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

Date Published: November 30, 2009

Publisher: Public Library of Science

Author(s): Valerie A. Walshe, Channa K. Hattotuwagama, Irini A. Doytchinova, MaiLee Wong, Isabel K. Macdonald, Arend Mulder, Frans H. J. Claas, Pierre Pellegrino, Jo Turner, Ian Williams, Emma L. Turnbull, Persephone Borrow, Darren R. Flower, Mario A. Ostrowski.

Abstract: 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.

Partial Text: The products of the Major Histocompatibility Complex (MHC) play a fundamental role in regulating immune responses, modulating the functional development of lymphocyte subsets, the acquisition and maintenance of self-tolerance, and the activation state and responses of host immune defences. MHC class I molecules expressed on the cell surface report on the internal status of cells by presenting ligands for surveillance by CD8+ T cells, natural killer T (NKT) cells and natural Killer (NK) cells [1]. CD8+ T cells recognise antigen as short peptide fragments complexed with classical MHC class I molecules [2]. NK cells express a diverse array of receptors that interact with ligands including classical and non-classical MHC class I molecules, which exert positive and negative influences on their functions [3]. Human MHC class I molecules are both polygenic and highly polymorphic [4]. This increases the chance that every pathogen will contain many epitopes recognised by individuals within the population and places restraints on a pathogen’s ability to escape immune control.

In this paper we have used a potent combination of in silico prediction and in vitro verification to characterise the peptide binding specificity of the previously poorly- characterised human MHC molecule HLA-Cw*0102. A 2D-QSAR approach was used to model binding of nonameric peptides to HLA-Cw*0102 [19]. We defined positive and negative contributions made to HLA-Cw*0102 binding affinity by amino acids at all positions of the nonameric peptide sequence. This model was employed to predict four “optimised” HLA-Cw*0102-binding peptides, all of which showed a high affinity of binding to HLA-Cw*0102. Alanine-scanning was used to probe and weight the contributions to affinity of amino acids at each position of a HLA-Cw*0102-binding peptide. Furthermore, the importance of amino acids randomly allocated at each position of the peptide was explored by evaluating the experimentally-determined binding affinities of a set of sequence-distinct peptides defined by discrimination of binding and diversity analysis. Realising that prediction must be verified both in vitro and ex vivo, we also validated the utility of our approach by predicting HLA-Cw*0102-binding nonameric peptides in the HIV-1 sequence, one of which was confirmed to be an epitope recognised by virus-specific T cells in a subset of HIV-infected HLA-Cw*0102-positive individuals.



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