Signals.

<p>Bar chart displaying the fluorescence signal obtained from binding of polyclonal anti-HSA to 15-mer peptides with 14 residue overlap (average signal from 5 copies of each peptide). <b>A:</b> peptides from HSA, <b>B:</b> peptides from BSA and <b>C:</b> peptides from RSA. The peptides are numbered on the x-axis according to the position of their n-terminal residue in the protein sequence. The y-axis denotes the average intensity of the signal (AU) after background subtraction.</p

All data: Binding of anti-HSA antibody to Array 2 (Identical to/Repeat of Array 1)

Datafile from peptide microarray measuring Human Serum Albumin(HSA) antibody binding to peptides from HSA and variations of these peptides

All data: Binding of anti-HSA antibody to Array 1

Datafile from peptide microarray measuring Human Serum Albumin(HSA) antibody binding to peptides from HSA and variations of these peptides

Identification and Mapping of Linear Antibody Epitopes in Human Serum Albumin Using High-Density Peptide Arrays

<div><p>We have recently developed a high-density photolithographic, peptide array technology with a theoretical upper limit of 2 million different peptides per array of 2 cm². Here, we have used this to perform complete and exhaustive analyses of linear B cell epitopes of a medium sized protein target using human serum albumin (HSA) as an example. All possible overlapping 15-mers from HSA were synthesized and probed with a commercially available polyclonal rabbit anti-HSA antibody preparation. To allow for identification of even the weakest epitopes and at the same time perform a detailed characterization of key residues involved in antibody binding, the array also included complete single substitution scans (i.e. including each of the 20 common amino acids) at each position of each 15-mer peptide. As specificity controls, all possible 15-mer peptides from bovine serum albumin (BSA) and from rabbit serum albumin (RSA) were included as well. The resulting layout contained more than 200.000 peptide fields and could be synthesized in a single array on a microscope slide. More than 20 linear epitope candidates were identified and characterized at high resolution i.e. identifying which amino acids in which positions were needed, or not needed, for antibody interaction. As expected, moderate cross-reaction with some peptides in BSA was identified whereas no cross-reaction was observed with peptides from RSA. We conclude that high-density peptide microarrays are a very powerful methodology to identify and characterize linear antibody epitopes, and should advance detailed description of individual specificities at the single antibody level as well as serologic analysis at the proteome-wide level.</p></div

Image of array.

<p>A small section of the peptide array used for identification and fine specificity mapping of HSA epitopes: The section shows approximately 300 of the total 220.428 peptide fields in this array. The peptides were synthesized in predefined, addressable fields generated by 2×2 mirrors on the DMD each measuring 10×10 µm resulting in peptide fields with the size 20×20 of µm. The peptide fields were spaced by 10 µm wide empty zones. Binding of polyclonal rabbit anti-HSA antibody to the fields was recorded by fluorescence microscopy after incubation with Cy3-conjugated goat anti-rabbit IgG.</p

Example of mean Rq-ratio.

<p>The figure shows two examples using the Rq-values. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068902#pone-0068902-g004" target="_blank">Figure 4a</a> shows residue 510–527 in the HSA sequence (top row) a sequence containing the epitope important residues LEVDETYV identified by Tukey’s HSD. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068902#pone-0068902-g004" target="_blank">Figure 4b</a> shows residue 317 to residue 338 containing the epitope important residues DEMPADLP-LAADFVESKD. The column below each HSA residue lists the Rq-values calculated in each of the 15 overlapping peptides in which the residue is represented. Rq values from peptides with no positions identified by Tukey’s HSD test are set as non significant (NS) in the figure. The color indicates the size of the Rq value; darker color indicates higher Rq values.</p

Example of Tukey’s HSD.

