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Designing a vaccine for a disease is one of the crucial tasks that involve millions and billions of dollars, several decades and yet there is no guarantee of successful results. Several pharmaceutical companies are investing their money and time in such activities. Computational biology could be of great help in these activities by proving a library of plausible candidates that might actually show some positive responses. MHC binding peptide prediction is one such area where the immense power of computers could be used to get a breakthrough. In this direction several databases and servers have been developed by many labs to predict the MHC binding peptides. These short peptides on the antigen surface are recognized by the MHC molecule and are presented to the receptors of T-cells for further immune response. Peptides that bind to a given MHC molecule share sequence similarity. Here we present a comparative study of servers that can predict the MHC binding peptides in a given protein sequence of the antigen. Based on this comparative analysis on HIV data, we are able to propose a library of putative vaccine candidates for the env GP-160 protein of HIV-1.

Comparative analysis of epitope predictions: proposed library of putative vaccine candidates for HIV