Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction

Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules

T Cells Recognizing a Peptide Contaminant Undetectable by Mass Spectrometry

Synthetic peptides are widely used in immunological research as epitopes to stimulate their cognate T cells. These preparations are never completely pure, but trace contaminants are commonly revealed by mass spectrometry quality controls. In an effort to characterize novel major histocompatibility complex (MHC) Class I-restricted β-cell epitopes in non-obese diabetic (NOD) mice, we identified islet-infiltrating CD8+ T cells recognizing a contaminating peptide. The amount of this contaminant was so small to be undetectable by direct mass spectrometry. Only after concentration by liquid chromatography, we observed a mass peak corresponding to an immunodominant islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP)206-214 epitope described in the literature. Generation of CD8+ T-cell clones recognizing IGRP206-214 using a novel method confirmed the identity of the contaminant, further underlining the immunodominance of IGRP206-214. If left undetected, minute impurities in synthetic peptide preparations may thus give spurious results

ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping

<div><p>Identification of epitopes targeted by antibodies (B cell epitopes) is of critical importance for the development of many diagnostic and therapeutic tools. For clinical usage, such epitopes must be extensively characterized in order to validate specificity and to document potential cross-reactivity.</p><p>B cell epitopes are typically classified as either linear epitopes, i.e. short consecutive segments from the protein sequence or conformational epitopes adapted through native protein folding. Recent advances in high-density peptide microarrays enable high-throughput, high-resolution identification and characterization of linear B cell epitopes. Using exhaustive amino acid substitution analysis of peptides originating from target antigens, these microarrays can be used to address the specificity of polyclonal antibodies raised against such antigens containing hundreds of epitopes. However, the interpretation of the data provided in such large-scale screenings is far from trivial and in most cases it requires advanced computational and statistical skills. Here, we present an online application for automated identification of linear B cell epitopes, allowing the non-expert user to analyse peptide microarray data. The application takes as input quantitative peptide data of fully or partially substituted overlapping peptides from a given antigen sequence and identifies epitope residues (residues that are significantly affected by substitutions) and visualize the selectivity towards each residue by sequence logo plots. Demonstrating utility, the application was used to identify and address the antibody specificity of 18 linear epitope regions in Human Serum Albumin (HSA), using peptide microarray data consisting of fully substituted peptides spanning the entire sequence of HSA and incubated with polyclonal rabbit anti-HSA (and mouse anti-rabbit-Cy3). The application is made available at: <a href="http://www.cbs.dtu.dk/services/ArrayPitope" target="_blank">www.cbs.dtu.dk/services/ArrayPitope</a>.</p></div

Examples of selectivity profile of epitope.

<p>Logo-plot representations of the selectivity of three 15-residue regions of the primary sequence of HSA. Selectivity is determined by Dunnett’s test on all substitutions of target position, being represented in overlapping peptides. Each letter represents replacing amino acids and is scaled by log(<i>p</i>), where the p-value is obtained from the Dunnett’s test. Positive letters denotes substitution values above the global mean, <i>μ</i>_<i>g</i>, while negative letters denotes substitution values below <i>μ</i>_<i>g</i>. The absolute height of an entire column represents the average relative change in signal of all substitutions of target residue. The average signal of native 15-mer peptides containing the epitopes is (<b>A</b>) 489 Au, (<b>B</b>) 130 Au and (<b>C</b>) 176 Au.</p

Example of substitution matrix.

<p>Figure showing overlapping peptides (left) with the valine (V highlighted in bold) being analyzed for antibody selectivity. The box contains the substitution values, <i>x</i>_{<i>i</i>,<i>j</i>}, with columns representing the amino acid, <i>i</i>, which substitutes valine and rows representing the position, <i>j</i>, of the valine in the peptide (illustrated to the left) undergoing substitution. The mean substitution value, <i>μ</i>_<i>i</i>, of each replacing amino acid is shown in the bottom row. The global mean, <i>μ</i>_<i>g</i>, is calculated across all substitution values in the matrix.</p