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David K. Gifford. Dai, Zheng(Computer scientist)Massachusetts Institute of Technology. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2021-05-24T20:23:45Z 2021-05-24T20:23:45Z 2021 2021 https://hdl.handle.net/1721.1/130782 1252064138 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021 Cataloged from the official PDF version of thesis. Includes bibliographical references (pages 85-86). 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. Methods for interpreting data from these platforms are still at an early stage, so in this thesis we also develop an approach for extracting useful information from such data. We demonstrate that the resulting sequence feature representations accurately capture the kinetics underlying pMHC binding, can be used to predict pMHC binding well enough to rival the current state of the art, and can be interpreted to show that they correlate with our current structural understanding of pMHC complexes. by Zheng Dai. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 86 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Understanding the effects of higher order sequence features on peptide MHC binding Thesis application/pdf 2021-05-24T20:23:45Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2ee5beb5-799d-4245-9c9d-910388b0fd5a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Understanding the effects of higher order sequence features on peptide MHC binding</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <Authors> <Author> <DisplayName>Dai, Zheng(Computer scientist)Massachusetts Institute of Technology.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <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. Methods for interpreting data from these platforms are still at an early stage, so in this thesis we also develop an approach for extracting useful information from such data. We demonstrate that the resulting sequence feature representations accurately capture the kinetics underlying pMHC binding, can be used to predict pMHC binding well enough to rival the current state of the art, and can be interpreted to show that they correlate with our current structural understanding of pMHC complexes.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>