Insilico Screening for Identification of Hits against SARS-Cov-2 Variant of Concern B.1.617 and NSP12 Mutants by Molecular Docking and Simulation Studies

Abstract

Abstract Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV), also known as SARS-CoV-2, have caused global epidemics with high morbidity and mortality. Active research on finding effective drugs against 2019-nCoV/SARS-CoV-2 is going on. In silico screening represents the best approach for hits identification and could shorten the time and reduce cost compared to de novo drug discovery. Recently, CoV2 mutations have been a big concern in India, particularly on non-structural proteins (NSPs) and Spike Protein (B.1.617) which are the key targets that play a pivotal role in mediating viral replication and transcription. Herein, this study analyzed the NSPs and spike’s structural aspects of mutant strains of SARS-CoV-2. The three-dimensional structures of NSPs and S Spike proteins were retrieved from the protein data bank or modeled. And a dataset of an antiviral compound library containing 490,000 drug-like ligands and structurally diverse biologically active scaffolds was used for our studies. Initially, the molecular alignment was performed for library compounds with the reference drug molecule to find targets that match the field points. Antiviral compounds having a similarity score >0.6; were selected for further docking studies with wild and mutant NSPs and S Spike protein of SARS-CoV-2 variant B.1.617. The docking studies identified a potent analog MA-11, which exhibited the highest binding affinity towards wild and mutant proteins. Further, molecular dynamics simulation studies of selected compounds confirmed their perfect fitting into NSP12 and spike active sites and offer direction for further lead optimization and rational drug design.