Ranking Potential Binding Peptides to MHC Molecules by a Computational Threading Approach

In this paper, an approach developed to address the inverse protein folding problem is applied to prediction of potential binding peptides to a specific major histocompatibility complex (MHC) molecule. Overlapping peptides, spanning the entire protein sequence, are threaded through the backbone coordinates of a known peptide fold in the MHC groove, and their interaction energies are evaluated using statistical pairwise contact potentials. With currently available tables for pairwise potentials, promising results are obtained for MHC-peptide complexes where hydrophobic interactions predominate. By ranking the peptides in an ascending order according to their energy values, it is demonstrated that, in most cases, known antigenic peptides are highly ranked. Furthermore, predicted hierarchies are consistent with experimental binding results. Currently, predictions of potential binding peptides to a specific MHC molecule are based on the identification of allele-specific binding motifs. However, it has been demonstrated that these motifs are neither sufficient nor strictly required to ensure binding. The computational procedure presented here succeeds in determining the MHC binding potential of peptides along a protein amino acid sequence, without relying on binding motifs. The proposed scheme may significantly reduce the number of peptides to be tested, identify good binders that do not necessarily show the known allele-specific binding motifs, and identify the best candidates among those with the motifs. In general, when structural information about a protein-peptide complex is available, the current application of the threading approach can be used to screen a large library of peptides for selection of the best binders to the target protein.