithm for discovering ligands that can inhibit both Mpro and PLpro simultaneously. Aim of the study. We aim to analyze the molecular structures involved in the interactions between current non-covalent inhibitors and the Mpro and PLpro proteases of SARS-CoV-2. The goal is to identify a common molecular structure that could be used to discover new inhibitors with a dual-acting mode using computer simulations. Materials and Methods. LigandScout 4.5 software was used for 3D-pharmacophore analysis, virtual screening and molecular docking. AutoDock Vina 1.1.2 tools was utilized for molecular docking. Web-servers PLIP (Protein-Ligand Interaction Profiler) and Pharmit were used for studying molecular binding mechanisms. Generation of evolutionary libraries was performed by DataWarrior 6.0. Analysis and visualization were performed by Discovery Studio 2024 Suite. Results. Our study analyzed various models of SARS-CoV-2 protease binding sites available in the Protein Data Bank (PDB) and their corresponding non-covalent inhibitor ligands. This analysis helped identify important features of the Mpro and PLpro ligands. By comparing the pharmacophore models of Mpro ligands with the structural features of PLpro inhibitors, we identified ligands that could potentially match the binding sites of both proteases. Using the structures of these ligands, an evolutionary library was created in the DataWarrior program. Virtual screening of this library using both Mpro and PLpro pharmacophores revealed several new hit molecules. Molecular docking of these molecules into the active sites of the Mpro and PLpro proteases and calculating their binding energetics led to the identification of several molecules and their corresponding scaffolds with dual inhibition potential. These findings can be further studied in vitro with the aim of discovering drugs for COVID-19. Conclusions. We used computer-based screening to search for ligands that could bind to both Mpro and PLpro proteases. After identifying these potential compounds, we developed synthesis methods to obtain them for further in vitro biological activity studies