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

BioRxiv : the Preprint Server for Biology
Edison OngYongqun He

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 protein, have been tested for vaccine development against SARS and MERS. We further used the Vaxign reverse vaccinology tool and the newly developed Vaxign-ML machine learning tool to predict COVID-19 vaccine candidates. The N protein was found to be conserved in the more pathogenic strains (SARS/MERS/COVID-19), but not in the other human coronaviruses that mostly cause 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 co...Continue Reading

Citations

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Datasets Mentioned

BETA
MN908947.3

Clinical Trials Mentioned

NCT04283461

Software Mentioned

Vaxign RV
PhyML
BepiPred
Vaxign
PyMol
Vaxign reverse
Vaxign analysis
reserve
MUSCLE
SEAVIEW

Related Concepts

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