The HLA-II immunopeptidome of SARS-CoV-2

Weingarten-Gabbay, Shira; Chen, Da-Yuan; Sarkizova, Siranush; Taylor, Hannah B.; Gentili, Matteo; Hernandez, Gabrielle M.; Pearlman, Leah R.; Bauer, Matthew R.; Rice, Charles M.; Clauser, Karl R.; Hacohen, Nir; Carr, Steven A.; Abelin, Jennifer G.; Saeed, Mohsan; Sabeti, Pardis C.

doi:10.1016/j.celrep.2023.113596

Cited by 3 publications

(1 citation statement)

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“…In this regard, Mass Spectrometry (MS)based immunopeptidomics stands out as a powerful scalable method for the unbiased identification of HLA-associated peptides [17][18][19] . In fact, MS has been increasingly applied in recent years to characterize the SARS-CoV-2 immunopeptidome [20][21][22][23][24][25][26][27][28][29][30] . These efforts have not only revealed conventional SARS-CoV-2 T-cell epitopes but also uncovered unconventional epitopes, including those arising from noncanonical translation, often escaping detection by conventional epitope mapping approaches 22,23 .…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning-Enhanced Immunopeptidomics and SARS-CoV-2 Population-Scale Analyses Unveils Novel Antigenic Features for Next-Generation COVID-19 Vaccines

Caron,

Kovalchik,

Hamelin

et al. 2024

Preprint

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Next-generation T-cell-directed vaccines for COVID-19 aim to induce durable T-cell immunity against circulating and future hypermutated SARS-CoV-2 variants. Mass Spectrometry (MS)-based immunopeptidomics holds promise for guiding vaccine design, but computational challenges impede the precise and unbiased identification of conserved T-cell epitopes crucial for vaccines against rapidly mutating viruses. We introduce a computational framework and analysis platform integrating a novel machine learning algorithm, immunopeptidomics, intra-host data, epitope immunogenicity, and geo-temporal CD8+ T-cell epitope conservation analyses. Central to our approach is MHCvalidator, a novel artificial neural network algorithm enhancing MS-based immunopeptidomics sensitivity by modeling antigen presentation and sequence features. MHCvalidator identified a novel nonconventional SARS-CoV-2 T-cell epitope presented by B7 supertype molecules, originating from a +1-frameshift in a truncated Spike (S) antigen, supported by ribo-seq data. Intra-host analysis of SARS-CoV-2 proteomes from ~100,000 COVID-19 patients revealed a prevalent S antigen truncation in ~51% of cases, exposing a rich source of frameshifted viral antigens. Our framework includes EpiTrack, a new computational pipeline tracking global mutational dynamics of MHCvalidator-identified SARS-CoV-2 CD8+ epitopes from vaccine BNT162b4. While most vaccine-encoded CD8+ epitopes exhibit global conservation from January 2020 to October 2023, a highly immunodominant A*01-associated epitope, especially in hospitalized patients, undergoes substantial mutations in Delta and Omicron variants. Our approach unveils unprecedented SARS-CoV-2 T-cell epitopes, elucidates novel antigenic features, and underscores mutational dynamics of vaccine-relevant epitopes. The analysis platform is applicable to any viruses, and underscores the need for continual vigilance in T-cell vaccine development against the evolving landscape of hypermutating SARS-CoV-2 variants.

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Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning-Enhanced Immunopeptidomics and SARS-CoV-2 Population-Scale Analyses Unveils Novel Antigenic Features for Next-Generation COVID-19 Vaccines

Caron,

Kovalchik,

Hamelin

et al. 2024

Preprint

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ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis

Xu,

Hu,

Dong

et al. 2024

Nat Commun

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Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

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