Major histocompatibility complex class I binding predictions as a tool in epitope discovery

Abstract

Over the last decade, in silico models of the major histocompatibility complex (MHC) class I pathway have developed significantly. Before, peptide binding could only be reliably modelled for a few major human or mouse histocompatibility molecules; now, high-accuracy predictions are available for any human leucocyte antigen (HLA) -A or -B molecule with known protein sequence. Furthermore, peptide binding to MHC molecules from several non-human primates, mouse strains and other mammals can now be predicted. In this review, a number of different prediction methods are briefly explained, highlighting the most useful and historically important. Selected case stories, where these 'reverse immunology' systems have been used in actual epitope discovery, are briefly reviewed. We conclude that this new generation of epitope discovery systems has become a highly efficient tool for epitope discovery, and recommend that the less accurate prediction systems of the past be abandoned, as these are obsolete.

Figure 1

Schematic and highly simplified view of the major histocompatibility complex (MHC) class I presenting pathway.ER, endoplasmic reticulum; TAP, transporter associated with antigen presentation.

Figure 2

The Kullback–Leibler (KL) sequence logo for the HLA-A*0201 allele is taken from the major histocompatibility complex (MHC) motif viewer website. The logo is calculated from the 30 peptides shown in the left panel. The KL information content is plotted along the 9mer. Amino acids with positive influence on the binding are plotted on the positive y-axis, and amino acids with a negative influence on binding are plotted on the negative y-axis. The height of each amino acid is given by their relative contribution to the binding specificity. For details on how to calculate position-specific scoring matrices and sequence logos to characterize binding motif specificities see refs ,.

Figure 3

Schematic view of the relationships between the peptide binding space of some human leucocyte antigen-A alleles clustered into supertypes using information from Lund et al. The distances and branch points are artificial and solely for visualization purposes. Sequence logos generated using the predicted peptide binding space as generated by major histocompatibility complex Motifviewer are shown for each allele.