Abstract
Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user’s choice.
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Acknowledgments
SJG gratefully acknowledges Zoetis (Pfizer) Animal Health for funding the development of Vacceed through a PhD scholarship.
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Goodswen, S.J., Kennedy, P.J., Ellis, J.T. (2021). Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed. In: Pfeifer, B.A., Hill, A. (eds) Vaccine Delivery Technology. Methods in Molecular Biology, vol 2183. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0795-4_4
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DOI: https://doi.org/10.1007/978-1-0716-0795-4_4
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