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

Abstract

<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