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Dissertation/Thesis Abstract

A Model of Antigen Processing Improves Prediction of MHC Class I-Presented Peptides
by O'Donnell, Timothy John, Ph.D., Icahn School of Medicine at Mount Sinai, 2020, 127; 28092843
Abstract (Summary)

T cells recognize peptides presented by major histocompatibility complex (MHC) proteins on cell surfaces. Computational prediction of MHC-presented peptides is an essential tool for epitope mapping and vaccine design. In this dissertation, I introduce improved predictors of peptide presentation on MHC class I. The predictors are fit to published datasets of MHC-presented peptides identified by mass spectrometry, as well as other sources. Separate models are developed for predicting MHC/peptide binding and the antigen processing steps that occur prior to MHC binding in vivo. I show that a combination model incorporating these two components achieves higher accuracy than existing methods at predicting MHC presentation, as well as neoantigens recognized by CD8+ T cells from cancer patients. The new methods are made available as an open source software package called MHCflurry (https://github.com/openvax/mhcflurry).

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Indexing (document details)
Advisor: Laserson, Uri
Commitee: Faith, Jeremiah, Horowitz, Amir, Rosenberg, Brad R., Greenbaum, Benjamin
School: Icahn School of Medicine at Mount Sinai
Department: Immunology
School Location: United States -- New York
Source: DAI-B 82/3(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Immunology, Bioinformatics, Cellular biology, Pharmaceutical sciences
Keywords: Antigen presentation, Antigen processing, HLA, Major histocompatibility complex, T cell, Vaccine design
Publication Number: 28092843
ISBN: 9798664795721
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