TY - JOUR T1 - Deciphering HLA motifs across HLA peptidomes correctly predicts neo-antigens and identifies allostery in HLA specificity JF - bioRxiv DO - 10.1101/098780 SP - 098780 AU - Michal Bassani-Sternberg AU - ChloƩ Chong AU - Philippe Guillaume AU - Marthe Solleder AU - HuiSong Pak AU - Philippe O Gannon AU - Lana E Kandalaft AU - George Coukos AU - David Gfeller Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/01/06/098780.abstract N2 - The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty publicly available in-depth HLA peptidomics datasets, we show that we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our novel approach uncovers new motifs for several alleles that up to now had no known ligands. HLA-ligand predictors trained on such data substantially improve neo-antigen predictions in four melanoma and two lung cancer patients, indicating that unbiased HLA peptidomics data are ideal for in silico identification of (neo-)antigens. The new motifs further reveal allosteric modulation of HLA-I binding specificity and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays. ER -