Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2

doi:10.1101/2020.05.14.095885

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[Preprint]. 2020 May 15:2020.05.14.095885.

doi: 10.1101/2020.05.14.095885.

Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2

Ang Gao^{1

2}, Zhilin Chen³, Florencia Pereyra Segal⁴, Mary Carrington^{3

5}, Hendrik Streeck⁶, Arup K Chakraborty^{1

3

2

7

8}, Boris Julg³

Affiliations

¹ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
² Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139, USA.
³ Ragon Insitute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, USA.
⁴ Brigham and Women's Hospital, Boston, MA 02115, USA.
⁵ Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
⁶ Institut für Virologie, Universitätsklinikum Bonn, 53127 Bonn, Germany.
⁷ Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁸ Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

PMID: 32511339
PMCID: PMC7241102
DOI: 10.1101/2020.05.14.095885

Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2

Ang Gao et al. bioRxiv. 2020.

[Preprint]. 2020 May 15:2020.05.14.095885.

doi: 10.1101/2020.05.14.095885.

Authors

Ang Gao^{1

2}, Zhilin Chen³, Florencia Pereyra Segal⁴, Mary Carrington^{3

5}, Hendrik Streeck⁶, Arup K Chakraborty^{1

3

2

7

8}, Boris Julg³

Affiliations

¹ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
² Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139, USA.
³ Ragon Insitute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, USA.
⁴ Brigham and Women's Hospital, Boston, MA 02115, USA.
⁵ Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
⁶ Institut für Virologie, Universitätsklinikum Bonn, 53127 Bonn, Germany.
⁷ Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁸ Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

PMID: 32511339
PMCID: PMC7241102
DOI: 10.1101/2020.05.14.095885

Update in

Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2.
Gao A, Chen Z, Amitai A, Doelger J, Mallajosyula V, Sundquist E, Pereyra Segal F, Carrington M, Davis MM, Streeck H, Chakraborty AK, Julg B. Gao A, et al. iScience. 2021 Apr 23;24(4):102311. doi: 10.1016/j.isci.2021.102311. Epub 2021 Mar 15. iScience. 2021. PMID: 33748696 Free PMC article.

Abstract

We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic T Lymphocyte (CTL) epitopes derived from diverse pathogens, given a Human Leukocyte Antigen (HLA) genotype. The model was trained and tested on experimental data on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. The method is more accurate than publicly available models. Our model predicts that only a fraction of SARS-CoV-2 epitopes that have been predicted to bind to HLA molecules is immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but the immunogenic epitopes in the SARS-CoV-2 spike protein alone are unlikely to do so. Our model predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to those contained in low-pathogenicity coronaviruses circulating in the population. Thus, we suggest that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.

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Figures

**Fig. 1.**
The ROC curve of the binary classifier based on our model (red), compared with the model developed by Calis et al (Calis et al., 2013) (green). (A) shows the ROC curves for the acute HIV infection group. The AUC of the red curve is 0.71. The AUC of the green curve is 0.57. (B) shows the ROC curves for the chronic HIV infection group. The AUC of the red curve is 0.66. The AUC of the green curve is 0.34.

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Cited by

Identification of immunodominant linear epitopes from SARS-CoV-2 patient plasma.
Farrera-Soler L, Daguer JP, Barluenga S, Vadas O, Cohen P, Pagano S, Yerly S, Kaiser L, Vuilleumier N, Winssinger N. Farrera-Soler L, et al. PLoS One. 2020 Sep 9;15(9):e0238089. doi: 10.1371/journal.pone.0238089. eCollection 2020. PLoS One. 2020. PMID: 32903266 Free PMC article.

References

1. Abdul-Jawad S. et al. (2016) ‘Increased valency of conserved-mosaic vaccines enhances the breadth and depth of Epitope recognition’, Molecular Therapy. American Society of Gene & Cell Therapy, 24(2), pp. 375–384. doi: 10.1038/mt.2015.210 - DOI - PMC - PubMed
1. Ahmed S. F. et al. (2019) ‘Sub-dominant principal components inform new vaccine targets for HIV Gag’, Bioinformatics, 35(20), pp. 3884–3889. doi: 10.1093/bioinformatics/btz524 - DOI - PubMed
1. Ahmed S. F., Quadeer A. A. and McKay M. R. (2020) ‘Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies’, Viruses, 12(3), p. 254. doi: 10.3390/v12030254 - DOI - PMC - PubMed
1. Akst J. (2020) ‘COVID-19 Vaccine Frontrunners’, TheScientist. doi: https://www.the-scientist.com/news-opinion/covid-19-vaccine-frontrunners....
1. Barton J. P. et al. (2016) ‘Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable’, Nature Communications. Nature Publishing Group, 7(May), pp. 1–10. doi: 10.1038/ncomms11660 - DOI - PMC - PubMed

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[1] Abdul-Jawad S. et al. (2016) ‘Increased valency of conserved-mosaic vaccines enhances the breadth and depth of Epitope recognition’, Molecular Therapy. American Society of Gene & Cell Therapy, 24(2), pp. 375–384. doi: 10.1038/mt.2015.210 - DOI - PMC - PubMed

[2] Abdul-Jawad S. et al. (2016) ‘Increased valency of conserved-mosaic vaccines enhances the breadth and depth of Epitope recognition’, Molecular Therapy. American Society of Gene & Cell Therapy, 24(2), pp. 375–384. doi: 10.1038/mt.2015.210 - DOI - PMC - PubMed

[3] Ahmed S. F. et al. (2019) ‘Sub-dominant principal components inform new vaccine targets for HIV Gag’, Bioinformatics, 35(20), pp. 3884–3889. doi: 10.1093/bioinformatics/btz524 - DOI - PubMed

[4] Ahmed S. F. et al. (2019) ‘Sub-dominant principal components inform new vaccine targets for HIV Gag’, Bioinformatics, 35(20), pp. 3884–3889. doi: 10.1093/bioinformatics/btz524 - DOI - PubMed

[5] Ahmed S. F., Quadeer A. A. and McKay M. R. (2020) ‘Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies’, Viruses, 12(3), p. 254. doi: 10.3390/v12030254 - DOI - PMC - PubMed

[6] Ahmed S. F., Quadeer A. A. and McKay M. R. (2020) ‘Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies’, Viruses, 12(3), p. 254. doi: 10.3390/v12030254 - DOI - PMC - PubMed

[7] Akst J. (2020) ‘COVID-19 Vaccine Frontrunners’, TheScientist. doi: https://www.the-scientist.com/news-opinion/covid-19-vaccine-frontrunners....

[8] Akst J. (2020) ‘COVID-19 Vaccine Frontrunners’, TheScientist. doi: https://www.the-scientist.com/news-opinion/covid-19-vaccine-frontrunners....

[9] Barton J. P. et al. (2016) ‘Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable’, Nature Communications. Nature Publishing Group, 7(May), pp. 1–10. doi: 10.1038/ncomms11660 - DOI - PMC - PubMed

[10] Barton J. P. et al. (2016) ‘Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable’, Nature Communications. Nature Publishing Group, 7(May), pp. 1–10. doi: 10.1038/ncomms11660 - DOI - PMC - PubMed

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This is a preprint.

Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2

Affiliations

Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2

Authors

Affiliations

Update in

Abstract

Figures

Similar articles

Cited by

References

Publication types

Grants and funding

LinkOut - more resources

Full Text Sources

Research Materials

Miscellaneous