Statistical analysis of MAGE-A4 antigen expression in relative cancers and predicting antigen Presentation by HLA-B37 molecule based on bioinformatics tools

Immunotoxins, one of the promising therapeutic agents can be used in targeted of cancer therapy based on specific binding to tumor-related antigen. Evaluating and specifying the structural and functional features of these antigens provide new perspectives in the field of cancer therapy. In this study, we decipher the structure and function of  MAGE antigen family based on in-silico and bioinformatic evaluation. In the first step, using SPSS software statistical analysis of the MAGE family antigens expression in a variety of cancer types was performed. After determining the MAGE- A4 as a superior antigen with the highest expression level, based on the review of the literature, the  MAGE-A4 binding epitopes were identified for HLA isoforms. Then bioinformatic databases such as BIMAS and SYFPEITHI programs were used to confirm the reported HLA-binding epitopes in literature. Stability index and immunogenicity of epitopes were defined by ProtPram and IEDB software respectively. To determine the binding affinity and stability of HLA-peptide complex, docking screening and molecular dynamic simulation was conducted using Autodock Vina and gromacs 5.1 softwares. Our results revealed that  the MAGE-A4 antigen had high expression compared with other MAGE family members.Also, MAGE-A4 derived peptide, VDELAHFLL, with the highest stability and favorable immunogenicity was appeared. On the other hand,  the most stable of HLA-B37- VDELAHFLL complex with the lowest amount of RMSD plot was Obtained. Generally, our results indicated that VDELAHFLL desired peptide from MAGE-A4 antigen might be promising to generate the therapeutic monoclonal antibody and vaccine development in modern cancer therapy.