A simple method of prediction of visibility of peptides in mass spectrometry with electrospray ionization

Abstract

A new method for selection of essential peptides applicable for protein detection and quantification analysis in the targeted positive electrospray mass spectrometry has been proposed. It is based on the prediction of the normalized abundance of the mass spectrometric peaks by using a linear regression model. This method has the following a priori restrictions: first selection of peptides must be arranged so that at pH 2.5 the tested peptides must be presented mainly as the 2⁺ and 3⁺ ions. Only peptides containing C-terminal lysine or arginine residues should be considered. The amino acid composition of the peptide, the peptide concentration, the ratio of the polar surface of peptide to common surface and ratio of the polar volume to the common volume are used as independent variables. Among several considered combinations of variables the best linear regression model had a determination coefficient in leave-one-out cross-validation procedure of 0.54. This model confidently discriminated peptides with high response ability and peptides with low response ability, and therefore it is applicable for selection of the most favorable peptides among peptides selected by means of simple criteria. This simple and fast screening method can be successfully applied to reduce the list of observed peptides.