Linked Life Data

15542373

Source:http://linkedlifedata.com/resource/pubmed/id/15542373

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2004-11-15
pubmed:abstractText
TEPITOPE is a prediction model that has been successfully applied to the in silico identification of T cell epitopes in the context of oncology, allergy, infectious diseases, and autoimmune diseases. Like most epitope prediction models, TEPITOPE's underlying algorithm is based on the prediction of HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epitopes. An important step in the design of subunit vaccines is the identification of promiscuous HLA-II ligands in sets of disease-specific gene products. TEPITOPE's user interface enables the systematic prediction of promiscuous peptide ligands for a broad range of HLA-binding specificity. We show how to apply the TEPITOPE prediction model to identify T cell epitopes, and provide both a road map and examples of its successful application.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1046-2023
pubmed:author
pubmed:issnType
Print
pubmed:volume
34
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
468-75
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE.
pubmed:affiliation
Section of Bioinformatics, Genetics and Genomics, Hoffmann-La Roche Inc., Nutley, New Jersey, USA.
pubmed:publicationType
Journal Article