Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON

Poran, Asaf; Harjanto, Dewi; Malloy, Matthew; Rooney, Michael S.; Srinivasan, Lakshmi; Gaynor, Richard B.

doi:10.1101/2020.04.06.027805

Cited by 20 publications

(22 citation statements)

References 36 publications

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“…Predicted 9-11 mer epitopes from the following open reading frames (ORFs) of the SARS-CoV-2 proteome were included in the analysis: ORF 1ab, 3a, 6, 7a, 7b, 8, 9b, 10, 14, envelope (E), membrane (M), nucleoprotein (N) and spike (S) protein. In addition, SARS-CoV-2 epitopes that were predicted to be most immunogenic by the science community were included for analysis (Table S1) [48][49][50][51] .…”

Section: Sars-cov-2 Epitope Selection and Peptide Synthesismentioning

confidence: 99%

Identification and characterization of an immunodominant SARS-CoV-2-specific CD8 T cell response

Gangaev

Ketelaars

Isaeva

et al. 2020

Preprint

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Global efforts are ongoing to develop vaccines against SARS-CoV-2 causing COVID-19. While there is accumulating information on antibody responses against SARS-CoV-2, less is known about CD8 T-cell recognized SARS-CoV-2 epitopes and the functional state of SARS- CoV-2-specific CD8 T cells. To address these issues, we analyzed samples from 18 COVID- 19 patients for CD8 T-cell recognition of 500 peptide HLA class I complexes, restricted by 10 common HLA alleles. Several epitopes derived from ORF1ab were identified, including an immunodominant epitope restricted by HLA-A*01:01. The immunodominance was further supported by high TCR diversity within the CD8 T cells specific for this epitope. Noteworthy, the ORF1ab is not included in the majority of vaccine candidates in development, which may influence their clinical activity. In-depth characterization of identified SARS-CoV-2-specific CD8 T cell responses revealed a lack of cytokine production and a gene expression profile inhibiting T cell re-activation and migration while sustaining cell survival.

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Section: Sars-cov-2 Epitope Selection and Peptide Synthesismentioning

confidence: 99%

Identification and characterization of an immunodominant SARS-CoV-2-specific CD8 T cell response

Gangaev

Ketelaars

Isaeva

et al. 2020

Preprint

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“…These features confer unique properties to the epitope maps that underlie our epitope hotspot predictions and digital twin optimization. These properties differ from the SARS-CoV-2 epitope maps that have been reported in recent preprints since the outbreak of this virus, which mainly utilize predictions based on HLA binding [29][30][31].…”

Section: Discussionmentioning

confidence: 87%

“…Taken together, this supports the approach taken in this study, which is to map computationally, a broad epitope landscape across the global viral SARS-CoV-2 proteome, which includes integrated CD8, CD4 and B cell targets in the modeling. There has been some preliminary efforts submitted into preprint servers recently that describe epitope maps generated [29][30][31], however it appears that the emphasis in those approaches were based mostly on HLA binding. It is important to profile in whole viral proteome epitope screens, as carried out in this study using an extensive artificial intelligence platform, not only the candidates that may bind to the HLA molecule but also those CD8 epitopes that are naturally processed by the cell's antigen processing machinery, and presented on the surface of the infected host cells.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

Malone

Simovski²,

Moliné³

et al. 2020

Preprint

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The global population is at present suffering from a pandemic of Coronavirus disease 2019 , caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

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“…The data features both linear epitopes recognized by B-cell receptors or antibodies as well as information on conformational epitopes (Pinto et al, 2020;Yuan et al, 2020) (recorded in Crowd-sourced annotations track, data not shown). These tracks also display epitopes recognized by CD8-positive or CD4-positive T-cells when presented by HLA molecules on host cells (Grifoni et al, 2020a;Poran et al, 2020) . Where possible, the latter are organized according to the HLA allele of the host used in their presentation.…”

Section: Uniprot Protein Annotationsmentioning

confidence: 99%

The UCSC SARS-CoV-2 Genome Browser

Fernandes

Hinrichs

Clawson

et al. 2020

Preprint

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Background:Researchers are generating molecular data pertaining to the SARS-CoV-2 RNA genome and its proteins at an unprecedented rate during the COVID-19 pandemic. As a result, there is a critical need for rapid and continuously updated access to the latest molecular data in a format in which all data can be quickly cross-referenced and compared. We adapted our genome browser visualization tool to the viral genome for this purpose. Molecular data, curated from published studies or from database submissions, are mapped to the viral genome and grouped together into "annotation tracks" where they can be visualized along the linear map of the viral genome sequence and programmatically downloaded in standard format for analysis. Results:The UCSC Genome Browser for SARS-CoV-2 ( https://genome.ucsc.edu/covid19.html ) provides continuously updated access to the mutations in the many thousands of SARS-CoV-2 genomes deposited in GISAID and the international nucleotide sequencing databases, displayed alongside phylogenetic trees. These data are augmented with alignments of bat, pangolin, and other animal and human coronavirus genomes, including per-base evolutionary rate analysis. All available annotations are cross-referenced on the virus genome, including those from major databases (PDB, RFAM, IEDB, UniProt) as well as up-to-date individual results from preprints. Annotated data include predicted and validated immune epitopes, promising antibodies, RT-PCR and sequencing primers, CRISPR guides (from research, diagnostics, vaccines, and therapies), and points of interaction between human and viral genes. As a community resource, any user can add manual annotations which are quality checked and shared publicly on the browser the next day.

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Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON

Cited by 20 publications

References 36 publications

Identification and characterization of an immunodominant SARS-CoV-2-specific CD8 T cell response

Identification and characterization of an immunodominant SARS-CoV-2-specific CD8 T cell response

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

The UCSC SARS-CoV-2 Genome Browser

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