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COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.
Front Immunol. 2020; 11:1581.FI

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 and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. 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 the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also 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.

Authors+Show Affiliations

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, United States.Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States. Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, United States.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Systematic Review

Language

eng

PubMed ID

32719684

Citation

Ong, Edison, et al. "COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning." Frontiers in Immunology, vol. 11, 2020, p. 1581.
Ong E, Wong MU, Huffman A, et al. COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning. Front Immunol. 2020;11:1581.
Ong, E., Wong, M. U., Huffman, A., & He, Y. (2020). COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning. Frontiers in Immunology, 11, 1581. https://doi.org/10.3389/fimmu.2020.01581
Ong E, et al. COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning. Front Immunol. 2020;11:1581. PubMed PMID: 32719684.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning. AU - Ong,Edison, AU - Wong,Mei U, AU - Huffman,Anthony, AU - He,Yongqun, Y1 - 2020/07/03/ PY - 2020/05/14/received PY - 2020/06/15/accepted PY - 2020/7/29/entrez PY - 2020/7/29/pubmed PY - 2020/8/4/medline KW - COVID-19 KW - S protein KW - machine learning KW - non-structural protein 3 KW - reverse vaccinology KW - vaccine KW - vaxign KW - vaxign-ML SP - 1581 EP - 1581 JF - Frontiers in immunology JO - Front Immunol VL - 11 N2 - 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 and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. 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 the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also 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. SN - 1664-3224 UR - https://www.unboundmedicine.com/medline/citation/32719684/COVID_19_Coronavirus_Vaccine_Design_Using_Reverse_Vaccinology_and_Machine_Learning_ L2 - https://doi.org/10.3389/fimmu.2020.01581 DB - PRIME DP - Unbound Medicine ER -