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Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes

BACKGROUND: Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized...

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Autores principales: Walshe, Valerie A., Hattotuwagama, Channa K., Doytchinova, Irini A., Wong, MaiLee, Macdonald, Isabel K., Mulder, Arend, Claas, Frans H. J., Pellegrino, Pierre, Turner, Jo, Williams, Ian, Turnbull, Emma L., Borrow, Persephone, Flower, Darren R.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779488/
https://www.ncbi.nlm.nih.gov/pubmed/19956609
http://dx.doi.org/10.1371/journal.pone.0008095
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author Walshe, Valerie A.
Hattotuwagama, Channa K.
Doytchinova, Irini A.
Wong, MaiLee
Macdonald, Isabel K.
Mulder, Arend
Claas, Frans H. J.
Pellegrino, Pierre
Turner, Jo
Williams, Ian
Turnbull, Emma L.
Borrow, Persephone
Flower, Darren R.
author_facet Walshe, Valerie A.
Hattotuwagama, Channa K.
Doytchinova, Irini A.
Wong, MaiLee
Macdonald, Isabel K.
Mulder, Arend
Claas, Frans H. J.
Pellegrino, Pierre
Turner, Jo
Williams, Ian
Turnbull, Emma L.
Borrow, Persephone
Flower, Darren R.
author_sort Walshe, Valerie A.
collection PubMed
description BACKGROUND: Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102. METHODOLOGY/FINDINGS: Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1. CONCLUSIONS/SIGNIFICANCE: A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.
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spelling pubmed-27794882009-12-03 Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes Walshe, Valerie A. Hattotuwagama, Channa K. Doytchinova, Irini A. Wong, MaiLee Macdonald, Isabel K. Mulder, Arend Claas, Frans H. J. Pellegrino, Pierre Turner, Jo Williams, Ian Turnbull, Emma L. Borrow, Persephone Flower, Darren R. PLoS One Research Article BACKGROUND: Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102. METHODOLOGY/FINDINGS: Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1. CONCLUSIONS/SIGNIFICANCE: A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology. Public Library of Science 2009-11-30 /pmc/articles/PMC2779488/ /pubmed/19956609 http://dx.doi.org/10.1371/journal.pone.0008095 Text en Walshe et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Walshe, Valerie A.
Hattotuwagama, Channa K.
Doytchinova, Irini A.
Wong, MaiLee
Macdonald, Isabel K.
Mulder, Arend
Claas, Frans H. J.
Pellegrino, Pierre
Turner, Jo
Williams, Ian
Turnbull, Emma L.
Borrow, Persephone
Flower, Darren R.
Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes
title Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes
title_full Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes
title_fullStr Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes
title_full_unstemmed Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes
title_short Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes
title_sort integrating in silico and in vitro analysis of peptide binding affinity to hla-cw*0102: a bioinformatic approach to the prediction of new epitopes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779488/
https://www.ncbi.nlm.nih.gov/pubmed/19956609
http://dx.doi.org/10.1371/journal.pone.0008095
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