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T-Epitope Designer: A HLA-peptide binding prediction server

The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. C...

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Detalles Bibliográficos
Autores principales: Kangueane, Pandjassarame, Sakharkar, Meena Kishore
Formato: Texto
Lenguaje:English
Publicado: Biomedical Informatics Publishing Group 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1891623/
https://www.ncbi.nlm.nih.gov/pubmed/17597847
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author Kangueane, Pandjassarame
Sakharkar, Meena Kishore
author_facet Kangueane, Pandjassarame
Sakharkar, Meena Kishore
author_sort Kangueane, Pandjassarame
collection PubMed
description The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. Consequently, we have developed a web server named T-EPITOPE DESIGNER to facilitate HLA-peptide binding prediction. The prediction server is based on a model that defines peptide binding pockets using information gleaned from X-ray crystal structures of HLA-peptide complexes, followed by the estimation of peptide binding to binding pockets. Thus, the prediction server enables the calculation of peptide binding to HLA alleles. This model is superior to many existing methods because of its potential application to any given HLA allele whose sequence is clearly defined. The web server finds potential application in T cell epitope vaccine design. AVAILABILITY: http://www.bioinformation.net/ted/
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spelling pubmed-18916232007-06-27 T-Epitope Designer: A HLA-peptide binding prediction server Kangueane, Pandjassarame Sakharkar, Meena Kishore Bioinformation Web Server The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. Consequently, we have developed a web server named T-EPITOPE DESIGNER to facilitate HLA-peptide binding prediction. The prediction server is based on a model that defines peptide binding pockets using information gleaned from X-ray crystal structures of HLA-peptide complexes, followed by the estimation of peptide binding to binding pockets. Thus, the prediction server enables the calculation of peptide binding to HLA alleles. This model is superior to many existing methods because of its potential application to any given HLA allele whose sequence is clearly defined. The web server finds potential application in T cell epitope vaccine design. AVAILABILITY: http://www.bioinformation.net/ted/ Biomedical Informatics Publishing Group 2005-05-15 /pmc/articles/PMC1891623/ /pubmed/17597847 Text en © 2005 Biomedical Informatics Publishing Group This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Web Server
Kangueane, Pandjassarame
Sakharkar, Meena Kishore
T-Epitope Designer: A HLA-peptide binding prediction server
title T-Epitope Designer: A HLA-peptide binding prediction server
title_full T-Epitope Designer: A HLA-peptide binding prediction server
title_fullStr T-Epitope Designer: A HLA-peptide binding prediction server
title_full_unstemmed T-Epitope Designer: A HLA-peptide binding prediction server
title_short T-Epitope Designer: A HLA-peptide binding prediction server
title_sort t-epitope designer: a hla-peptide binding prediction server
topic Web Server
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1891623/
https://www.ncbi.nlm.nih.gov/pubmed/17597847
work_keys_str_mv AT kangueanepandjassarame tepitopedesignerahlapeptidebindingpredictionserver
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