MHC-binding Motifs Research Articles

Fine mapping of T-cell determinants of bovine beta-lactoglobulin.

T-cell recognition sites, i.e. T-cell determinants, of bovine beta-lactoglobulin, a major allergen in milk, were analyzed in detail. For this purpose, we prepared primary cultures of lymph node cells from three strains of mice, C57BL/6 (H-2b), C3H/HeN (H-2k), and BALB/c (H-2d), and examined the proliferative response of these cells to a complete set of overlapping 15-mer peptides which covered the entire sequence of beta-lactoglobulin by shifting in single amino acid steps. We were able to determine the putative core sequence of each T-cell determinant and estimate its relative importance. In the case of C57BL/6 mice, dominant, subdominant, and minor determinants were identified as residues 122-130, 16-26, and 108-122, respectively, as represented by their core sequences. Each determinant peptide induced the production of interferon-gamma, the amount of which showed a correlation with the intensity of the proliferative response induced by each determinant. In the case of C3H/HeN mice, a dominant determinant comprised of residues 140-148 was identified together with three subdominant and two minor determinants. Dominant T-cell determinants recognized in BALB/c mice were identified as residues 67-75, 71-79, and 80-88, and six other regions were identified as subdominant determinants. Comparisons between our results and the determinants predicted from relevant MHC-binding motifs reported to date revealed the inadequacy of the motifs in predicting even the dominant determinants. The information obtained by complete mapping of T-cell determinants as done in this study is expected to be helpful in establishment and evaluation of new prediction methods and also may contribute to the development of a new approach to control immune responses by manipulation of the T-cell determinants of allergens.

Prediction of HIV peptide epitopes by a novel algorithm.

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.

Two 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 prediction, OptiMer and EpiMer, which incorporate current knowledge of MHC-binding motifs. OptiMer locates amphipathic segments of protein antigens with a high density of MHC-binding motifs. EpiMer identifies peptides 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 prediction of putative T cell epitopes from Mycobacterium tuberculosis and human immunodeficiency virus protein antigens, and demonstrate that these two algorithms may provide sensitive and efficient means for the prediction of promiscuous T cell epitopes that may be critical to the development of vaccines against these and other pathogens.

New methods to predict MHC-binding sequences within protein antigens

The identification and analysis of MHC-binding sequences within protein antigens, and ultimately the ability to predict them, is central to immunology. Recent advances have revealed increasingly complex MHC-binding motifs and allow prediction of sequences that bind to both classes of MHC molecules. The systematic characterization of binding motifs for all human MHC alleles is now possible and will facilitate the design of peptides for therapeutic intervention.

MHC-binding motifs and the design of peptide-based vaccines

The epitope of an immunogenic peptide can be partially replaced with peptide fragments from various pathogens without any detrimental effect to their anchoring within the binding groove of MHC class II molecules. Using this principle, effective peptide vaccines against influenza Aichi virus have been found to induce antibody responses that neutralize virus in vitro.