Understanding the effects of higher order sequence features on peptide MHC binding

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Dai, Zheng(Computer scientist)Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• David K. Gifford.

• Electrical Engineering and Computer Science.

Statement dc: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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130782

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130782