Project description:HLA-C expresion varies widely across the different HLA-C alleles. MicroRNA binding can partly explain the differences in HLA-C allele expression however other contributing factors still remain undetermined. Here we use two common HLA-C alleles, HLA-C*05:01 and HLA-C*07:02, to explore differences in expression levels. Using functional, structural and peptide repertoire comparisons we demonstrate that HLA-C expression levels are not only modulated at the RNA level but also at the protein level. This dataset contains RAW data and database search results for HLA-C*05:01 and HLA-C*07:02 from the 721.221 cell line.
Project description:Analysis of peptide presentation by Human Leukocyte Antigen (HLA) class I of influenza B infected C1R cells expressing HLA-B*07:02, -B*08:01 or -B*35:01.
Project description:Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.