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 R caf. , that is, bitterness relative to that of 1 mM caffeine solution (R caf. 5 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 applicationsOur 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 structurefunction relationships of peptides with other activities.

BSA, bovine serum albumin; B, bromelain; B-MPC, bromelain hydrolysate of milk protein concentrate; F, fi cin; F-MPC, fi cin hydrolysate of milk protein concentrate; MLR, multivariate linear regression; MPC, milk protein concentrate; O-MPC, non-hydrolyzed milk protein concentrate; P, papain; P-MPC, papain hydrolysate of milk protein concentrate; PK, proteinase K; PK-MPC, proteinase K hydrolysate of milk protein concentrate; RP-HPLC, reversed-phase high performance liquid chromatography; RP-HPLC-MS/MS, reversed-phase high performance liquid chromatography and mass spectrometry; Rcaf., the ratio of caffeine (the threshold concentration for 1 mM caffeine solution as a standard (Rcaf. = 1.0); t R predicted , theoretical retention time; t R experimental , experimental retention time; α s1 , casein; α s2-CN, α s2-casein; α-La, α-lactalbumin; β-Lg, βlactoglobulin; β-CN, βcasein; κ-CN, κ-casein; and TFA, trifl uoracetic acid.

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.

This paper presents the use of principal component analysis (PCA) to study the effect of specific physicochemical attributes on bitterness of di-and tripeptides originating from food proteins. Peptide sequences were derived from the BIOPEP-UWM database of sensory peptides and amino acids. Descriptors defining the physicochemical properties of amino acids forming the analyzed peptides were study variables. They were derived from ProtScale program and Biological Magnetic Resonance Data Bank. Finally, PCA was carried out for 51 dipeptides/12 variables, and 51 tripeptides/18 variables using STATISTICA ® 13.1 software. PCA allowed reducing the input datasets to 4 principal components (PCs) for dipeptides and to 5 PCs for tripeptides. The impact of the following properties on the bitterness of peptides was observed: relatively high molecular weight, bulkiness, increasing number of carbon and hydrogen atoms of amino acids forming the sequences. These properties characterized the N-(negative correlations) and C-terminal residue (positive correlations) of both di-and tripeptides. An additional property affecting peptide bitterness was amino acids' hydrophobicity. Our results were consistent with scientific reports on structure-bitterness of peptides. Thus, we find PCA a chemometric approach helpful in broadening the knowledge about the function of peptides resulting from their chemical nature.

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