Peptides: two-faced, cheating go-betweens? Limits in the cellular immune system

Relative immunogenicity of Fya and K antigens in a Caucasian population, based on HLA class II restriction analysis

BACKGROUND

It has long been known that relative immunogenicity is a characteristic of protein red blood cell (RBC) antigens, but the mechanisms remain unclear. The aim of this work was to elucidate the mechanisms underlying this relative immunogenicity.

STUDY DESIGN AND METHODS

Two RBC antigens were used as a model--the highly immunogenic K antigen (KEL1) and the less immunogenic Fya antigen (FY1)--and analyzed the distribution of DRB1* molecules in two groups of Caucasian individuals producing anti-Fya (n = 29) or anti-K (n = 30) alloantibodies. These experimental results were compared to the results generated by TEPITOPE, a DRB1* peptide-binding motif prediction algorithm.

RESULTS

It was found that within the anti-Fya group, the DRB1*04 phenotypic frequency was 100 percent, indicating that the DRB1*04 molecule is the restriction molecule. In the anti-K group, numerous DRB1* molecules were identified, demonstrating a high degree of histocompatibility promiscuity, corresponding to the predominant molecules in the Caucasian population. These findings were confirmed by TEPITOPE.

CONCLUSION

These results strongly suggest that protein RBC intrinsic immunogenicity depends on the distribution of DRB1* restriction molecules.

A novel predictive technique for the MHC class II peptide-binding interaction

Antigenic peptide is presented to a T-cell receptor through the formation of a stable complex with a Major Histocompatibility Complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC Class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. We have developed a novel predictive technique that uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC Class II peptide complex. This is the 1st structure-based technique, as previous methods have been based on binding data. ROC curves are used to quantify the accuracy of the molecular modeling technique. The novel predictive technique is found to be comparable with the best predictive software currently available.