{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/130782"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/130782","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Understanding the effects of higher order sequence features on peptide MHC binding","abstract":"Understanding the factors that contribute to peptide-MHC (pMHC) affinity is critical for the study of immune responses and the development of novel therapeutics. In this thesis we propose the use of sequence feature representations as a means of capturing and categorizing these factors, and we develop the theoretical framework and justification for their use. We then apply sequence feature representations to analyze data derived from yeast display platforms, which enable the collection of pMHC binding data for vast libraries of peptides. 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