Undirected modifications between food proteins and secondary plant metabolites can occur during food processing. The results of covalent interactions can alter the functional and biological ...properties of the proteins. The present work studied the extent of which covalent conjugation of the bioactive metabolite benzyl isothiocyanate (BITC; a glucosinolate breakdown product) to the whey protein α-lactalbumin affects the protein's allergenicity. Additional to the immunological analysis of native untreated and BITC-modified α-lactalbumin, the analysis of antigenic properties of proteolytically digested protein derivatives was also performed by high performance thin layer chromatography and immunostaining. As a result of the chemical modifications, structural changes in the protein molecule affected the allergenic properties. In this process, epitopes are destroyed or inactivated, but at the same time, buried epitopes can be exposed or newly formed, so that the net effect was an increase in allergenicity, in this case. Results from the tryptic hydrolysis suggest that BITC conjugation sterically hindered the cleavage sites for the enzyme, resulting in reduced digestibility and allergenicity. Residual antigenicity can be still present as short peptide fragments that provide epitopes. The desire to make food safer for allergy sufferers and to protect sensitized individuals from an allergenic reaction makes it clear that the detection of food antigens is mandatory; especially by considering protein interactions.
•α-Lactalbumin was modified with benzyl isothiocyanate.•Tryptic hydrolyzates of modified α-lactalbumin were separated by 2D-HPTLC.•Chemical modification with benzyl isothiocyanate impaired tryptic ...hydrolysis.•Protein modifications were detected by comparison of 2D-HPTLC peptide patterns.
In complex food matrices, non-directed reactions between food proteins and secondary plant metabolites (SPM) are conceivable. In this study, the interaction between the bioactive metabolite from garden cress (Lepidium sativum) and selected Brassicaceae – benzyl isothiocyanate (BITC) – and the dairy protein α-lactalbumin (α-LA) was investigated. It was focused on monitoring the proteolytic degradation behaviour of unmodified and BITC-modified α-LA with two-dimensional high-performance thin-layer chromatography (2D-HPTLC). The two-dimensional approach of HPTLC offers high resolution in the separation of complex peptide mixtures and might enable differentiation of protein modifications. Based on the specific peptide patterns of native and modified peptides, conclusions can be drawn about differences in protein/peptide polarity, location of a modification, and digestibility. The aim was to characterize tryptically hydrolyzed unmodified and BITC-modified peptides using the 2D method and to investigate the influence of BITC modification of α-LA on polarity and digestibility. To determine the repeatability of peptide separation by 2D-HPTLC, the unmodified and BITC-modified protein hydrolyzates were separated six times. The absolute standard deviations between the retardation factors of the individual peptide spots varied between 0.52 and 4.79 mm for the x-coordinates and between 0.41 and 6.47 mm for the y-coordinates for all three samples. Here, the mean relative standard deviations ranged from 5.80 to 10.4% for the x-coordinates and from 5.91 to 18.3% for the y-coordinates. The results of the tryptic hydrolysis indicated that, depending on the concentration of BITC used, the modification sterically hinders the cleavage sites for the enzyme, resulting in a reduced digestibility. Covalent binding of the hydrophobic BITC altered the digestibility and polarity of the protein, leading to a difference in peptide patterns between the unmodified and modified α-LA. It was concluded that the reaction was undirected, resulting in a mixture of unmodified and modified peptides, and that elongated modified peptides were formed by BITC blocking of trypsin cleavage sites.
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.
Graphical abstract
The quality of food is influenced by several factors during production and storage. When using marker compounds, different steps in the production chain, as well as during storage, can be monitored. ...This might enable an optimum prediction of food's shelf life and avoid food waste. Especially, proteoforms and peptides thereof can serve as indicators for exogenous influences. The development of a proteomics-based workflow for detecting and identifying differences in the proteome is complex and time-consuming. The aim of the study was to develop a fast and universal workflow with ultra-high temperature (UHT) milk as a proteinaceous model food with expectable changes in protein/peptide composition. To find an optimum shelf life without sticking to a theoretically fixed best-before date, new evaluation and analytical methods are needed. Consequently, a modeling approach was used to monitor the shelf life of the milk after it was treated thermally and stored. The different peptide profiles determined with high-resolution mass spectrometry (HRMS) showed a significant difference depending on the preparation method of the samples. Potential marker peptides were determined using orthogonal projections to latent structures discriminant analysis (OPLSDA) and principal component analysis (PCA) following a typical proteomics protocol with tryptic hydrolysis. An additional Python-based algorithm enabled the identification of eight potential tryptic marker peptides (with mass spectrometric structural indications
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885.4843,
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639.3500,
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635.8622,
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634.3570,
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412.7191,
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623.2967,
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880.4767, and
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692.4041), indicating the effect of the heat treatment. The developed workflow is flexible and can be easily adapted to different research questions in the field of peptide analysis. In particular, the process of feature identification can be carried out with significantly less effort than with conventional methods.
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