<p>The figure shows two examples example of epitope sequences identified using ANOVA followed by Tukey’s HSD post-hoc analysis. The leftmost column shows overlapping 15-mer peptides from HSA from position 510–527 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068902#pone-0068902-g003" target="_blank">figure 3A</a> and from position 309 to 333 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068902#pone-0068902-g003" target="_blank">figure 3b</a>. The rightmost column highlights the amino acids identified as being important for antibody binding (dashes indicate non-important positions). The important amino acids were determined on the p<0.01 level in the HSD post-hoc analysis.</p

Identification of optimal motif length using the <i>NNAlign</i> method.

<p><b>Left panel:</b> Histogram of the optimal motifs lengths for the 14 HLA-DR molecules in the Wang dataset as identified by the <i>NNAlign</i> method. Right panel: Predictive performance measured in terms of the root mean square error (RMSE) between observed and predicted values as a function of the motif length for the two molecules DRB1*0101 and DRB1*1501. <i>NNAlign</i> was trained using the same parameters settings described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026781#pone-0026781-g004" target="_blank">Figure 4</a>. At each motif length are shown the mean and standard error of the mean RMSE as estimated by bootstrap sampling. For DRB1*0101 a single consistent optimal motif length of 9 amino acids is found. For DRB1*1501 all motif length 8–11 had statistically indistinguishable performance (paired t-test).</p

Analysing high-density peptide array data with <i>NNAlign</i>.

<p>a) Fluorescence microscopy picture of a peptide microarray. The image is a magnified segment of the peptide chip used in the trypsin cleavage analysis. b) Trypsin peptide-chip data. The normalized observed (target) likelihood of cleavage as a function of the prediction score for the trypsin data set. Localizations of peptides containing the pairs of amino acids RP, RA or RR are highlighted in the plot. Proline (P) is known to prevent cleavage after arginine (R), whereas cleavage is observed with other amino acids such as R and A. c) Chymotrypsin peptide-chip data. Correlation plot between predicted and measured (target) data from the chymotrypsin data set. Values are binned by their x,y proximity, so that the scatterplot represents the density of data in each bin. <i>NNAlign</i> was trained with linear rescaling of the quantitative data, a motif length of 4 amino acids without inclusion of PFR encoding, Blosum encoding of peptide sequences, a combination of 3,7,15 hidden neurons, 10 initial seeds, 5-fold exhaustive cross-validation, training was stopped on the best test set performance.</p

Example of output from the <i>NNAlign</i> server trained on MHC class II binding data for allele HLA-DRB1*0101.

<p>Links on the results page (in pink) redirect to additional files and figures relevant for the analysis. Run ID is a sequential identifier for the current job, and Run Name a user-defined prefix that is added to all files of the run. The “view data distribution” link shows the transformation applied to the data in pre-processing, which can be either a linear or logarithmic transformation. In this case the method was trained with a motif length of 9, including a PFR of size 3 to both ends of the peptide, and encoding in the network input layer peptide length and PFR length. The hidden layer was made of a fixed number of 20 neurons. Peptides were presented to the networks using a Blosum encoding to account for amino acid similarity, for 500 hundred iterations per peptide without stopping on the best test set performance. At each cross-validation step, 10 networks were trained starting from 10 different initial configurations. The subsets for cross-validation were constructed using a Hobohm1 method that groups in the same subset sequences that align with more than 80% identity (thr = 0.8). The model can be downloaded to disk using the dedicated link, and can be resubmitted to <i>NNAlign</i> to find occurrences of the learned pattern in new data. The estimated performance of the trained method is expressed in terms of Root Mean Square Error, Pearson and Spearman correlation. A visual representation of the correlation can be obtained from the scatterplot of predicted versus observed values. The “complete alignment core” link allows downloading the prediction values in cross-validation for each peptide, and where the core was placed within the peptides. Next follows a section on the sequence logo, showing a logo representation of the binding motif learned by the network ensemble. If the relative option is selected, links to logos for the individual networks in the final ensemble are also listed here. Finally, if an evaluation set is uploaded, an additional section shows performance measures and core alignment for these data.</p