SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approaches

Abstract

The outbreak of severe acute respiratory coronavirus 2 (SARS-CoV-2) has created a public health emergency globally. SARS-CoV-2 enters the human cell through the binding of the spike protein to human angiotensin converting enzyme 2 (ACE2) receptor. Significant changes have been reported in the mutational landscape of SARS-CoV-2 in the receptor binding domain (RBD) of S protein, subsequent to evolution of the pandemic. The present study examines the correlation between the binding affinity of mutated S-proteins and the rate of viral infectivity. For this, the binding affinity of SARS-CoV and variants of SARS-CoV-2 towards ACE2 was computationally determined. Subsequently, the RBD mutations were classified on the basis of the number of strains identified with respect to each mutation and the resulting variation in the binding affinity was computationally examined. The molecular docking studies indicated a significant correlation between the Z-Rank score of mutated S proteins and the rate of infectivity, suitable for predicting SARS-CoV-2 infectivity. Accordingly, a 30-mer peptide was designed and the inhibitory properties were computationally analyzed. Single amino acid-wise mutation was performed subsequently to identify the peptide with the highest binding affinity. Molecular dynamics and free energy calculations were then performed to examine the stability of the peptide-protein complexes. Additionally, selected peptides were synthesized and screened using a colorimetric assay. Together, this study developed a model to predict the rate of infectivity of SARS-CoV-2 variants and propose a potential peptide that can be used as an inhibitor for the viral entry to human.

Communicated by Ramaswamy H. Sarma.

Keywords:

• SARS-CoV-2 S protein
• ACE2
• molecular docking
• infectivity
• peptide inhibitors

Acknowledgements

The authors acknowledge the SIUCEB, DBT-BIF and AICADD support for the Department of Computational Biology and Bioinformatics, University of Kerala, India for extending the necessary facilities to carry out this study.

Authors’ contributions

CSA and PRS designed the study. CSA and AAP performed the experiments. Data analysis by CSA, AK ASN, KNR, OVO and PRS. Manuscript written by CSA and PRS, edited by RR, TSKP, KNR, OVO and ASN. All authors read and approved the final manuscript.

Disclosure statement

The authors report no conflicts of interest.

Additional information

Funding

This study was supported by the SIUCEB at the Department of Computational Biology and Bioinformatics, University of Kerala, India. P.R.S. was supported by Asutosh Mookerjee Fellowship by ISCA, Kolkata.