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COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning

View ORCID ProfileEdison Ong, Mei U Wong, Anthony Huffman, View ORCID ProfileYongqun He
doi: https://doi.org/10.1101/2020.03.20.000141
Edison Ong
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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Mei U Wong
2Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
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Anthony Huffman
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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Yongqun He
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
2Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
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  • For correspondence: yongqunh@med.umich.edu
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Abstract

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign reverse vaccinology tool and the newly developed Vaxign-ML machine learning tool to predict COVID-19 vaccine candidates. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and linear B-cell epitopes localized in specific locations and functional domains of the protein. By applying reverse vaccinology and machine learning, we predicted potential vaccine targets for effective and safe COVID-19 vaccine development. We then propose that an “Sp/Nsp cocktail vaccine” containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

Footnotes

  • This revision has updated the abstract, introduction and discussion. In the revision, we propose and discuss a new strategy of developing an "Sp/Nsp cocktail vaccine" for COVID-19, which would include a structural protein(s) (Sp, such as spike protein) and a non-structural protein(s) (Nsp, such as nsp3 protein). This revision does not change all the figures, and only a minor change is made on Table 1.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 23, 2020.
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COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning
Edison Ong, Mei U Wong, Anthony Huffman, Yongqun He
bioRxiv 2020.03.20.000141; doi: https://doi.org/10.1101/2020.03.20.000141
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COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning
Edison Ong, Mei U Wong, Anthony Huffman, Yongqun He
bioRxiv 2020.03.20.000141; doi: https://doi.org/10.1101/2020.03.20.000141

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