An in silico molecular dynamics simulation study on the inhibitors of SARS-CoV-2 proteases (3CL^pro and PL^pro) to combat COVID-19

ABSTRACT

The work attempts to recognise the possible inhibitors against Papain-like protease (PL^pro) and 3-Chymotrypsin-like protease (3CL^pro) of SARS-CoV-2 to combat infectious COVID-19 virus using in silico studies. These two proteases are predominantly involved in the virus replication cycle; hence they are considered as potential drug targets. The virtual dock screening was performed for 53 selected drugs. The drugs with higher binding energy and oriented in the vicinity of active binding sites were selected for finding thermal stability using molecular dynamics (MD) simulation. The docking result reflects that the drugs A17 (Dasabuvir) and A34 (Methisazone) bind with PL^pro and the drugs A17 and A53 (Vaniprevir) bind with 3CL^pro with higher binding affinities. The MD simulation and principal component analysis show that the drug A17 has stable dynamic behaviour with both proteins over the 300 ns time-scale. The binding free energy of complexes was predicted from the last 100 ns trajectories using MM/PBSA. The predicted binding free energy of PL^Pro-A17 (Dasabuvir) and PL^pro-A34 complexes (Methisazone) were −16.1 kcal/mol and −12.3 kcal/mol, respectively and −41.3 kcal/mol and −11.9 kcal/mol for 3CL^pro-A17 (Dasabuvir) and 3CL^pro-A53 (Vaniprevir) complexes, respectively. However, further experimental validation is required to confirm their inhibitory activities against SARS-CoV-2 causing COVID-19.

KEYWORDS:

• SARS-CoV-2 PL^pro
• SARS-CoV-2 3CL^pro inhibitors
• molecular docking
• molecular dynamics simulation
• MM/PBSA

Acknowledgements

TK acknowledges the management of SASTRA Deemed University, Thanjavur, Tamil Nadu, India for providing necessary infrastructure and computational facilities. Special thanks to Dr. Satish Selvaraj Venkatesh (prufer.in) for proofreading the manuscript. TK, DCD and KB designed the research. TK, VJR, PRB performed the molecular docking. KB performed MD Simulations. TK and KB wrote the article, and TK, KB, DCD and JH reviewed the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Department of Science and Technology, New Delhi, India under DST-FIST project [Project No: SR/FST/PS-1/2020/135]. DCD acknowledges Czech Academy of Sciences Post-doctoral Fellowship [No. L200551951 Programu podpory perspektivn Nevilidských zdrojů – postdoktorandů]. KB and JZ acknowledge the Ministry of Education, Youth, and Sport of the Czech Republic (MEYS CR) [grant number LTAUSA18168] (Inter-Excellence Inter-Action) for funding. 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.