Exploiting the co-crystal ligands shape, features and structure-based approaches for identification of SARS-CoV-2 Mpro inhibitors

Abstract

SARS-CoV-2 enters the host cell through the ACE2 receptor and replicates its genome using an RNA-Dependent RNA Polymerase (RDRP). The functional RDRP is released from pro-protein pp1ab by the proteolytic activity of Main protease (Mpro) which is encoded within the viral genome. Due to its vital role in proteolysis of viral polyprotein chains, it has become an attractive potential drug target. We employed a hierarchical virtual screening approach to identify small synthetic protease inhibitors. Statistically optimized molecular shape and color-based features (various functional groups) from co-crystal ligands were used to screen different databases through various scoring schemes. Then, the electrostatic complementarity of screened compounds was matched with the most active molecule to further reduce the hit molecules’ size. Finally, five hundred eighty-seven molecules were docked in Mpro catalytic binding site, out of which 29 common best hits were selected based on Glide and FRED scores. Five best-fitting compounds in complex with Mpro were subjected to MD simulations to analyze their structural stability and binding affinities with Mpro using MM/GB(PB)SA models. Modeling results suggest that identified hits can act as the lead compounds for designing better active Mpro inhibitors to enhance the chemical space to combat COVID-19.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARSCoV2
• Mpro
• Pharmacophore modeling
• Virtual screening
• Molecular Docking
• MD Simulations
• PCA
• DCCM
• MM/GB(PB)SA

Acknowledgments

Authors are grateful to Higher Education Commission of Pakistan to provide funds vide project No. 6804/Federal/NRPU/R&D/HEC/2016 to conduct this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Authors thanks Higher Education Commission of Pakistan for providing funds vide projects no. 6804/Federal/NRPU/R&D/HEC/2016 and 8094/Balochistan/NRPU/R&D/HEC/2017 to purchase software and hardware..