Understanding the nature of bitter-taste di- and tripeptides derived from food proteins based on chemometric analysis.

Multiple linear regression (MLR) models were constructed to explain the bitter taste of di- and tripeptides based on their chemical nature (structure). Sequences (51 di- and 51 tripeptides) were derived from the BIOPEP-UWM database of sensory peptides and amino acids. The measure of their bitterness was Rcaf. , that is, bitterness relative to that of 1 mM caffeine solution (Rcaf. = 1.0). The variables were the indices describing properties of a single residue forming a peptide structure taken from ProtScale and Biological Magnetic Resonance Data Bank. MLR was made for two separate data sets by use of Statistica 13.1. We found that the presence of branched side residues or ring in a di- or tripeptide sequence (as in L, I, V, Y, F) affected its bitterness. Another variable affecting the bitter taste of di- and tripeptides was the hydrophobicity of amino acids. Using the commonly available statistical tools as well as chemical information reflecting the nature of peptides may be helpful in understanding the structure-taste relationship in food peptides. PRACTICAL APPLICATIONS: Our approach takes account of bioinformatic and cheminformatic techniques of data mining to analyze structure-bitterness of di- and tripeptides derived from food protein sources. Data on bitter peptides available in databases of biological and chemical information can be useful in creating models which help understanding the relationship between the role of structural properties of a molecule (e.g., peptide) and its function (e.g., taste). The bitterness of a peptide resulting from the presence of specific residues in its sequence, which represent different physicochemical properties may contribute to extending the knowledge about their taste-forming role in food systems. Such knowledge may be useful in designing food products with improved properties like taste which can be either enhanced or masked (considered as unwanted when thinking about the sensory value of foods). Our research strategy is universal and can also be applied to study structure-function relationships of peptides with other activities.

Structure⁻Activity Prediction of ACE Inhibitory/Bitter Dipeptides-A Chemometric Approach Based on Stepwise Regression.

Forward and backward stepwise regression (FR and BR, respectively) was applied for the structure⁻bioactivity prediction of angiotensin converting enzyme (ACE)-inhibitory/bitter-tasting dipeptides. The datasets used in this study consisted of 28 sequences and numerical variables reflecting dipeptides' physicochemical nature. The data were acquired from the BIOPEP-UWM, Biological Magnetic Resonance Databank, ProtScale, and AAindex databases. The calculations were computed using STATISTICA®13.1. FR/BR models differed in R² (0.91/0.76, respectively). The impact of C-atC(-) and N-Molw(+) on the dual function of dipeptides was observed. Positive (+) and negative (-) correlations with log IC50 are presented in parens. Moreover, C-Bur(+), N-atH(+), and N-Pol(-) were also found to be important in the FR model. The additional statistical significance of N-bul(-), N-Bur(-), and N-Hdr(+) was reported in the BR model. These attributes reflected the composition of the dipeptides. We report that the "ideal" bitter ACE inhibitor should be composed of P, Y, F (C-end) and G, V, I, L (N-end). Functions: log Rcaf. = f (observed log IC50) and log Rcaf. = f (predicted log IC50) revealed no direct relationships between ACE inhibition and the bitterness of the dipeptides. It probably resulted from some structural discrepancies between the ACE inhibitory/bitter peptides and/or the measure of activity describing one of the two bioactivities. Our protocol can be applicable for the structure⁻bioactivity prediction of other bioactivities peptides.

The Use of Peptide Markers of Carp and Herring Allergens as an Example of Detection of Sequenced and Non-Sequenced Proteins.

The objective of this study is to identify fish protein markers for detecting multiple species based on a comparative proteomic approach that relies on fragments with identical sequences. The possibilities and challenges of the use of peptides obtained from carp (Cyprinus carpio) and herring (Clupea harengus) proteins are discussed. A bioinformatic analysis was followed by an LC-MS/MS experiment to identify markers predicting the presence of fish allergenic proteins. Selected myosin peptides were found in carp protein hydrolysates with known sequences and in herring protein hydrolysates with unknown sequences. The results obtained for carp and herring proteins myosin and parvalbumin indicate that proteins with unknown sequences can be identified by peptide markers. Such markers can be designed by disregarding the principle that peptides should be unique (present in one sequence). The challenge is to determine a group of proteins that can be detected by peptide identification.

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science.

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.

Common Amino Acid Subsequences in a Universal Proteome--Relevance for Food Science.

A common subsequence is a fragment of the amino acid chain that occurs in more than one protein. Common subsequences may be an object of interest for food scientists as biologically active peptides, epitopes, and/or protein markers that are used in comparative proteomics. An individual bioactive fragment, in particular the shortest fragment containing two or three amino acid residues, may occur in many protein sequences. An individual linear epitope may also be present in multiple sequences of precursor proteins. Although recent recommendations for prediction of allergenicity and cross-reactivity include not only sequence identity, but also similarities in secondary and tertiary structures surrounding the common fragment, local sequence identity may be used to screen protein sequence databases for potential allergens in silico. The main weakness of the screening process is that it overlooks allergens and cross-reactivity cases without identical fragments corresponding to linear epitopes. A single peptide may also serve as a marker of a group of allergens that belong to the same family and, possibly, reveal cross-reactivity. This review article discusses the benefits for food scientists that follow from the common subsequences concept.

Epitopic hexapeptide sequences from Baltic cod parvalbumin beta (allergen Gad c 1) are common in the universal proteome.

The aim of this study was to analyze the distribution of hexapeptide fragments considered as epitopes of Baltic cod parvalbumin beta (allergen Gad c 1) in the universal proteome. Cod (Gadus morhua subsp. callarias) parvalbumin hexapeptides cataloged in the Immune Epitope Database were used as query sequences. The UniProt database was screened using the WU-BLAST 2 program. The distribution of hexapeptide fragments was investigated in various protein families, classified according to the presence of the appropriate domains, and in proteins of plant, animal and microbial species. Hexapeptides from cod parvalbumin were found in the proteins of plants and animals which are food sources, microorganisms with various applications in food technology and biotechnology, microorganisms which are human symbionts and commensals as well as human pathogens. In the last case possible coverage between epitopes from pathogens and allergens should be avoided during vaccine design.