Dataset Information

Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

ABSTRACT: Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.

SUBMITTER: Wendorff M

PROVIDER: S-EPMC7438773 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

Wendorff Mareike M Garcia Alvarez Heli M HM Østerbye Thomas T ElAbd Hesham H Rosati Elisa E Degenhardt Frauke F Buus Søren S Franke Andre A Nielsen Morten M

Frontiers in immunology 20200805

Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unpr ...[more]

PMID: 32903714

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Project description:The major histocompatibility complex of class II (MHCII) immunopeptidome represents the repertoire of antigenic peptides with the potential to activate CD4+ T cells. An understanding of how the relative abundance of specific antigenic epitopes affects the outcome of T cell responses is an important aspect of adaptive immunity and offers a venue to more rationally tailor T cell activation in the context of disease. Recent advances in mass spectrometric instrumentation, computational power, labeling strategies, and software analysis have enabled an increasing number of stratified studies on HLA ligandomes, in the context of both basic and translational research. A key challenge in the case of MHCII immunopeptidomes, often determined for different samples at distinct conditions, is to derive quantitative information on consensus epitopes from antigenic peptides of variable lengths. Here, we present the design and benchmarking of a new algorithm [peptide landscape antigenic epitope alignment utility (PLAtEAU)] allowing the identification and label-free quantification (LFQ) of shared consensus epitopes arising from series of nested peptides. The algorithm simplifies the complexity of the dataset while allowing the identification of nested peptides within relatively short segments of protein sequences. Moreover, we apply this algorithm to the comparison of the ligandomes of cell lines with two different expression levels of the peptide-exchange catalyst HLA-DM. Direct comparison of LFQ intensities determined at the peptide level is inconclusive, as most of the peptides are not significantly enriched due to poor sampling. Applying the PLAtEAU algorithm for grouping of the peptides into consensus epitopes shows that more than half of the total number of epitopes is preferentially and significantly enriched for each condition. This simplification and deconvolution of the complex and ambiguous peptide-level dataset highlights the value of the PLAtEAU algorithm in facilitating robust and accessible quantitative analysis of immunopeptidomes across cellular contexts. In silico analysis of the peptides enriched for each HLA-DM expression conditions suggests a higher affinity of the pool of peptides isolated from the high DM expression samples. Interestingly, our analysis reveals that while for certain autoimmune-relevant epitopes their presentation increases upon DM expression others are clearly edited out from the peptidome.

Dataset Information

Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

Publications

Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

Similar Datasets

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