A Dataset of Experimental HLA-B*2705 Peptide Binding Affinities

T-cell epitopes form the basis of many vaccines, diagnostics, and reagents. Current methods for the in silico identification of T-cell epitopes rely, in the main, on the accurate quantitative prediction of peptide-Major Histocompatibility Complex (pMHC) affinity using data-driven computational approaches. Here, we describe a dataset of experimentally determined pMHC binding affinities for the problematic human class I allele HLA-B*2705. Using an in-house, FACS-based, MHC stabilization assay, we measured binding of 223 peptides. This dataset includes both nonbinding and binding peptides, with measured affinities (expressed as of the half-maximal binding level) ranging from 1.2 to 7.4. This dataset should provide a useful independent benchmark for new and existing methods for predicting peptide binding to HLA-B*2705. 1. Introduction Products of the Major Histocompatibility Complex (MHC) play a fundamental role in regulating immune responses. T cells recognise antigen as peptide fragments bound by MHC molecules, a process requiring initial antigen degradation through complex proteolytic digestion prior to formation of a binary complex. The biological role of MHC proteins is thus to bind peptides and “present” these at the cell surface for inspection by T-cell antigen receptors (TCRs) [1]. Class I molecules are composed of a heavy chain in complex with β2-microglobulin. The MHC-peptide-binding site consists of a β-sheet, forming the base, flanked by two α-helices, which together form a narrow cleft or groove accommodating bound peptides. The principal difference between class I and class II MHCs is the structure of the peptide binding groove: this is constrained to bind 8–11 amino acid peptides in class I, although it has become clear recently that much longer peptides can also bind to class I MHCs [2]. Predictive models of peptide-MHC binding affinity have become important components of modern computational immunovaccinology [3]. Previously, such approaches have been built around relatively uncomplicated classification methods but have now largely given way to quantitative regression-based approaches [4]. Immunoinformatics, a newly emergent subdiscipline of bioinformatics, which addresses informatic problems within immunology, uses QSAR technology to tackle the crucial issue of epitope prediction [5]. As high-throughput biology reveals the genomic sequences of pathogenic bacteria, viruses, and parasites, such prediction will become increasingly important in the postgenomic discovery of novel vaccines, reagents, and diagnostics. In order to better