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Predicting MHC class I epitopes in large datasets

BACKGROUND: Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of...

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Autores principales: Roomp, Kirsten, Antes, Iris, Lengauer, Thomas
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2836306/
https://www.ncbi.nlm.nih.gov/pubmed/20163709
http://dx.doi.org/10.1186/1471-2105-11-90
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author Roomp, Kirsten
Antes, Iris
Lengauer, Thomas
author_facet Roomp, Kirsten
Antes, Iris
Lengauer, Thomas
author_sort Roomp, Kirsten
collection PubMed
description BACKGROUND: Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening. RESULTS: We have examined the performance of four diverse MHC Class I prediction methods on comparatively large HLA-A and HLA-B allele peptide binding datasets extracted from the Immune Epitope Database and Analysis resource (IEDB). The chosen methods span a representative cross-section of available methodology for MHC binding predictions. Until the development of IEDB, such an analysis was not possible, as the available peptide sequence datasets were small and spread out over many separate efforts. We tested three datasets which differ in the IC(50 )cutoff criteria used to select the binders and non-binders. The best performance was achieved when predictions were performed on the dataset consisting only of strong binders (IC(50 )less than 10 nM) and clear non-binders (IC(50 )greater than 10,000 nM). In addition, robustness of the predictions was only achieved for alleles that were represented with a sufficiently large (greater than 200), balanced set of binders and non-binders. CONCLUSIONS: All four methods show good to excellent performance on the comprehensive datasets, with the artificial neural networks based method outperforming the other methods. However, all methods show pronounced difficulties in correctly categorizing intermediate binders.
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spelling pubmed-28363062010-03-11 Predicting MHC class I epitopes in large datasets Roomp, Kirsten Antes, Iris Lengauer, Thomas BMC Bioinformatics Research article BACKGROUND: Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening. RESULTS: We have examined the performance of four diverse MHC Class I prediction methods on comparatively large HLA-A and HLA-B allele peptide binding datasets extracted from the Immune Epitope Database and Analysis resource (IEDB). The chosen methods span a representative cross-section of available methodology for MHC binding predictions. Until the development of IEDB, such an analysis was not possible, as the available peptide sequence datasets were small and spread out over many separate efforts. We tested three datasets which differ in the IC(50 )cutoff criteria used to select the binders and non-binders. The best performance was achieved when predictions were performed on the dataset consisting only of strong binders (IC(50 )less than 10 nM) and clear non-binders (IC(50 )greater than 10,000 nM). In addition, robustness of the predictions was only achieved for alleles that were represented with a sufficiently large (greater than 200), balanced set of binders and non-binders. CONCLUSIONS: All four methods show good to excellent performance on the comprehensive datasets, with the artificial neural networks based method outperforming the other methods. However, all methods show pronounced difficulties in correctly categorizing intermediate binders. BioMed Central 2010-02-17 /pmc/articles/PMC2836306/ /pubmed/20163709 http://dx.doi.org/10.1186/1471-2105-11-90 Text en Copyright ©2010 Roomp et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research article
Roomp, Kirsten
Antes, Iris
Lengauer, Thomas
Predicting MHC class I epitopes in large datasets
title Predicting MHC class I epitopes in large datasets
title_full Predicting MHC class I epitopes in large datasets
title_fullStr Predicting MHC class I epitopes in large datasets
title_full_unstemmed Predicting MHC class I epitopes in large datasets
title_short Predicting MHC class I epitopes in large datasets
title_sort predicting mhc class i epitopes in large datasets
topic Research article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2836306/
https://www.ncbi.nlm.nih.gov/pubmed/20163709
http://dx.doi.org/10.1186/1471-2105-11-90
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