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A peptide filtering relation quantifies MHC class I peptide optimization

Dalchau, Neil, Phillips, Andrew, Goldstein, Leonard D., Howarth, Mark, Cardelli, Luca, Emmott, Stephen, Elliott, Tim and Werner, Joern M. (2011) A peptide filtering relation quantifies MHC class I peptide optimization PLoS Computational Biology, 7, (10), e1002144-[14pp]. (doi:10.1371/journal.pcbi.1002144). (PMID:22022238).

Record type: Article

Abstract

Major Histocompatibility Complex (MHC) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells, thereby preventing disease progression. MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface. Competing fragments (peptides) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I, by a process known as peptide optimization. A better understanding of the optimization process is important for our understanding of immunodominance, the predominance of some T lymphocyte specificities over others, which can determine the efficacy of an immune response, the danger of immune evasion, and the success of vaccination strategies. In this paper we present a dynamical systems model of peptide optimization by MHC class I. We incorporate the chaperone molecule tapasin, which has been shown to enhance peptide optimization to different extents for different MHC class I alleles. Using a combination of published and novel experimental data to parameterize the model, we arrive at a relation of peptide filtering, which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates. From this relation, we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface, and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding. Importantly, our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles, providing a quantitative basis to investigate viral infection or disease at the cellular level. We exemplify this by simulating optimization of the distribution of peptides derived from Human Immunodeficiency Virus Gag-Pol polyprotein.

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Published date: 13 October 2011
Organisations: Cancer Sciences, Centre for Biological Sciences

Identifiers

Local EPrints ID: 337334
URI: http://eprints.soton.ac.uk/id/eprint/337334
ISSN: 1553-734X
PURE UUID: 8819bdf5-51fb-46ef-8fe9-e44872de5005
ORCID for Tim Elliott: ORCID iD orcid.org/0000-0003-1097-0222
ORCID for Joern M. Werner: ORCID iD orcid.org/0000-0002-4712-1833

Catalogue record

Date deposited: 24 Apr 2012 13:00
Last modified: 18 Jul 2017 06:03

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Contributors

Author: Neil Dalchau
Author: Andrew Phillips
Author: Leonard D. Goldstein
Author: Mark Howarth
Author: Luca Cardelli
Author: Stephen Emmott
Author: Tim Elliott ORCID iD
Author: Joern M. Werner ORCID iD

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