Integration of QSAR modelling and QM/MM analysis to investigate functional food peptides with antihypertensive activity

Antihypertensive peptides derived from dietary proteins have long been recognised as an important source of developing functional foods with blood pressure-lowering effect. However, most of such peptides exhibit diverse tastes, such as sweet, bitter, sour and salty, which is a non-negligible aspect considered in the food development process. In the present study, several predictive quantitative structure–activity relationship (QSAR) models that correlate peptide's structural features with their multi-bioactivities and bitter taste are established at both sequence and structure levels, and the models are then used to conduct extrapolation on thousands of randomly generated, structurally diverse peptides with chain lengths ranging from two to six amino acid residues. Based on the statistical results gained from QSAR modelling, the relationship between the antihypertensive activity and bitter taste of peptides at different sequence lengths is investigated in detail. Moreover, the structural basis, energetic property and biological implication underlying peptide interactions with angiotensin-converting enzyme (ACE), a key target of antihypertensive therapy, are analysed at a complex three-dimensional structure level by using a high-level hybrid quantum mechanics/molecular mechanics scheme. It is found that (a) bitter taste is highly dependent on peptide length, whereas ACE inhibitory potency has only a modest correlation with the length, (b) dipeptides and tripeptides perform a moderate relationship between their ACE inhibition and bitterness, but the relationship could not be observed for those peptides of more than three amino acid residues and (c) the increase in sequence length does not cause peptides to exhibit substantial enhancement of antihypertensive activity; this is particularly significant for longer peptides such as pentapeptides and hexapeptides.     

Minimum analogue peptide sets (MAPS) for quantitative structure-activity relationships

The information contents in previously published peptide sets was compared with smaller sets of peptides selected according to statistical designs. It was found that minimum analogue peptide sets (MAPS) constructed by factorial or fractional factorial designs in physiochemical properties contained substantial structure-activity information. Although five to six times smaller than the originally published peptide sets the MAPS resulted in QSAR models able to predict biological activity. The QSARs derived from a MAPS of nine dipeptides, and from a set of 58 dipeptides inhibiting angiotensin converting enzyme were compared and found to be of equal strength. Furthermore, for a set of bitter tasting dipeptides it was found that an incomplete MAPS of 10 dipeptides gave just as good a model as the model based on a set of 48 dipeptides. By comparison other non-designed sets of peptides gave QSARs with poor predictive power. It was also demonstrated how MAPS centered on a lead peptide can be constructed as to specifically explore the physiochemical and biological properties in the vicinity of the lead. It was concluded that small information-rich peptide sets MAPS can be constructed on the basis of statistical designs with principal properties of amino acids as design variables.     

BITTER PEPTIDES, OCCURRENCE AND STRUCTURE

The bitter taste of many protein rich foods resides in the peptidefraction. 61 bitter tasting peptides, isolated from naturalsystems, and 145 bitter tasting synthetic peptides are reviewed.The relationships between average hydrophobicity and bittertaste are then discussed.