In-silico approaches for identification of compounds inhibiting SARS-CoV-2 3CL protease.

The world has witnessed of many pandemic waves of SARS-CoV-2. However, the incidence of SARS-CoV-2 infection has now declined but the novel variant and responsible cases has been observed globally. Most of the world population has received the vaccinations, but the immune response against COVID-19 is not long-lasting, which may cause new outbreaks. A highly efficient pharmaceutical molecule is desperately needed in these circumstances. In the present study, a potent natural compound that could inhibit the 3CL protease protein of SARS-CoV-2 was found with computationally intensive search. This research approach is based on physics-based principles and a machine-learning approach. Deep learning design was applied to the library of natural compounds to rank the potential candidates. This procedure screened 32,484 compounds, and the top five hits based on estimated pIC50 were selected for molecular docking and modeling. This work identified two hit compounds, CMP4 and CMP2, which exhibited strong interaction with the 3CL protease using molecular docking and simulation. These two compounds demonstrated potential interaction with the catalytic residues His41 and Cys154 of the 3CL protease. Their calculated binding free energies to MMGBSA were compared to those of the native 3CL protease inhibitor. Using steered molecular dynamics, the dissociation strength of these complexes was sequentially determined. In conclusion, CMP4 demonstrated strong comparative performance with native inhibitors and was identified as a promising hit candidate. This compound can be applied in-vitro experiment for the validation of its inhibitory activity. Additionally, these methods can be used to identify new binding sites on the enzyme and to design new compounds that target these sites.