Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: Implications for vaccine design

T cell recognition of the peptide–MHC complex initiates a cascade of immunological events necessary for immune responses.Accurate T-cell epitope prediction is an important part of the vaccine designing. Development of predictive algorithms basedon sequence profile requires a very large number of experimental binding peptide data to major histocompatibility complex(MHC) molecules. Here we used inverse folding approach to study the peptide specificity of MHC Class-I molecule with the aimof obtaining a better differentiation between binding and nonbinding sequence. Overlapping peptides, spanning the entireprotein sequence, are threaded through the backbone coordinates of a known peptide fold in the MHC groove, and theirinteraction energies are evaluated using statistical pairwise contact potentials. We used the Miyazawa & Jernigan andBetancourt & Thirumalai tables for pairwise contact potentials, and two distance criteria(Nearest atom ≫ 4.0 Å & C-beta ≫ 7.0 Å)for ranking the peptides in an ascending order according to their energy values, and in most cases, known antigenic peptides are highlyranked. The predictions from threading improved when used multiple templates and average scoring scheme. In general, when structuralinformation about a protein-peptide complex is available, the current application of the threading approach can be used to screen alarge library of peptides for selection of the best binders to the target protein. The proposed scheme may significantly reduce thenumber of peptides to be tested in wet laboratory for epitope based vaccine design.