In silico Approach for B Cell Epitopes Prediction of Respiratory Syncytial Virus

In recent years, epitope-based peptides have constituted a prominent and prospective class in pharmacology; especially in mounting specific immune responses against pathogenic agents. Respiratory Syncytial Virus (RSV) has been a leading cause of mortality in infants and children aged under 5 years. To date, there is a dearth of vaccines or small molecules targeting RSV. The identification of RSV B-cell epitopes is a preliminary step in developing an epitope-based vaccine design. The prediction for B-cell epitopes using an in-silico approach will enhance our understanding of etiopathogenesis and aid in the creation of effective vaccines that target B-cell response. In our study, three distinct prediction tools- ABCpred, Bepipred, and BCpred were used to assess the RSV proteomes, leading to the prediction of 3,314 B-cell epitopes, from which 128 were revealed to be overlapping epitopes. The physicochemical properties of 128 overlapping epitopes were studied subsequently. A total of 35/128 of them were anticipated to be antigenic, non-allergenic, non-toxic, and non-homologous peptides. According to structural analysis utilizing the Ellipro database, 133 linear epitopes and 53 discontinuous epitopes were predicted. Finally, 4 potent epitopes show a high binding score of 0.819 to 0.838, which will improve and strengthen the development of effective RSV vaccines.