Schematic diagram of TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning-based workflow to predict CD8 + T-cell epitopes from MHC-I presented pathogenic or self-peptides. Once peptides have been predicted by NetMHCpan to bind HLA alleles, the TRAP uses the peptide sequence and NetMHCpan rank scores as inputs to predict the immunogenicity of the peptide with the respective HLA binding affinity. The TRAP workflow will output TRAP prediction score along with confidence in its prediction. If the prediction is detected to have a low confidence, we recommend predicting cancer neoepitopes using TESLA [ 52 ], which is known to use more general features such as agretopicity and dissimilarity to self-proteome, and pathogenic peptides with RSAT (relative similarity to autoantigens or tumour-associated antigens).
