RT Journal Article
SR Electronic
T1 Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 098780
DO 10.1101/098780
A1 Michal Bassani-Sternberg
A1 Chloé Chong
A1 Philippe Guillaume
A1 Marthe Solleder
A1 HuiSong Pak
A1 Philippe O Gannon
A1 Lana E Kandalaft
A1 George Coukos
A1 David Gfeller
YR 2017
UL http://biorxiv.org/content/early/2017/05/13/098780.abstract
AB 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 public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal allosteric modulation of the binding specificity of HLA-I alleles and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.