Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk.

The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.

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 characterization of proteins/peptides and to verify whether it is possible to predict the retention behavior of peptides based on their properties. As models, the five most abundant milk proteins α-lactalbumin, ß-lactoglobulin, α-, ß-, and κ-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 deviations 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 separation, 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 establishing correlations between the properties of the peptides and their retardation factors, 51 of 85 selected 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 (α = 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 retardation 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 corresponding 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.