Targeted immune epitope prediction to HHLA2 and MAGEB5 protein variants as therapeutic approach to related viral diseases

BackgroundTargeted immunotherapy is mostly associated with cancer treatment wherein designed molecules engage signaling pathways and mutant proteins critical to the survival of the cell. One of several genetic approaches is the use of in silico methods to develop immune epitopes targeting specific antigenic regions on related mutant proteins. In a recent study we showed a functional association between the gamma retrovirus HERV-H Long Terminal Associating (HHLA1, HHLA2 and HHLA3) proteins and melanoma associated antigen of the B class proteins (MAGEB5), with a resultant decrease in expression of HLA class I and II immune variants. HLA-C and HLA-DRB5 were the main HLA class I and II Immune variants, respectively, that showed expression changes across viral samples of interest. Specific immune variants for HLA-C and HLA-DRB5 were filtered for the top ten based on their relative frequency of counts across the samples.ResultsProtein variants for HHLA1, HHLA2, HHLA3 and MAGEB5 were used to predict antigenic epitope peptides to immune peptide-MHC class I and II binding using artificial neural networks. For IC50 peptide scores (PS) ≥ 0.5 with a transformed binding ability between 0 and 1, the top 5 epitopes identified for all targeted genes HHLA1,2 & 3 and MAGEB5 were qualified as strong or weak binders according to the threshold. Domain analysis using NCBI Conserved Domain Database (CDD) identified HHLA2 with immunoglobulin-like domains (Ig_C1-set) and MAGEB5 with the MAGE Homology Domain (MHD). Linear regression showed a statistical correlation (P ConclusionSpecific immune responses to targeted epitope peptides and their prediction models, suggested co-expression and co-evolution for HHLA2 and MAGEB5 in viral related diseases. HHLA2 and MAGEB5 could be considered markers for virus related tumors and targeted therapy for oncogenic diseases.