Unknown

Dataset Information

0

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.


ABSTRACT: 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 distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.

SUBMITTER: Bassani-Sternberg M 

PROVIDER: S-EPMC5584980 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.

Bassani-Sternberg Michal M   Chong Chloé C   Guillaume Philippe P   Solleder Marthe M   Pak HuiSong H   Gannon Philippe O PO   Kandalaft Lana E LE   Coukos George G   Gfeller David D  

PLoS computational biology 20170823 8


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 alle  ...[more]

Similar Datasets

| S-EPMC6324732 | biostudies-literature
| S-EPMC2846625 | biostudies-literature
| S-EPMC7808151 | biostudies-literature
| S-EPMC7456902 | biostudies-literature
| S-EPMC6210219 | biostudies-literature
| S-EPMC6553928 | biostudies-literature
2021-09-17 | PXD028088 | Pride
| S-EPMC5561472 | biostudies-literature
| S-EPMC5846733 | biostudies-literature
2023-01-08 | GSE211000 | GEO