Chikungunya is a mosquito-borne disease, caused by the member of the Togaviridae family belongs to the genus alphavirus, making it a major threat in all developing countries as well as some developed countries. The mosquito acts as a vector for the disease and carries the CHIK-Virus. To date there is no direct treatment available and that demands the development of more effective vaccines. In this study author employed Immune Epitope Database and Analysis Resource, a machine learning-based algorithm principally working on the Artificial Neural Network (ANN) algorithm, also known as (IEDB-ANN) for the prediction and analysis of Epitopes. A total of 173 epitopes were identified on the basis of IC50 values, among them 40 epitopes were found, sharing part with the linear B-cell epitopes and exposed to the cTAP1protein, and out of 40, 6 epitopes were noticed to show interactions with the cTAP with their binding energy ranging from - 3.61 to - 1.22 kcal/mol. The six epitopes identified were exposed to the HLA class I alleles and from this all revealed interaction with the HLA alleles and minimum binding energy that ranges from - 4.12 to - 5.88 kcal/mol. Besides, two T cell epitopes i.e. 145KVFTGVYPE153 and 395STVPVAPPR403 were found most promiscuous candidates. These promiscuous epitopes-HLA complexes were further analyzed by the molecular dynamics simulation to check the stability of the complex. Results obtained from this study suggest that the identified epitopes i.e. and 395 STVPVAPPR 403 , are likely to be capable of passing through the lumen of ER to bind withthe HLA class I allele and provide new insights and potential application in the designing and development of peptide-based vaccine candidate for the treatment of chikungunya.
Keywords: Chikungunya; Docking; Epitope; HLA; Immunoinformatics; NAMD; Vaccine; cTAP.
© Springer Nature B.V. 2020.