Design & discovery of small molecule COVID-19 inhibitor via dual approach based virtual screening and molecular simulation studies

Abstract

The emerged COVID-19 (SARS corona virus) pandemic leads to severe or fatal respiratory tract infections affecting millions of people worldwide since its outbreak. The situation needs the newer molecule to control the infections as the pandemic had very badly affected the health and socioeconomic conditions of human being. CoV-2 main protease is considered to be key enzyme by targeting which we can design or develop the drug candidate. The active fitting and binding of any molecule depends upon the shape and electrostatic properties of ligand complementary to the receptor site. In this study ZINC13 database, a drug like subset (13,195,609 molecules) was subjected to shape and electrostic based virtual screening (VROCS & EON software) and followed by molecular modelling studies using docking and molecular dynamics simulation. Further the drug ability of identified candidate was predicted by the SiteMap analysis. The best shape and electrostatic similarities were observed between ZINC19973962 and reference molecule. The Taminto_shape and Tanimoto_{electrostatic} was found to be 0.667 and 0.022 respectively. The molecule also displayed the identical binding pattern with docking score −7.964 and this interaction was further validated by the molecular dynamics simulations. The RMSD & RMSF values were found to be 1.5 Å and1.8 Å respectively suggesting the stability of complex and very low fluctuation in ligand–protein complex over the entire MD simulation run. SiteMap analysis showed the identical Dscore of reference and identified HIT that indicated the molecule ZINC19973962 would be the promising druggable candidate against COVID main protease enzyme and can be used as lead molecule for the development of anti-COVID molecule.