Immunoinformatics based design and prediction of proteome-wide killer cell epitopes of : Potential application in vaccine development.

Despite several extensive and exhaustive efforts, search for potential therapy against leishmaniasis has not made much progress. In the present work, we have employed mining strategy to screen proteome for identification of promising vaccine candidate. We have screened 21 potential antigenic proteins from 7960 total protein of , based on the presence of signal peptide, GPI anchor, antigenicity prediction and substractive proteomic approach. Secondly, we have also performed comprehensive immunogenic epitope prediction from the screened 21 proteins, using IEDB-AR tools. Out of the 21 antigenic proteins, we obtained 11 immunogenic epitopes from 9 proteins. The final results revealed that four predicted epitopes namely; , , and , have significantly better binding potential with respective alleles and could elicits immune responses. Docking analysis using PATCHDOCK server and molecular dynamic simulation using GROMACS revealed the potential of the sequences as immunogenic epitopes. In silico studies also suggested that the epitopes occupied almost same binding cleft with the respective alleles, when compared with the reference peptides. It is also suggested from the molecular dynamic simulation data that the peptides were intact in the pocket for longer periods of time. Our study was designed to select MHC class I restricted epitopes for the activation of CD8 T cells using immunoinformatics for the prediction of probable vaccine candidate against parasites. Communicated by Ramaswamy H. Sarma.