Immunoinformatics aided approach for predicting potent cytotoxic T cell epitopes of respiratory syncytial virus

Abstract

Respiratory syncytial virus (RSV) is an infectious viral pathogen that causing serious respiratory infection in adults and neonates. The only approved therapies for RSV are the monoclonal antibodies palivizumab and its derivative motavizumab. Both treatments are expensive and require a hospital setting for administration. A vaccine represents a safe, effective and cheaper alternative for preventing RSV infection. In silico prediction methods have proven to be valuable in speeding up the process of vaccine design. In this study, reverse vaccinology methods were used to predict the cytotoxic T lymphocytes (CTL) epitopes from the entire proteome of RSV strain A. From amongst 3402 predicted binders to 12 high frequency alleles from the Immune Epitope Database (IEDB), 567 had positive processing scores while 327 epitopes were predicted to be immunogenic. A thorough examination of the 327 epitopes for possible antigenicity, allergenicity and toxicity resulted in 95 epitopes with desirable properties. A BLASTp analysis revealed 94 unique and non-homologous epitopes that were subjected to molecular docking across the 12 high frequency alleles. The final dataset of 70 epitopes contained 13 experimentally proven and 57 unique epitopes from a total of 11 RSV proteins. From our findings on selected T-cell-specific RSV antigen epitopes, notably the four epitopes confirmed to exhibit stable binding by molecular dynamics. The prediction pipeline used in this study represents an effective way to screen the immunogenic epitopes from other pathogens.

Communicated by Ramaswamy H. Sarma

Keywords:

• Respiratory syncytial virus
• epitope
• allele
• vaccine
• immunogenicity
• cytotoxic T cell

Acknowledgements

The author gratefully acknowledges the Department of Nanoscience and Technology, Bharathiar University, Coimbatore, Tamilnadu India, for providing essential computational services and reliable command throughout the research work. We also thank Mr. Kamarajan Rajagopalan, International Research Centre, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India for supporting the impart about molecular docking study.

Authors’ contribution

The study was designed by PP and MM. The experiments were carried out by GA, while simulations were performed by YN. The results are analysed by GA, PP, MM and YN. The manuscript was written by GA with feedback from all of the contributors. Paramasivam Premasudha: Supervision, Investigation, Methodology, Data curation, Review & Editing. Manikandan Mohan: Conceptualization, Methodology, Validation, Data curation. Yogesh B. Narkhede: MD simulation, Validation, Data curation, Review & Editing. Gayathri Anandhan: Methodology, Analysis, Data curation, Writing.

Disclosure statement

The authors declare no competing interest.

Availability of data and material

All data supporting this study is provided as supplementary information accompanying this paper

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.