High-throughput sequencing enhanced phage display enables the identification of patient-specific epitope motifs in serum

Phage display is a prominent screening technique with a multitude of applications including therapeutic antibody development and mapping of antigen epitopes. In this study, phages were selected based on their interaction with patient serum and exhaustively characterised by high-throughput sequencing. A bioinformatics approach was developed in order to identify peptide motifs of interest based on clustering and contrasting to control samples. Comparison of patient and control samples confirmed a major issue in phage display, namely the selection of unspecific peptides. The potential of the bioinformatic approach was demonstrated by identifying epitopes of a prominent peanut allergen, Ara h 1, in sera from patients with severe peanut allergy. The identified epitopes were confirmed by high-density peptide micro-arrays. The present study demonstrates that high-throughput sequencing can empower phage display by (i) enabling the analysis of complex biological samples, (ii) circumventing the traditional laborious picking and functional testing of individual phage clones and (iii) reducing the number of selection rounds.Fil: Christiansen, Anders. Technical University of Denmark; DinamarcaFil: Kringelum, Jens V.. Technical University of Denmark; DinamarcaFil: Hansen, Christian S.. Technical University of Denmark; DinamarcaFil: Bøgh, Katrine L.. Technical University of Denmark; DinamarcaFil: Sullivan, Eric. Roche Nimble Gen; Estados UnidosFil: Patel, Jigar. Roche Nimble Gen; Estados UnidosFil: Rigby, Neil M.. Institute of Food Research; Reino UnidoFil: Eiwegger, Thomas. Medical University of Vienna; AustriaFil: Szépfalusi, Zsolt. Medical University of Vienna; AustriaFil: Masi, Federico De. Technical University of Denmark; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Lund, Ole. Technical University of Denmark; DinamarcaFil: Dufva, Martin. Technical University of Denmark; Dinamarc

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

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

Example of target-specific positions of individual peptides.

<p>The table shows output of 33 overlapping peptides mapping the positions 56 to 88 of the HSA protein sequence. Positions identified as being important for binding (identified by the Dunnett’s test of complete single-amino acid substitutions at the p<0.0001 level) are highlighted whereas dashes indicate positions not involved in binding. The median signal of copies of the corresponding native peptide is shown.</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

Example of selectivity tables.

<p>Tables showing substitution values of residue 518D of human serum albumin against all 20 amino acids (columns) and being represented in 10 out of 15 positions of the overlapping peptides (rows). Entire blank rows depict the native residue being represented in peptides with no positive signal. Individual blank cells are missing data. The row <i>μ</i> shows the mean substitution value of the replacing amino acids. Here, cells are highlighted by the effect of substitution as a color gradient from red to green through white, where white corresponds to the global mean, <i>μ</i>_<i>g</i>, equal to 0.240.</p