Computational prediction of vaccine potential epitopes and 3-dimensional structure of XAGE-1b for non-small cell lung cancer immunotherapy.

BACKGROUND: XAGE-1b is shown to be overexpressed in lung adenocarcinoma and to be a strong immunogenic antigen among non-small cell lung cancer (NSCLC) patients. However, 3D structure of XAGE-1b is not available and its confirmation has not been solved yet.

METHODS: Multiple sequence alignment was run to select the most reliable templates. Homology modeling technique was performed using computer-based tool to generate 3-dimensional structure models, eight models were generated and assessed on basis of local and global quality. Immune Epitope Database (IEDB) tools were then used to determine potential B-Cell epitopes while NetMHCpan algorithms were used to enhance the determination for potential epitopes of both Cytotoxic T-lymphocytes and T-helper cells.

RESULTS: Computational prediction was performed for B-Cell epitopes, prediction results generated; 3 linear epitopes where XAGE-1b (13-21) possessed the best score of 0.67, 5 discontinuous epitopes where XAGE-1b (40-52) possessed the best score of 0.67 based on the predicted model of the finest quality. For a potential vaccine design, computational prediction yielded potential Human Leukocyte Antigen (HLA) class I epitopes including HLA-B*08:01-restricted XAGE-1b (3-11) epitope which was the best with 0.2 percentile rank. Regarding HLA Class II epitopes, HLA-DRB1*12:01-restricted XAGE-1b (25-33) was the most antigenic epitope with 5.91 IC50 value. IC50 values were compared with experimental values and population coverage percentages of epitopes were computed.

CONCLUSIONS: This study predicted a model of XAGE-1b tertiary structure which could explain its antigenic function and facilitate usage of predicted peptides for experimental validation towards designing immunotherapies against NSCLC.