Prediction of deleterious non-synonymous SNPs of TMPRSS2 protein combined with Molecular Dynamics Simulations and free energy analysis to identify the potential peptide substrates against SARS-CoV-2

Abstract

Globally the SARS-CoV-2 viral infection demands for the new drugs, the TMPRSS2 target plays a vital role in facilitating the virus entry. The aim of the present study is to identify the potential peptide substrate from the Anti-viral database against TMPRSS2 of SARS-CoV-2. The compound screening and variation analysis were performed using molecular docking analysis and online tools such as PROVEAN and SNAP2 server, respectively. The re-docked crystal structure peptide substrate exhibits −128.151 kcal/mol whereas the RRKK peptide substrate shows −134.158 kcal/mol. Further, the selected compounds were proceeded with Molecular Dynamics Simulation, it explores the stability of the complex by revealing the hotspot residues (His296 and Ser441) were active for nucleophilic attack against TMPRSS2. The average Binding Free Energy values computed through MM/GBSA for RRKK, Camostat, and Crystal Structure were shown −69.9278 kcal/mol, −64.5983 kcal/mol, and −63.9755 kcal/mol, respectively against TMPRSS2. The ‘rate of acylation’ emerges as an indicator for RRKK’s efficacy, it maintains the distance of 3.2 Å with Ser441 resembles, whilst its -NH backbone stabilizes at 2.5 Å ‘Michaelis Complex’ which leads to prevent the entry of SARS-CoV-2 to human cells. The sequence variation analysis explores that the V160 and G6 substitutions are essential to emphasize the uncover possibilities for the ongoing drug discovery research. Therefore, the identified peptide substrate found to be potent against SARS-CoV-2 and these results will be valuable for ongoing drug discovery research.

Communicated by Ramaswamy H. Sarma

Keywords:

• TMPRSS2
• COVID-19
• peptide substrate RRKK
• spike protein
• molecular docking
• molecular dynamics

Acknowledgements

BR acknowledges the Department of Bioinformatics, Alagappa University for providing computational facilities to carry out this work.

Author contributions

Conceptualization: Balajee Ramachandran and Muthupandian Saravanan; Data curation: Aruchamy Mohanprasanth and Muthupandian Saravanan; Formal analysis: Balajee Ramachandran, Ahmed Nadeem and Aruchamy Mohanprasanth; Metadynamics: Balajee Ramachandran and Ahmed Nadeem; Methodology: Balajee Ramachandran; Resources: Balajee Ramachandran; Software: Balajee Ramachandran; Supervision: Muthupandian Saravanan; Validation: Balajee Ramachandran and Muthupandian Saravanan; Writing—original draft: Balajee Ramachandran; Writing—review & editing: Balajee Ramachandran, Aruchamy Mohanprasanth and Muthupandian Saravanan.

Disclosure statement

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

Additional information

Funding

The authors acknowledge and extend their appreciation to the Researchers Supporting Project Number (RSP2024R124), King Saud University, Riyadh, Saudi Arabia.