| Title: |
Understanding the effects of higher order sequence features on peptide MHC binding |
| Authors: |
Dai, Zheng(Computer scientist)Massachusetts Institute of Technology. |
| Contributors: |
David K. Gifford.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| Publisher Information: |
Massachusetts Institute of Technology |
| Publication Year: |
2021 |
| Collection: |
DSpace@MIT (Massachusetts Institute of Technology) |
| Subject Terms: |
Electrical Engineering and Computer Science |
| Description: |
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 |
| Document Type: |
thesis |
| File Description: |
86 pages; application/pdf |
| Language: |
English |
| Relation: |
https://hdl.handle.net/1721.1/130782 |
| Availability: |
https://hdl.handle.net/1721.1/130782 |
| Rights: |
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 |
| Accession Number: |
edsbas.16D21AFE |
| Database: |
BASE |