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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.

Original publication

DOI

10.1371/journal.pone.0008095

Type

Journal article

Journal

PLoS One

Publication Date

30/11/2009

Volume

4

Keywords

Alleles, Amino Acid Motifs, Computational Biology, Edetic Acid, Epitopes, HIV-1, HLA-C Antigens, Histocompatibility Antigens Class I, Humans, In Vitro Techniques, Leukocytes, Mononuclear, Major Histocompatibility Complex, Models, Statistical, Peptides, Protein Binding, Protein Structure, Tertiary