Binding and inhibitory effect of ravidasvir on 3CL^pro of SARS-CoV‐2: a molecular docking, molecular dynamics and MM/PBSA approach

Abstract

Drug repurposing requires a limited resource, cost-effective and faster method to combat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, this in silico studies attempts to identify the drug-likeness properties of ravidasvir, an II/III phase clinical trial chronic hepatitis C drug against 3-Chymotrypsin-like protease (3CL^pro) of SARS-CoV-2 to combat the ongoing coronavirus disease 2019 (COVID-19) pandemic. This protease is predominantly involved in virus replication cycle; hence it is considered as a potent drug target. The molecular docking results showed that ravidasvir was found to be potent inhibitors of 3CL^pro with scoring function based binding energy is −26.7 kJ/mol. Further dynamic behaviour of apo form and complex form of ravidasvir with 3CL^pro were studied using molecular dynamics (MD) simulations over 500 ns each, total 2 µs time scale. The motion of the protein was studied using principal component analysis of the MD simulation trajectories. The binding free energy calculated using MM/PBSA method from the MD simulation trajectory was −190.3 ± 70.2 kJ/mol and −106.0 ± 26.7 kJ/mol for GROMOS96 54A7 and AMBER99SB-ILDN force field, respectively. This in silico studies suggesting ravidasvir might be a potential lead molecule against SARS-CoV-2 for further optimization and drug development to combat the life-threatening COVID-19 pandemic.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• 3CL^pro
• molecular docking
• molecular dynamics simulation
• principal component analysis
• MM/PBSA

Acknowledgements

A special thanks to my Ph.D. supervisor RNDr. Mgr. Jozef Hritz, Ph.D., Protein Structure and Dynamics - Lukáš Žídek research group and CEITEC-MU for infrastructure and support for this reserach and Ms Shilpa Chatterjee for valuable discussion.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is funded by the Ministry of Education, Youth, and Sport of the Czech Republic (MEYS CR), grant number LTAUSA18168 (Inter-Excellence Inter-Action). Computational resources were supplied by (metacentrum and IT4Innovations National Supercomputing Center) the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Krishnendu Bera is also supported by Brno Ph.D. Talent Scholarship – funded by the Brno City Municipality, Brno, Czech Republic.