In silico approach of modified melanoma peptides and their immunotherapeutic potential

Melanoma is a type of skin cancer with increasing incidence worldwide and high lethality. Conventional forms of treatment are not effective in advanced cancer stages. Hence, immunotherapeutic approaches have been tested to modulate immune response against tumor cells. Some vaccine models using tumor-associated antigens (TAAs) such as glycoprotein 100 (gp100) have been studied, but their expected effectiveness has not been shown until now. Antigen immunogenicity is a crucial point to improve the immune response, and therefore mutations are inserted in peptide sequences. It is possible to understand the interactions which occur between peptides and immune system molecules through computer simulation, and this is essential in order to guide efficient vaccine models. In this work, we have calculated the interaction binding energies of crystallographic data based on modified gp100 peptides and HLA-A*0201 using density functional theory (DFT) and the molecular fractionation with conjugated caps (MFCC) approach. Our results show the most relevant residue-residue interactions, the impact of three mutations in their binding sites, and the main HLA-A*0201 amino acids for peptide–HLA binding.