IMPROVED HLA EPITOPE PREDICTION

摘要

Adaptive immune responses rely on the ability of cytotoxic T cells to identify and eliminate cells displaying disease-specific antigens on human leukocyte antigen (HLA) class I molecules. Investigations into antigen processing and display have immense implications in human health, disease and therapy. To extend understanding of the rules governing antigen processing and presentation, immunopurified peptides from B cells, each expressing a single HLA class I allele, were profiled using accurate mass, high-resolution liquid chromatography-mass spectrometry (LC-MS/MS). A resource dataset containing thousands of peptides bound to 28 distinct class I HLA-A, -B, and -C alleles was generated by implementing a novel allele-specific database search strategy. Applicants discovered new binding motifs, established the role of gene expression in peptide presentation and improved prediction of HLA-peptide binding by using these data to train machine-learning models. These streamlined experimental and analytic workflows enable direct identification and analysis of endogenously processed and presented antigens.