(387al) Development of Computational and Experimental Tools for Understanding Immune Recognition

A significant goal in society is to advance the medical field to improve the overall health of the population. To advance this field, my PhD has focused on investigating understudied aspects of immune interactions with viral proteins. From my studies, I have obtained skills in analyzing large datasets, utilizing complex software, and developing research protocols. Post-graduation, I hope to utilize these skills to continue investigating different diseases that interact with the immune system, including cancers and autoimmune diseases.

In my first project, I developed a workflow for analyzing population-level changes in MHC Class II-mediated immune recognition, combining self-developed analysis with integrated software. T cells detect peptides presented on the surface of cells through Major Histocompatibility Complex (MHC) molecules, with each person possessing a unique set of these molecules. Through the designed workflow, the effects on each protein within the virus, allele predicted, ethnicity, and overall population level can be investigated. The general workflow for this study involves 1) identifying protein sequences at different times; 2) predict the MHC Class II binding using an available algorithm; 3) calculate change in binding between the wildtype virus and mutated virus; 4) include various weights to account for allele frequency for a given ethnicity, ethnicity frequency, and protein abundance. This analysis was performed on the 2009 H1N1 Influenza virus and the Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) virus, with the only difference in analysis occurred in step one. For SARS-CoV-2, the variants have been identified with clear mutations, allowing for the variant protein sequences to be easily identified. Consensus sequences were developed for the Influenza study by determining the most frequent amino acid sequence from aligning grouped sequences. During Project 1, I created a workflow that utilized several software tools, including Docker, ClustalOmega, and Oracle VirtualBox.

My second research focus is to enhance the understanding of antibody-peptide interactions for the classification of binding epitopes. Computational studies have demonstrated that most of the binding energy is caused by three to five residues. This has led to the hypothesis that an antibody could bind to a peptide with very few residues “specified” if they are within a correct configuration. Thus, our goal is to determine the minimum number of “specified” residues required for antibody binding by doing a modified alanine scanning experiment using an ELISA assay. As of the submission of the abstract, these experiments are being planned to be performed in the following months.

Research Interests

Immunology, viral diseases, cancer, and immune response