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IEDB-Predicted Epitopes from Grifoni et al 2020   (All Immunology tracks)

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Description

This composite track indicates the immune epitope predictions for B cells, CD4 T-cells and CD8 T-cells, using these software packages: B cells = BebiPred 2.0, CD4 = IEDB Tepitool, CD8 = NetMHCpan4.0EL

The color range for the markers is from dark blue (strong prediction) to dark red (weak prediction). Black is used for markers with no calculated prediction.

From the publication: Candidate targets for immune responses to 2019-Novel Coronavirus (nCoV): sequence homology- and bioinformatic-based predictions, full reference below.

The prediction of epitopes for CD8 T-cells was run for the following HLA alleles, as they have a worldwide population frequency > 6%

HLA allelesFrequency in worldwide population
HLA-A*01:0116.2
HLA-A*02:0125.2
HLA-A*03:0115.4
HLA-A*11:0112.9
HLA-A*23:016.4
HLA-A*24:0216.8
HLA-B*07:0213.3
HLA-B*08:0111.5
HLA-B*35:016.5
HLA-B*40:0110.3
HLA-B*44:029.2
HLA-B*44:037.6

Summary

We identified potential targets for immune responses to 2019-nCoV and provide essential information for understanding human immune responses to this virus and evaluation of diagnostic and vaccine candidates.

Abstract

Effective countermeasures against the recent emergence and rapid expansion of the 2019-Novel Coronavirus (2019-nCoV) require the development of data and tools to understand and monitor viral spread and immune responses. However, little information about the targets of immune responses to 2019-nCoV is available. We used the Immune Epitope Database and Analysis Resource (IEDB) resource to catalog available data related to other coronaviruses, including SARS-CoV, which has high sequence similarity to 2019-nCoV, and is the best-characterized coronavirus in terms of epitope responses. We identified multiple specific regions in 2019-nCoV that have high homology to SARS virus. Parallel bionformatic predictions identified a priori potential B and T cell epitopes for 2019-nCoV. The independent identification of the same regions using two approaches reflects the high probability that these regions are targets for immune recognition of 2019-nCoV.

Credits

Data collected by Arkal Arjun Rao for the Sgourakis Research Group, U.C. Santa Cruz

References

Grifoni A, Sidney J, Zhang Y, Scheuermann RH, Peters B, Sette A. Candidate targets for immune responses to 2019-Novel Coronavirus (nCoV): sequence homology- and bioinformatic-based predictions, BioRxiv 2020 (doi: https://doi.org/10.1101/2020.02.12.946087)