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MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A nove...

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Detalles Bibliográficos
Autores principales: Zhang, Guang Lan, Khan, Asif M., Srinivasan, Kellathur N., August, J. Thomas, Brusic, Vladimir
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160213/
https://www.ncbi.nlm.nih.gov/pubmed/15980449
http://dx.doi.org/10.1093/nar/gki452
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author Zhang, Guang Lan
Khan, Asif M.
Srinivasan, Kellathur N.
August, J. Thomas
Brusic, Vladimir
author_facet Zhang, Guang Lan
Khan, Asif M.
Srinivasan, Kellathur N.
August, J. Thomas
Brusic, Vladimir
author_sort Zhang, Guang Lan
collection PubMed
description MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets—termed T-cell epitope hotspots. MULTIPRED is available at .
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spelling pubmed-11602132005-06-29 MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides Zhang, Guang Lan Khan, Asif M. Srinivasan, Kellathur N. August, J. Thomas Brusic, Vladimir Nucleic Acids Res Article MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets—termed T-cell epitope hotspots. MULTIPRED is available at . Oxford University Press 2005-07-01 2005-06-27 /pmc/articles/PMC1160213/ /pubmed/15980449 http://dx.doi.org/10.1093/nar/gki452 Text en © The Author 2005. Published by Oxford University Press. All rights reserved
spellingShingle Article
Zhang, Guang Lan
Khan, Asif M.
Srinivasan, Kellathur N.
August, J. Thomas
Brusic, Vladimir
MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides
title MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides
title_full MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides
title_fullStr MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides
title_full_unstemmed MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides
title_short MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides
title_sort multipred: a computational system for prediction of promiscuous hla binding peptides
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160213/
https://www.ncbi.nlm.nih.gov/pubmed/15980449
http://dx.doi.org/10.1093/nar/gki452
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