ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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An Application of Attention for the Prediction of TCR-Epitope Binding Affinity

Description
T-cells are an integral component of the immune system, enabling the body to distinguish between pathogens and the self. The primary mechanism which enables this is their T-cell receptors (TCR) which bind to antigen epitopes foreign to the body. This detection mechanism allows the T-cell to determine when an immune…

T-cells are an integral component of the immune system, enabling the body to distinguish between pathogens and the self. The primary mechanism which enables this is their T-cell receptors (TCR) which bind to antigen epitopes foreign to the body. This detection mechanism allows the T-cell to determine when an immune response is necessary. The computational prediction of TCR-epitope binding is important to researchers for both medical applications and for furthering their understanding of the biological mechanisms that impact immunity. Models which have been developed for this purpose fail to account for the interrelationships between amino acids and demonstrate poor out-of-sample performance. Small changes to the amino acids in these protein sequences can drastically change their structure and function. In recent years, attention-based deep learning models have shown success in their ability to learn rich contextual representations of data. To capture the contextual biological relationships between the amino acids, a multi-head self-attention model was created to predict the binding affinity between given TCR and epitope sequences. By learning the structural nuances of the sequences, this model is able to improve upon existing model performance and grant insights into the underlying mechanisms which impact binding.
ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis advisor) / Bang, Seojin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2021