Ranking of inhibitors of SARS-CoV-2 Mpro protease with end-point methodologies

Abstract

The aim of the following work is to find the computational calculation method capable of quantitatively ranking a list of inhibitors from natural products1 of the main protease of SARS-CoV-2, the protein in charge of reproducing the virus related to the recent COVID-19 pandemic (WHO), according to its binding energy with the protein (receptor). Given the complexity of both the system and the reaction conditions, this simulation is complicated and it is necessary to evaluate it in different ways in order to find the method that most closely matches, thus understanding its behaviour and being able to develop different drugs or remedies against this disease and against other upcoming diseases with similar characteristics. Such inhibitors are present in nature and have been found by different molecular dynamics assays, starting from a database of approximately 2000 compounds providing a total of 11 inhibitors, of which 5 have inhibitory activity in vivo. Finding the calculation method that ranks them can help to expand this number and thus increase the chances of finding the substance with the highest inhibitory capacity. To rank the inhibitors, we will assess how well different methods calculate their binding energy to the receptor (as we will call the protein) relative to the inhibitory activity they have demonstrated in in vivo assays. This binding energy is the difference in energies of the individual receptor and inhibitor and the ligand-receptor complex. The ideal stage of this work would be to obtain energies close to 0 for ligands with no inhibitory activity and a low energy for those with inhibitory activity. Results obtained do not give this exact order but give the first steps to achieve the final method. Previous order1 give 2 true positive compounds and the final method in this work can give 3, a part of obtaining interesting conclusions