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.

The aim of this study was to analyze soybean proteins as sources of peptides likely to be bitter using fragmentomic and hybrid approaches involving in silico and in vitro studies. The bitterness of peptides (called parent peptides) was theoretically estimated based on the presence of bitter-tasting motifs, particularly those defined as bitter-tasting indicators. They were selected based on previously published multilinear stepwise regression results. Bioinformatic-assisted analyses covered the hydrolysis of five major soybean-originating protein sequences using bromelain, ficin, papain, and proteinase K. Verification of the results in experimental conditions included soy protein concentrate (SPC) hydrolysis, RP-HPLC (for monitoring the proteolysis), and identification of peptides using RP-HPLC-MS/MS. Discrepancies between in silico and in vitro results were observed when identifying parent peptide SPC hydrolysate samples. However, both analyses revealed that conglycinins were the most abundant sources of parent peptides likely to taste bitter. The compatibility percentage of the in silico and in vitro results was 3%. Nine parent peptides with the following sequences were identified in SPC hydrolysates: LSVISPK, DVLVIPLG, LIVILNG, NPFLFG, ISSTIV, PQMIIV, PFPSIL, DDFFL, and FFEITPEK (indicators are in bold). The fragmentomic idea of research might provide a supportive method for predicting the bitterness of hydrolysates. However, this statement needs to be confirmed experimentally.

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.

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 R_caf. = f (observed log IC₅₀) and log R_caf. = 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.

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.