Biology subjects

Hildebrand, W.

Publications and source records attributed to Hildebrand, W..

3 recordsLinked to original sources

bioRxiv2022

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome by accurate and comprehensive HLA-DQ antigen presentation prediction

HLA class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the - and {beta}-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific MS-immunopeptidomics data. The analysis demonstrated a highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants revealed a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study has furthered our understanding of HLA-DQ specificities and has for the first time cast light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2.

bioinformatics↗

bioRxiv2021

Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome

Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology and next-generation vaccine development. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that accurately deal with these challenges is currently a major bottleneck for the large-scale application of this technique. Here, we present the MHCMotifDecon that benefits from state-of-the-art HLA class-I and class-II predictions to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely HLA allele while discarding co-purified contaminants. We have benchmarked the tool against other state-of-the-art methods and illustrated its application on experimental datasets for HLA-DR demonstrating a previously underappreciated role for HLA-DRB3/4/5 molecules in defining HLA class II immune repertoires. With its ease of use MHCMotifDecon can efficiently guide interpretation of immunopeptidome datasets, serving the discovery of novel T cell targets.

immunology↗

bioRxiv2021

Immunogenic potential of neopeptides depends on parent protein subcellular location

Antigen presentation via the major histocompatibility complex (MHC) is essential for anti-tumor immunity, however the rules that determine what tumor-derived peptides will be immunogenic are still incompletely understood. Here we investigate whether protein subcellular location driven constraints on accessibility of peptides to the MHC associate with potential for peptide immunogenicity. Analyzing over 380,000 peptides from studies of MHC presentation and peptide immunogenicity, we find clear spatial biases in both eluted and immunogenic peptides. We find that including parent protein location improves prediction of peptide immunogenicity in multiple datasets. In human immunotherapy cohorts, location was associated with response to a neoantigen vaccine, and immune checkpoint blockade responders generally had a higher burden of neopeptides from accessible locations. We conclude that protein subcellular location adds important information for optimizing immunotherapies. HighlightsO_LIPeptides eluted from class I and II MHC reflect biases in the subcellular location of the parent proteins C_LIO_LIAn embedding-based indicator of parent protein location improves prediction of neoepitope immunogenicity and immunotherapy response C_LIO_LINeoepitope location improves estimation of effective neoantigen burden and stratification of potential for immunotherapy response C_LI

bioinformatics↗