2 NOVEL T-CELL EPITOPE PREDICTION ALGORITHMS BASED ON MHC-BINDING MOTIFS - COMPARISON OF PREDICTED AND PUBLISHED EPITOPES FROM MYCOBACTERIUM-TUBERCULOSIS AND HIV PROTEIN SEQUENCES

We have designed two computer-based algorithms for T cell epitope pred iction, OptiMer and EpiMer, which incorporate current knowledge of MHC -binding motifs. OptiMer locates amphipathic segments of protein antig ens with a high density of MHC-binding motifs. EpiMer identifies pepti des with a high density of MHC-binding motifs alone. These algorithms exploit the striking tendency for MHC-binding motifs to cluster within short segments of each protein. Putative epitopes predicted by these algorithms contain motifs corresponding to many different MHC alleles, and may contain both class I and class II motifs, features thought to be ideal for the peptide components of synthetic subunit vaccines. In this study, we describe the use of OptiMer and EpiMer for the predict ion of putative T cell epitopes from Mycobacterium tuberculosis and hu man immunodeficiency virus protein antigens, and demonstrate that thes e two algorithms may provide sensitive and efficient means for the pre diction of promiscuous T cell epitopes that may be critical to the dev elopment of vaccines against these and other pathogens.