Validation of the binding affinities and stabilities of ivermectin and moxidectin against Sars-CoV-2 receptors using molecular docking and molecular dynamics simulation

Abstract

Corona-viruses (CoVs), a large family of single-stranded RNA viruses, can infect animals and also humans, causing respiratory, gastrointestinal, hepatic, and neurologic disease. As the largest known RNA viruses, they are further divided into four genera: alpha-coronavirus, beta- corona- virus, gamma-corona virus and delta-coronavirus. SARS-CoV-2 belong to genus betacoronavirus. The viral genome of SARS-CoV-2 codes 4 major structural proteins: the nucleocapsid (N) protein, the transmembrane (M) protein, the envelope (E) protein, and the spike (S) protein. It also encodes 16 nonstructural proteins (NSPs) and 9 accessory proteins required for replication and pathogenesis. The Molecular docking simulations was used to determine the binding affinities of Ivermectin, Moxidectin and Molnupiravir against NSP13 receptor of SARS-CoV-2. The experimental crystal structures of the receptor was obtained from the protein data bank (PDB). The receptor was prepared using Chimera-1.10.1 and AutoDock tools-1.5.6. The 3D structure of the selected approved drugs and the reference ligand was obtained from PDB and Drugbank and prepared using AutoDock tools-1.5.6. Validation of docking protocol was done by reproducing the PDB crystal structures insilico. Molecular docking simulations were performed using AutoDockVina-4.2.6 on the Linux operating system (ubuntu) 20.04. Then the docking results were analysed and visualized using Pymol-2.3.0. Molecular dynamics of the frontrunners with the reference ligand and protein was done in 10000 ps. Moxidectin, molnupiravir and Ivermectin showed high binding affinities to the receptors. Moxidectin and Ivermectin showed stability after molecular dynamics simulation to further validate the claim. These drugs are predicted as possible antivirals in the treatment of Covid-19.