The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is not showing any sign of slowing down even after the ongoing efforts of vaccination. The threats of new strains are concerning, as some of them are more infectious than the original one. A therapeutic against the disease is, therefore, of urgent need. Here, we use the DrugBank database to screen for potential inhibitors against the 3CLpro main protease of SARS-CoV-2. Instead of using the traditional approach of computational screening by docking, we developed a kernel ridge regressor (using a part of the docking data) to predict the binding energy of ligands. We used this model to screen the DrugBank database and shortlist two lead candidates (bromocriptine and avoralstat) for in vitro enzymatic study. Our results show that the 3CLpro enzyme activity in presence of 100 μM concentration of bromocriptine and avoralstat is 9.9% and 15.9%, respectively. Remarkably, bromocriptine exhibited submicromolar IC50 of 130 nM (0.13 μM). Avoralstat showed an IC50 of 2.16 μM. Further, the interactions of both drugs with 3CLpro were analyzed using molecular dynamics simulations of 100 ns. Results indicate that both ligands are stable in the binding pocket of the 3CLpro receptor. In addition, the MM-PBSA analysis revealed that bromocriptine (-29.37 kcal/mol) has a lower binding free energy compared to avoralstat (-6.91 kcal/mol). Further, hydrogen bond analysis also showed that bromocriptine interacts with the two catalytic residues, His41 and Cys145, more frequently than avoralstat.Communicated by Ramaswamy H. Sarma.
Keywords: COVID-19; Drug discovery: SARS-CoV-2; avoralstat; bromocriptine; high throughput screening; kernel regression; machine learning; molecular docking; molecular dynamics.