Structure-based virtual screening, in silico docking, ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro

Abstract

COVID-19 is a viral pandemic caused by SARS-CoV-2. Due to its highly contagious nature, millions of people are getting affected worldwide knocking down the delicate global socio-economic equilibrium. According to the World Health Organization, COVID-19 has affected over 186 million people with a mortality of around 4 million as of July 09, 2021. Currently, there are few therapeutic options available for COVID-19 control. The rapid mutations in SARS-CoV-2 genome and development of new virulent strains with increased infection and mortality among COVID-19 patients, there is a great need to discover more potential drugs for SARS-CoV-2 on a priority basis. One of the key viral enzymes responsible for the replication and maturation of SARS-CoV-2 is M pro protein. In the current study, structure-based virtual screening was used to identify four potential ligands against SARS-CoV-2 M pro from a set of 8,722 ASINEX library compounds. These four compounds were evaluated using ADME filter to check their ADME profile and druggability, and all the four compounds were found to be within the current pharmacological acceptable range. They were individually docked to SARS-CoV-2 M pro protein to assess their molecular interactions. Further, molecular dynamics (MD) simulations was carried out on protein–ligand complex using Desmond at 100 ns to explore their binding conformational stability. Based on RMSD, RMSF and hydrogen bond interactions, it was found that the stability of protein–ligand complex was maintained throughout the entire 100 ns simulations for all the four compounds. Some of the key ligand amino acid residues participated in stabilizing the protein–ligand interactions includes GLN 189, SER 10, GLU 166, ASN 142 with PHE 66 and TRP 132 of SARS-CoV-2 M pro . Further optimization of these compounds could lead to promising drug candidates for SARS-CoV-2 M pro target.

Computational Exploration of gp63: Unlocking Potential Inhibitors for Leishmaniasis

“…Gcomplex denotes complex energy, Gprotein denotes receptor energy and Gligand denotes unbound ligand energy [27].…”

Natural product-inspired synthesis of coumarin–chalcone hybrids as potential anti-breast cancer agents

“…The pharmacological properties of 26-37 compounds were assessed using the QikProp v6.8 tool of Maestro 12.6.144 (Schrödinger 2020-4 LCC, New York, United States) software. The QikProp v6.8 tool analyzes a number of significant factors, such as molecular weight (MW), hydrogen bond donor (HBD), hydrogen bond acceptor (HBA), polar surface area (PSA), lipophilicity (LogP), aqueous solubility (LogS), Caco cell permeability, MSCK permeability, human serum albumin (HSA), and human oral absorption (HOA), that are useful in predicting drug-like candidate properties (Mohan et al, 2022) performed the analysis, and wrote the paper. All authors contributed to the article and approved the submitted version.…”

The Main Protease of SARS-CoV-2 as a Target for Phytochemicals against Coronavirus

“…Enabled by the development of bioinformatics tools along with eased access to protein databases, virtual screening has proven to be a fundamental tool in drug design and drug repurposing research [ 28 , 29 ]. In the virtual screening approach, automated molecular docking tools are usually used to predict the best possible variant for binding one molecule to another, considering the best orientation with the best binding affinity [ 30 ].…”

“…Furthermore, vaccines, as we have learned from previous viruses, can represent a selection pressure resulting in the evolution of novel resistant viral mutants, which again highlights SARS-CoV-2 Mpro as a good drug target, as it is less subject to such selection pressure caused by vaccines targeting the viral spike protein [26,27]. Enabled by the development of bioinformatics tools along with eased access to protein databases, virtual screening has proven to be a fundamental tool in drug design and drug repurposing research [28,29]. In the virtual screening approach, automated molecular docking tools are usually used to predict the best possible variant for binding one molecule to another, considering the best orientation with the best binding affinity [30].…”