Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Biological and Environmental Sciences


Cell and Molecular Biology (CMB)


Cell & Molecular Biology

First Advisor

Anne S. De Groot


Immunoinformatics tools have multiple applications in human immunology research. One of their most prominent applications is the prediction of T cell epitopes to accelerate vaccine development. T cell epitopes are short peptides derived from pathogens that are recognized by the cells of the immune system (T cells) when presented on the surface of cells bound to a major histocompatibility complex (MHC) molecule, which results in a specific immune response. For their role as key drivers of the immune response, numerous algorithms have been developed to predict binding of peptides to MHC molecules; however, comparable tools are limited for other species. The goal of this thesis is to develop immunoinformatics tools for swine to aid in the design of vaccines for pathogens affecting the pork industry. One of the main reasons for the limited development of T cell epitope mapping tools for swine is the lack of data required to train and test the predictive algorithms. Through this research, we have developed PigMatrix, a tool for prediction of peptide binding to swine MHC molecules. In an initial analysis, PigMatrix predictive performance was favorable, in particular because its development did not require training data. Using PigMatrix, we have identified immunogenic peptides conserved in seven different strains of influenza A virus (IAV), a highly diverse virus that has a significant impact not only on swine, but also for humans. Protective potential of IAV vaccines is commonly predicted using genetic data and antibody cross-reactivity properties of the hemagglutinin (HA) surface protein, the most variable antigen and primary target of the antibody immune response. However, protection has been reported in the absence of cross-reactive antibodies to HA. To explore the role of T cell epitopes in vaccine protection, we have developed a method (EpiCC) to compare T cell epitope content between proteins. We found that the relationship of predicted T cell epitopes between HA sequences of a swine IAV inactivated vaccine and challenge strains was associated with protection, providing evidence that T cells contribute to vaccine efficacy. This approach may complement current methods for selection of influenza vaccines against novel viruses and influenza strains for vaccine development. Taken together, these findings demonstrate the potential of immunoinformatics tools for the development and evaluation of swine vaccines and will allow for further research to improve the tools and apply them to design novel vaccine candidates.



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