<i>In silico</i> identification of epitope-based vaccine candidates against HTLV-1

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., <i>Hbz, Tax, Pol, Gag</i>, and <i>Env, were</i> 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 <b>overlapping epitopes detected on both B and T cells.</b> Then, the interaction of the epitope and the designed protein with immune receptors was validated in an <i>in silico</i> 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 <i>in silico</i> results showed that the peptide and the designed protein could elicit humoral and cell-mediated immune responses. Communicated by Ramaswamy H. Sarma