![Author ORCID: We display the ORCID iD icon alongside authors names on our website to acknowledge that the ORCiD has been authenticated when entered by the user. To view the users ORCiD record click the icon. [opens in a new tab]](https://chemrxiv.org/engage/assets/public/chemrxiv/images/logos/orcid.png)
Abstract
The SARS-CoV-2 virus is highly contagious to humans and has caused a pandemic of global proportions. Despite worldwide research efforts, efficient targeted therapies against the virus are still lacking. With the ready availability of the macromolecular structures of coronavirus and its known variants, the search for anti-SARS-CoV-2 therapeutics through in silico analysis, has become a highly promising field of research. In this study, we investigate the inhibiting potentialities of triazole-based compounds against the SARS-CoV-2 main protease (Mpro). The SARS-CoV-2 main protease (Mpro) is known to play a prominent role in the processing of polyproteins that are translated from the viral RNA. Compounds were pre-screened from 171 candidates (collected from the DrugBank database). The results showed that four candidates (Bemcentinib, Bisoctrizole, PYIITM and NIPFC) had high binding affinity values and had the potential to interrupt the main protease (Mpro) activities of the SARS-CoV-2 virus. The pharmacokinetic parameters of these candidates were assessed and they were then put through molecular dynamic (MD) simulation for stability, interaction and conformation analysis. In summary, we successfully identified the most suitable compounds for targeting SARS-CoV-2 main protease (Mpro). Based on our computational studies, we can suggest that the identified compounds can be used for further experimental approach as potential drug molecules against SARS-CoV-2.
Supplementary materials
Title
In silico identification and validation of organic triazole based ligands as potential inhibitory drug compounds of SARS-CoV-2 main protease
Description
Table S1 List of Viruses used for triazole based ligand’s antiviral activity screening: Table S2 List of interacting residues participating in Mpro ligand pocket formation: Table S3 List of best ligand molecules according to their binding affinity score through docking process: Table S4 Evaluation of Lipinski's rule of 5 with a drug-likeness score by Molsoft LLC: Drug-Likeness and molecular property prediction of the selected molecules (best 4 ligands): Figure S1 2D chemical structure of the best 23 triazole based organic ligands : Figure S2 Drug likeness evaluation of selected ligands using Molsoft LLC: Drug-Likeness and molecular property prediction. Bemcentinib (DB12411) (A), Bisoctrizole (DB11262) (B), PYIITM (DB07213) (C), and NIPFC (DB07020) (D): Table S5 Triazole based organic ligands antiviral activity screening through web based antiviral compound prediction server: Script S1 NVT run: Script S2 NPT run: Script S3 MD run : Script 4 Interaction energy run.
Actions
