Prediction of HIV Peptide Epitopes by a Novel Algorithm

Abstract

Identification of promiscuous or multideterminant T cell epitopes is essential for HIV vaccine development, however, current methods for T cell epitope identification are both cost intensive and labor intensive. We have developed a computer-driven algorithm, named EpiMer, which searches protein amino acid sequences for putative MHC class I- and/or class II-restricted T cell epitopes. This algorithm identifies peptides that contain multiple MHC-binding motifs from protein sequences. To evaluate the predictive power of EpiMer, the amino acid sequences of the HIV-1 proteins nef, gp160, gag p55, and tat were searched for regions of MHC-binding motif clustering. We assessed the algorithm's predictive power by comparing the EpiMer-predicted peptide epitopes to T cell epitopes that have been published in the literature. The EpiMer method of T cell epitope identification was compared to the standard method of synthesizing short, overlapping peptides and testing them for immunogenicity (overlapping peptide method), and to an alternate algorithm that has been used to identify putative T cell epitopes from primary structure (AMPHI). For the four HIV-1 proteins analyzed, the in vitro testing of EpiMer peptides for immunogenicity would have required the synthesis of fewer total peptides than either AMPHI or the overlapping peptide method. The EpiMer algorithm proved to be more efficient and more sensitive per amino acid than both the overlapping peptide method and AMPHI. The EpiMer predictions for these four HIV proteins are described. Since EpiMer-predicted peptides have the potential to bind to multiple MHC alleles, they are strong candidates for inclusion in a synthetic HIV vaccine.