Determining and characterizing immunological self/non-self
type: CreativeWork
description: The immune system has the ability to discriminate self from non-self proteins and also make appropriate immune responses to pathogens. A fundamental problem is to understand the genomic differences and similarities among the sets of self peptides and non-self peptides. The sequencing of human, mouse and numerous pathogen genomes and cataloging of their respective proteomes allows host self and non-self peptides to be identified. T-cells make this determination at the peptide level based on peptides displayed by MHC molecules.In this project, peptides of specific lengths (k-mers) are generated from each protein in the proteomes of various model organisms. The set of unique k-mers for each species is stored in a library and defines its "immunological self". Using the libraries, organisms can be compared to determine the levels of peptide overlap. The observed levels of overlap can also be compared with levels which can be expected "at random" and statistical conclusions drawn.A problem with this procedure is that sequence information in public protein databases (Swiss-PROT, UniProt, PIR) often contains ambiguities. Three strategies for dealing with such ambiguities have been explored in earlier work and the strategy of removing ambiguous k-mers is used here.Peptide fragments (k-mers) which elicit immune responses are often localized within the sequences of proteins from pathogens. These regions are known as "immunodominants" (i.e., hot spots) and are important in immunological work. After investigating the peptide universes and their overlaps, the question of whether known regions of immunological significance (e.g., epitope) come from regions of low host-similarity is explored. The known regions of epitopes are compared with the regions of low host-similarity (i.e., non-overlaps) between HIV-1 and human proteomes at the 7-mer level. Results show that the correlation between these two regions is not statistically significant. In addition, pairs involving human and hum…
abstract: The immune system has the ability to discriminate self from non-self proteins and also make appropriate immune responses to pathogens. A fundamental problem is to understand the genomic differences and similarities among the sets of self peptides and non-self peptides. The sequencing of human, mouse and numerous pathogen genomes and cataloging of their respective proteomes allows host self and non-self peptides to be identified. T-cells make this determination at the peptide level based on peptides displayed by MHC molecules.In this project, peptides of specific lengths (k-mers) are generated from each protein in the proteomes of various model organisms. The set of unique k-mers for each species is stored in a library and defines its "immunological self". Using the libraries, organisms can be compared to determine the levels of peptide overlap. The observed levels of overlap can also be compared with levels which can be expected "at random" and statistical conclusions drawn.A problem with this procedure is that sequence information in public protein databases (Swiss-PROT, UniProt, PIR) often contains ambiguities. Three strategies for dealing with such ambiguities have been explored in earlier work and the strategy of removing ambiguous k-mers is used here.Peptide fragments (k-mers) which elicit immune responses are often localized within the sequences of proteins from pathogens. These regions are known as "immunodominants" (i.e., hot spots) and are important in immunological work. After investigating the peptide universes and their overlaps, the question of whether known regions of immunological significance (e.g., epitope) come from regions of low host-similarity is explored. The known regions of epitopes are compared with the regions of low host-similarity (i.e., non-overlaps) between HIV-1 and human proteomes at the 7-mer level. Results show that the correlation between these two regions is not statistically significant. In addition, pairs involving human and hum…
author: Li, Ying
language: en_US
keywords: Locality Clustering, Proteome, Similarity, Self/Non-self
date published: 2007-02-15
date modified: 2026-07-24T04:26:59Z
provider: University of Saskatchewan
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Saskatchewan

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10388/etd-02152007-125646