Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE205588 | GEO | 2023/04/06
REPOSITORIES: GEO
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Project description:CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC-II molecules. The high polymorphism of MHC-II genes represents an important hurdle towards accurate predictions of CD4+ T-cell epitopes in different individuals and different species. Here we generated and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to determine the binding motifs of 88 MHC-II alleles across human, mouse, cattle and chicken. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse binding mode in MHC-II ligands. We then developed a machine learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T-cell epitopes, as demonstrated by the identification of several viral and bacterial epitopes following the aforementioned reverse binding mode.
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Project description:CD8 T cells from human donors were sorted with peptide-MHC multimers based on SARS-Cov-2 epitopes. For each donor the alpha and beta chains were sequenced separately
2022-04-25 | GSE201212 | GEO
Project description:Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes
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Project description:CD8+ T cell immunity to SARS-CoV-2 has been implicated in COVID-19 severity and virus control. Here, we identified non-synonymous mutations in MHC-I restricted CD8+ T cell epitopes after deep sequencing of 747 SARS-CoV-2 virus isolates. Mutant peptides exhibited diminished or abrogated MHC-I binding, which was associated with a loss of recognition and functional responses by CD8+ T cells isolated from HLA-matched COVID-19 patients. Our findings highlight the capacity of SARS-CoV-2 to subvert CD8+ T cell surveillance through sporadically emerging escape mutations in MHC- I restricted viral epitopes.
2021-03-03 | GSE166651 | GEO
Project description:Improved predictions of immunogenicity reveal SARS-Cov-2 CD8 T-cell epitopes
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Project description:BackgroundCD (cluster of differentiation) 4+ T-cell responses to APOB (apolipoprotein B) are well characterized in atherosclerotic mice and detectable in humans. CD4+ T cells recognize antigenic peptides displayed on highly polymorphic HLA (human leukocyte antigen)-II. Immunogenicity of individual APOB peptides is largely unknown in humans. Only 1 HLA-II-restricted epitope was validated using the DRB1*07:01-APOB3036-3050 tetramer. We hypothesized that human APOB may contain discrete immunodominant CD4+ T-cell epitopes that trigger atherosclerosis-related autoimmune responses in donors with diverse HLA alleles.MethodsWe selected 20 APOB-derived peptides (APOB20) from an in silico screen and experimentally validated binding to the most commonly occurring human HLA-II alleles. We optimized a restimulation-based workflow to evaluate antigenicity of multiple candidate peptides in HLA-typed donors. This included activation-induced marker assay, intracellular cytokine staining, IFNγ (interferon gamma) enzyme-linked immunospot and cytometric bead array. High-throughput sequencing revealed TCR (T-cell receptor) clonalities of APOB-reactive CD4+ T cells.ResultsUsing stringent positive, negative, and crossover stimulation controls, we confirmed specificity of expansion-based protocols to detect CD4+ T cytokine responses to the APOB20 pool. Ex vivo assessment of AIM+CD4+ T cells revealed a statistically significant autoimmune response to APOB20 but not to a ubiquitously expressed negative control protein, actin. Resolution of CD4+ T responses to the level of individual peptides using IFNγ enzyme-linked immunospot led to the discovery of 6 immunodominant epitopes (APOB6) that triggered robust CD4+ T activation in most donors. APOB6-specific responding CD4+ T cells were enriched in unique expanded TCR clonotypes and preferentially expressed memory markers. Cytometric bead array analysis detected APOB6-induced secretion of both proinflammatory and regulatory cytokines. In clinical samples from patients with angiographically verified coronary artery disease, APOB6 stimulation induced higher activation and memory phenotypes and augmented secretion of proinflammatory cytokines TNF (tumor necrosis factor) and IFNγ, compared with patients with low coronary artery disease.ConclusionsUsing 3 cohorts, each with ≈20 donors, we discovered and validated 6 immunodominant, HLA-II-restricted APOB epitopes. The immune response to these APOB epitopes correlated with coronary artery disease severity.
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Project description:BackgroundMajor histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing.MethodsWe generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets.ResultsWe show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand.ConclusionsThe results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.
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Project description:We used customized peptide arrays of 163 TXNDC16 peptides to identify immunogenic TXNDC16 epitopes and asked whether discrimination of meningioma and control sera is possible with a specific subset selection of all immunogenic epitopes. CelluSpotsM-bM-^DM-" peptide arrays were manufactured by Intavis (Germany) with 163 TXNDC16 spanning peptides spotted as two replicate subarrays. 15-mer peptides overlapping by 10 amino acids were covalently bound to cellulose membranes with their C-termini. Please note that the non_normalized.txt contains Ch1 Mean values for all samples (each sequence represented in duplicates) and the identifiers in the 'index' column corresponds to those in the 'index' column in raw gpr files; sample data table contains classification values (for each sequence) described in the sample data processing field.
2014-07-01 | E-GEOD-58949 | biostudies-arrayexpress
Project description:In the past decade, the substantial achievements of therapeutic cancer vaccines have shed a new light on cancer immunotherapy. The major challenge for designing potent therapeutic cancer vaccines is to identify neoantigens capable of inducing sufficient immune responses, especially involving major histocompatibility complex (MHC)-II epitopes. However, most previous studies on T-cell epitopes were focused on either ligand binding or antigen presentation by MHC rather than the immunogenicity of T-cell epitopes. In order to better facilitate a therapeutic vaccine design, in this study, we propose a revolutionary new tool: a convolutional neural network model named FIONA (Flexible Immunogenicity Optimization Neural-network Architecture) trained on IEDB datasets. FIONA could accurately predict the epitopes presented by the given specific MHC-II subtypes, as well as their immunogenicity. By leveraging the human leukocyte antigen allele hierarchical encoding model together with peptide dense embedding fusion encoding, FIONA (with AUC = 0.94) outperforms several other tools in predicting epitopes presented by MHC-II subtypes in head-to-head comparison; moreover, FIONA has unprecedentedly incorporated the capacity to predict the immunogenicity of epitopes with MHC-II subtype specificity. Therefore, we developed a reliable pipeline to effectively predict CD4+ T-cell immune responses against cancer and infectious diseases.
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