PREDICTION OF HIV PEPTIDE EPITOPES BY A NOVEL ALGORITHM

Identification of promiscuous or multideterminant T cell epitopes is e ssential for HIV vaccine development, however, current methods for T c ell epitope identification are both cost intensive and labor intensive , We have developed a computer-driven algorithm, named EpiMer, which s earches protein amino acid sequences for putative MHC class I- and/or class II-restricted T cell epitopes. This algorithm identifies peptide s that contain multiple MHC-binding motifs from protein sequences, To evaluate the predictive power of EpiMer, the amino acid sequences of t he HIV-1 proteins nef, gp160, gag p55, and tat were searched for regio ns of MHC-binding motif clustering, We assessed the algorithm's predic tive power by comparing the EpiMer-predicted peptide epitopes to T cel l epitopes that have been published in the literature, The EpiMer meth od of T cell epitope identification was compared to the standard metho d of synthesizing short, overlapping peptides and testing them for imm unogenicity (overlapping peptide method), and to an alternate algorith m 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 overlappin g peptide method, The EpiMer algorithm proved to be more efficient and more sensitive per amino acid than both the overlapping peptide metho d and AMPHI, The EpiMer predictions for these four HIV proteins are de scribed, Since EpiMer-predicted peptides have the potential to bind to multiple MHC alleles, they are strong candidates for inclusion in a s ynthetic HIV vaccine.