Two-dimensional high-performance thin-layer chromatography for the characterization of milk peptide properties and a prediction of the retention behavior - a proof-of-principle study

High-performance thin-layer chromatography (HPTLC) is a suitable method for the analysis of peptides and proteins due to a wide selection of stationary and mobile phases and various detection options. Especially, two-dimensional HPTLC (2D-HPTLC) enables a higher resolution compared to one-dimensional HPTLC in the separation of complex peptide mixtures. Similar to 2D electrophoresis, characteristic peptide patterns can be obtained, allowing a differentiation of ingredients based on varying protein origins. The aim of this study was to evaluate 2D-HPTLC with regard to its suitability for the characteriza-tion of proteins/peptides and to verify whether it is possible to predict the retention behavior of pep-tides based on their properties. As models, the five most abundant milk proteins alpha-lactalbumin, beta- lactoglobulin, alpha-, beta-, and kappa-Casein were used. In order to determine the repeatability of the peptide separation by 2D-HPTLC, each tryptic protein hydrolyzate was separated eight times. The standard de-viations of the retardation factors for the separated peptides varied between 1.0 and 11.1 mm for the x-coordinate and 0.5-7.3 mm for the y-coordinate. It was also shown that after the chromatographic sep-aration, peptides of the individual protein hydrolyzates were located in specific areas on the HPTLC plate, so that a clustering could be obtained for the whey proteins' as well as the caseins' hydrolyzates. For es-tablishing correlations between the properties of the peptides and their retardation factors, 51 of 85 se-lected peptides were identified by matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF-MS/MS). On this basis, statistically significant correlations (alpha = 0.05) between the retardation factors of the peptides and their isoelectric points, as well as the percentage of anionic and non-polar amino acids in the peptides were established. Finally, it was investigated, whether the re-tardation factors for peptides can be predicted on the basis of a linear regression of the percentage of non-polar amino acids in a peptide. For this purpose, a mixture of artifical (synthetic) peptides ( n = 14) was separated by 2D-HPTLC and the measured retardation factors were compared with the correspond-ing retardation factors calculated. Absolute deviations of 0.3-17.9 mm were obtained. In addition, the universal applicability of the method to other protein sources other than milk proteins (animal protein) was tested using a mixture of pea peptides (plant protein, n = 3) resulting in absolute deviations of 0.7-8.6 mm. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

HPTLC, especially 2D-HPTLC, is an efficient method for peptide and protein analysis, offering high resolution and various detection options. This study evaluated 2D-HPTLC for protein/peptide characterization, establishing correlations between peptide properties and their retention factors. Findings showed the potential for predicting peptide retention factors based on the percentage of non-polar amino acids.