Abstract

The applicability of models to describe peptide retention in hydrophilic interaction liquid chromatography (HILIC) was investigated. A tryptic digest of bovine-serum-albumin (BSA) was used as a test sample. Several different models were considered, including adsorption, mixed-mode, exponential, quadratic and Neue–Kuss models. Gradient separations were performed on three different HILIC stationary-phases under three different mobile-phase conditions to obtain model parameters. Methods to track peaks for specific peptides across different chromatograms are shown to be essential. The optimal mobile-phase additive for the separation of BSA digest on each of the three columns was selected by considering the retention window, peak width and peak intensity with mass-spectrometric detection. The performance of the models was investigated using the Akaike information criterion (AIC) to measure the goodness-of-fit and evaluated using prediction errors. The F-test for regression was applied to support model selection. RPLC separations of the same sample were used to test the models. The adsorption model showed the best performance for all the HILIC columns investigated and the lowest prediction errors for two of the three columns. In most cases prediction errors were within 1%.

Highlights

  • Proteomics is a field comprising of different techniques used to identify and quantify the proteins present in cells, tissues and organisms [1]

  • We have investigated the retention of peptides in hydrophilic interaction liquid chromatography (HILIC) and we have explored five models to fit the data

  • The performance of the models was characterized by the Akaike information criterion (AIC) to determine the goodness of fit and evaluated using prediction errors

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Summary

Introduction

Proteomics is a field comprising of different techniques used to identify and quantify the proteins present in cells, tissues and organisms [1]. The identification and quantification is challenging, due to the high complexity of the sample, especially in bottom-up proteomics, and the great differences in the relative abundance of proteins in a cell proteome [4]. MS alone cannot be used to analyse complex samples, such as whole-cell lysates. For this reason, separation techniques are typically coupled to MS analysis, providing the much needed simplification of the sample prior to its introduction into the MS

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