Template-based scoring functions for visualizing biological insights of H-2Kb-peptide-TCR complexes

Abstract

Class-I major histocompatibility complex (MHC), peptide, and T-cell receptor (TCR) play an essential role of adaptive immune responses. Many prediction servers are available for identification of peptides that bind to MHC class I molecules. These servers are often lack of detailed interacting residues and binding models for analyzing MHC-peptide-TCR interaction mechanisms. This study numerously enhanced the template-based scoring function derived from protein-protein interactions for identifying MHC-peptide-TCR binding models. The scoring function considers both the template similarity and interacting force to ensure the statistically significant interface similarity between the peptide candidates and structure templates. The result shows that our scoring function is comparative to the public websites for identifying MHC binding peptides. Our model, considering both the MHC-peptide and peptide-TCR interfaces, is able to provide visualization and the biological insights of MHC-peptide-TCR binding models. We believe that our model is useful for the development of peptide-based vaccines.