Predicting HIV-1 T Cell Epitopes Using Bio-basis Function Neural Networks

T cell lymphocytes play a major role in the immune response against HIV-1. Development of a safe and effective T cell immunogen based vaccine for HIV-1 depends on robust prediction of T cell epitopes. 13 HIV-1 HXB2 protein sequences with marked CTL epitope sites were obtained from the Los Alamos HIV molecular immunology database. Bio-basis function neural networks with regression and Fisher ratio methods are used for epitope prediction. A residue-by-residue approach has been taken to avoid over-estimating the accuracy of prediction. Epitopes for all HLA alleles are considered, as opposed to creating allele specific models. Mean accuracy varies from 54.55% to 81.08% amongst the HIV-1 proteins.