Computational investigation of peptidomimetics as potential inhibitors of SARS-CoV-2 spike protein

Abstract

Several variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were observed since the outbreak of the global pandemic at the end of 2019. The trimeric spike glycoprotein of the SARS-CoV-2 virus is crucial for the viral access to the host cell by interacting with the human angiotensin converting enzyme 2 (ACE2). Most of the mutations take place in the receptor-binding domain (RBD) of the S1 subunit of the trimeric spike glycoprotein. In this work, we targeted both S1 and S2 subunits of the spike protein in the wild type (WT) and the Omicron variant guided by the interaction of the neutralizing monoclonal antibodies. Virtual screening of two different peptidomimetics databases, ChEMBL and ChemDiv databases, was carried out against both S1 and S2 subunits. The use of these two databases provided diversity and enhanced the chance of finding protein-protein interaction inhibitors (PPIIs). Multi-layered filtration, based on physicochemical properties and docking scores, of nearly 114,000 compounds found in the ChEMBL database and nearly 14,000 compounds in the ChemDiv database was employed. Four peptidomimetics compounds were effective against both the WT and the Omicron S1 subunit with the minimum binding free energy of −25 kcal/mol. Five peptidomimetics compounds were effective against the S2 subunit with the minimum binding free energy of −19 kcal/mol. The dynamical cross-correlation matrix insinuated that the mutations of the RBD in the Omicron variant of the SARS-CoV-2 virus altered the correlated conformational motion of the different regions of the protein. Communicated by Ramaswamy H. Sarma