Prediction and molecular modeling of T-cell epitopes derived from placental alkaline phosphatase for use in cancer immunotherapy

Abstract

In our ongoing efforts to combat cancer, peptide-based tumor vaccines are promising as one of the several alternatives used for cancer immunotherapy and immunoprevention. We have attempted to identify T-cell epitopes suitable for the development of a peptide-based cancer vaccine directed towards placental isozyme of alkaline phosphatase (PLAP), an oncofetal antigen. After identifying amino acid residues specific to PLAP and distinct from other close PLAP homologs, we have used sequence-based immunoinformatics tools (BIMAS and SYFPEITHI) and conducted molecular modeling studies using InsightII to investigate the binding affinity of the epitopes containing the unique residues with respective MHC class I molecules. Promiscuous epitopes binding to different alleles of different class I HLA loci were analyzed to get a population coverage that is widespread. Binding affinity deduced from the modeling studies corroborated the status of most of the epitopes scoring high in BIMAS and SYFPEITHI. We have thus identified specific epitopes from PLAP that have a potential for binding to their respective MHC class I alleles with high affinity. These peptides would be analysed in experiments to demonstrate their involvement in the induction of primary cytotoxic T-cell responses in vitro, using respective HLA-restricted T-cells in our way towards the development of an effective anti-cancer vaccine in a background of diverse MHC haplotypes.