Connecting MHC-I-binding motifs with HLA alleles via deep learning
Journal
Communications Biology
Journal Volume
4
Journal Issue
1
Pages
1194
Date Issued
2021
Author(s)
Abstract
The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned. ? 2021, The Author(s).
Subjects
peptide
protein binding
allele
chemistry
gene
genetics
human
Alleles
Deep Learning
Genes, MHC Class I
Humans
Peptides
Protein Binding
SDGs
Publisher
Nature Research
Type
journal article