Repurposing Peptidomimetic Drugs as Potential Inhibitors of Novel SARS-CoV-2 Main Protease Using Network Theory: A Protein-Ligand Binding Site Similarity Approach

Abstract A new strain of coronavirus known as severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) is responsible for the current COVID-19 pandemic, which has not only affected the health of millions of individuals, but also caused severe socio-economic disruption. To curb the spread of the virus, various strategies have been employed to develop new drug therapy. Considering the pandemic condition, targeting the substrate binding site of main protease enzyme of SARS-CoV-2, which plays an important role in replication of the coronavirus, will be beneficial. Its high similarity with its predecessor virus’s main protease and dissimilarity with human protease makes it a promising target and will also facilitate drug repurposing. In this study, we have used Protein Encoded Shape Distributions (PESD) to calculate similarity between the ligand binding site of the SARS-CoV-2 main protease and other proteins. Similarity networks were constructed between these proteins using different distance metrics with edge density < 0.1 percent. Construction of low edge density protein-ligand binding site similarity networks helped in rational identification of the most similar ligand binding sites of proteins with the SARS-CoV-2 main protease. Based on this knowledge, a dataset of FDA approved drug molecules as well as experimental drugs was collected from the literature. These molecules were subjected to virtual screening through molecular docking against the SARS-CoV-2 main protease, followed by conventional molecular dynamics simulation, replica exchange molecular dynamics and binding free energy calculations. Based on these studies Q27458218 have been found to be suitable for repurposing as a SARS-CoV-2 main protease inhibitor.