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Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC

Peptide binding to major histocompatibility complex (MHC) molecules is the most selective requisite for T-cell recognition. Therefore, prediction of peptide–MHC binding is the main basis for anticipating T-cell epitopes. A very popular and accurate method to predict peptide–MHC binding is based on m...

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Detalles Bibliográficos
Autores principales: Molero-Abraham, Magdalena, Lafuente, Esther M., Reche, Pedro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122735/
https://www.ncbi.nlm.nih.gov/pubmed/25048133
http://dx.doi.org/10.1007/978-1-4939-1115-8_18
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author Molero-Abraham, Magdalena
Lafuente, Esther M.
Reche, Pedro
author_facet Molero-Abraham, Magdalena
Lafuente, Esther M.
Reche, Pedro
author_sort Molero-Abraham, Magdalena
collection PubMed
description Peptide binding to major histocompatibility complex (MHC) molecules is the most selective requisite for T-cell recognition. Therefore, prediction of peptide–MHC binding is the main basis for anticipating T-cell epitopes. A very popular and accurate method to predict peptide–MHC binding is based on motif-profiles and here we show how to make them using EPIMHC (http://imed.med.ucm.es/epimhc/). EPIMHC is a database of T-cell epitopes and MHC-binding peptides that unlike any related resource provides a framework for computational vaccinology. In this chapter, we describe how to derive peptide–MHC binding motif-profiles in EPIMHC and use them to predict peptide–MHC binding and T-cell epitopes. Moreover, we show evidence that customization of peptide–MHC binding predictors can lead to enhanced epitope predictions.
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spelling pubmed-71227352020-04-06 Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC Molero-Abraham, Magdalena Lafuente, Esther M. Reche, Pedro Immunoinformatics Article Peptide binding to major histocompatibility complex (MHC) molecules is the most selective requisite for T-cell recognition. Therefore, prediction of peptide–MHC binding is the main basis for anticipating T-cell epitopes. A very popular and accurate method to predict peptide–MHC binding is based on motif-profiles and here we show how to make them using EPIMHC (http://imed.med.ucm.es/epimhc/). EPIMHC is a database of T-cell epitopes and MHC-binding peptides that unlike any related resource provides a framework for computational vaccinology. In this chapter, we describe how to derive peptide–MHC binding motif-profiles in EPIMHC and use them to predict peptide–MHC binding and T-cell epitopes. Moreover, we show evidence that customization of peptide–MHC binding predictors can lead to enhanced epitope predictions. 2014-05-06 /pmc/articles/PMC7122735/ /pubmed/25048133 http://dx.doi.org/10.1007/978-1-4939-1115-8_18 Text en © Springer Science+Business Media New York 2014 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Molero-Abraham, Magdalena
Lafuente, Esther M.
Reche, Pedro
Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC
title Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC
title_full Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC
title_fullStr Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC
title_full_unstemmed Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC
title_short Customized Predictions of Peptide–MHC Binding and T-Cell Epitopes Using EPIMHC
title_sort customized predictions of peptide–mhc binding and t-cell epitopes using epimhc
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122735/
https://www.ncbi.nlm.nih.gov/pubmed/25048133
http://dx.doi.org/10.1007/978-1-4939-1115-8_18
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