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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.

Walshe, VA; Hattotuwagama, CK; Wong, M; Doytchinova, IA; Macdonald, IK; Mulder, A; Claas, FHJ; ... Flower, DR; + view all (2009) Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes. PLoS One , 4 , Article e8095. 10.1371/journal.pone.0008095. Green open access

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Abstract

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.

Type: Article
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.
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0008095
Publisher version: http://dx.doi.org/10.1371/journal.pone.0008095
Language: English
Additional information: � 2009 Walshe et al. 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 credited. This work was supported by the Biotechnology and Biological Sciences Research Council SRC Grant BB/D004020/1 and the Grand Challenges in Global Health Program of the Bill and Melinda Gates Foundation (#37384). No part of the work was supported by the University of Oxford. Early stages of the work were funded by The Edward Jenner Institute for Vaccine Research (EJIVR). The Edward Jenner Institute for Vaccine Research thanks its erstwhile charitable sponsors: the Medical Research Council, the Biotechnology and Biological Sciences Research Council, the Department of Health, and GlaxoSmithKline. These funding bodies had no role of any kind in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. GlaxoSmithKline did not directly fund the work described here, but did make an indirect charitable donation to the core funding of the EJIVR. PB and DRF received salary support from Senior Jenner Fellowships, and are both Jenner Institute Investigators. IKM and DRF drew additional support from the Wellcome Trust Grant WT079287MA.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute for Global Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute for Global Health > Infection and Population Health
URI: http://discovery.ucl.ac.uk/id/eprint/1356553
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