Analysis of HLA‐A24‐restricted peptides of carcinoembryonic antigen using a novel structure‐based peptide‐HLA docking algorithm

Abstract

Carcinoembryonic antigen (CEA) is a very common tumor marker because many types of solid cancer usually produce a variety of CEA and a highly sensitive measuring kit has been developed. However, immunological responses associated with CEA have not been fully characterized, and specifically a weak immunogenicity of CEA protein as a tumor antigen is reported in human leukocyte antigen (HLA)-A24-restricted CEA peptide-based cancer immunotherapy. These observations demonstrated that immunogenic and potent HLA-A24-restricted CTL epitope peptides derived from CEA protein are seemingly difficult to predict using a conventional bioinformatics approach based on primary amino acid sequence. In the present study, we developed an in silico docking simulation assay system of binding affinity between HLA-A24 protein and A24-restricted peptides using two software packages, AutoDock and MODELLER, and a crystal structure of HLA-A24 protein obtained from the Protein Data Bank. We compared the current assay system with HLA-peptide binding predictions of the bioinformatics and molecular analysis section (BIMAS) in terms of the prediction capability using MHC stabilization and peptide-stimulated CTL induction assays for CEA and other HLA-A24 peptides. The MHC stabilization score was inversely correlated with the affinity calculated in the docking simulation alone (r = -0.589, P = 0.015), not with BIMAS score or the IFN-γ production index. On the other hand, BIMAS was not significantly correlated with any other parameters. These results suggested that our in silico assay system has potential advantages in efficiency of epitope prediction over BIMAS and ease of use for bioinformaticians.