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Human T cell leukemia virus type-1 (HTLV-1) is the cause of adult T cell leukemia/lymphoma (ATL), uveitis, and certain pulmonary diseases. In recent decades, many scientists have proposed the development of different treatment and prevention strategies to combat HTLV-1 infection. In this study, we used bioinformatics tools to predict peptide and protein vaccine candidates against HTLV-1 that can potentially induce antibody production and both CD4+ and CD8+ T cell immune responses. Five critical proteins, viz.,  Hbz, Tax, Pol, Gag , and  Env, were  analyzed for predicting immunogenic T and B cell epitopes and subsequently evaluated using bioinformatics tools. Based on the predictions, the most antigenic epitopes were selected, and their interaction with immune receptors was investigated. We also designed a protein vaccine candidate with an eight-epitopes-rich domain, including  overlapping epitopes detected on both B and T cells.  Then, the interaction of the epitope and the designed protein with immune receptors was validated in an  in silico  docking study. The docking analysis showed that the O2 epitope and D8 protein interact strongly with immune receptors, especially the HLA-A*02:01 receptor. The stability of the interactions was investigated by molecular dynamics (MD) for 100 ns. The root mean square deviation, radius of gyration, hydrogen bonds, and solvent-accessible surface area were calculated for the 100 ns trajectory period. MD studies demonstrated that the O2–HLA-A*02:01 and D8–HLA-A*02:01 complexes were stable during the simulation. Analysis of  in silico  results showed that the peptide and the designed protein could elicit humoral and cell-mediated immune responses. Communicated by Ramaswamy H. Sarma

In silico identification of epitope-based vaccine candidates against HTLV-1