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.
Keywords: bioinformatics; bitter peptides; chemometrics; databases; proteins.
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