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Human leukocyte antigen (HLA) is an important molecule family in the field ofhuman immunity, which recognizes foreign threats and triggers immune responsesby presenting peptides to T cells. In recent years, the synthesis of tumorvaccines to induce specific immune responses has become the forefront of cancertreatment. Computationally modeling the binding patterns between peptide andHLA can greatly accelerate the development of tumor vaccines. However, most ofthe prediction methods performance is very limited and they cannot fully takeadvantage of the analysis of existing biological knowledge as the basis ofmodeling. In this paper, we propose TripHLApan, a novel pan-specific predictionmodel, for HLA molecular peptide binding prediction. TripHLApan exhibitspowerful prediction ability by integrating triple coding matrix, BiGRU +Attention models, and transfer learning strategy. The comprehensive evaluationsdemonstrate the effectiveness of TripHLApan in predicting HLA-I and HLA-IIpeptide binding in different test environments. The predictive power of HLA-Iis further demonstrated in the latest data set. In addition, we show thatTripHLApan has strong binding reconstitution ability in the samples of amelanoma patient. In conclusion, TripHLApan is a powerful tool for predictingthe binding of HLA-I and HLA-II molecular peptides for the synthesis of tumorvaccines.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning