Quantitative structure-activity relationship modelling of ACE-inhibitory peptides derived from milk proteins
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- Pripp, A.H., Isaksson, T., Stepaniak, L. et al. Eur Food Res Technol (2004) 219: 579. doi:10.1007/s00217-004-1004-4
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Quantitative structure-activity relationship (QSAR) modelling was performed on peptides derived from milk proteins that inhibit angiotensin-I-converting enzyme (ACE). Physico-chemical descriptors expressed hydrophobicity, size and charge of side chains of the two most external amino acids in N- or C-terminal position. Models were estimated with partial least squares regression and validated with full cross-validation. A relationship (R=0.73, p<0.001) was found between hydrophobicity and positively charged amino acid in C-terminal position, size of amino acid next to C-terminal position and ACE-inhibition of peptides up to six amino acids in length. When longer peptides were included the relationship between C-terminal structure and activity decreased, reflecting the likely influence by steric effects. No relationship between N-terminal structure and inhibition activity was found. These biochemical interpretations were supported by findings from QSAR-modelling using so-called z-scales developed by Jonsson et al. (1989, Quant. Struct.-Act Relat. 8, 204–209) for amino acids.
Angiotensin-I-converting enzyme (ACE) is by definition associated with the renin-angiotensin system, which regulates peripheral blood pressure. The enzyme can increase blood pressure by converting angiotensin I to the potent vasoconstrictor, angiotensin II, and catalyse the degradation of bradykinin and enkephalins. Inhibition of ACE may therefore exert an antihypertensive effect and potent synthetic inhibitors of ACE are used extensively in the treatment of hypertension in humans . Several peptides derived from milk proteins by enzymatic cleavage have also been found to be potent inhibitors of ACE [2, 3]. Thus, foods enriched with such inhibitory peptides could be targeted towards consumers as functional food with milk-derived neutraceuticals that reduce blood pressure. A composition of peptides in the foods giving a high inhibitory potency would be crucial to obtain claimed health effects. Taking into account the large amount of theoretical possible peptides, i.e. 400 dipeptides, 8,000 tripeptides, 160,000 quatropeptides, 3.2×106 pentapeptides etc., the examination of all possible peptides to find highly efficient inhibitors would be a daunting task. At present, the main strategy has been to identify and characterise inhibitory peptides isolated from enzymatic digests of proteins, but methods describing the relationship between peptide structure and ACE-inhibition are needed to predict theoretical inhibitory potential of food protein hydrolysates.
One approach is to develop statistical models that predict the relationship between structure (e.g. amino acid sequence) and activity (e.g. ACE-inhibition). The approach is referred to as quantitative structure-activity relationship (QSAR) modelling. Besides contributing to a biochemical understanding of which peptides show activity, it provides a tool to predict the amino acid sequence of peptides that would give a potent inhibitory potential. Using physico-chemical variables to describe the chemical structure of active components, data analysis can reveal the relationship between activity and structure. Besides a suitable dataset of active compounds, an optimal set of descriptor variables is critical in QSAR modelling. Descriptor variables such as hydrophobicity/-philicity, molecular mass and shape, charge and electronic properties of the individual amino acids are good candidates. The development within the field of chemometrics has introduced powerful regression-type techniques such as partial least square regression (PLSR) to find relationships between variables . QSAR-modelling makes possible the identification of relationships between variables describing the chemical structure of peptides and their activity.
A recent development of QSAR on peptides is the use of amino acid “z-scores” obtained by principal component analysis (PCA) of property data [5, 6]. The z-scores have been interpreted as related to hydrophilicity (z1-score), side-chain bulk (z2-score) and electronic properties (z3-score) of amino acids. The z-scores have been proven to be useful for modelling a number of biological effects of small peptides, among them inhibition of ACE activity by dipeptides . Since a QSAR-modeling study of ACE-inhibition activity of dipeptides has been proven successful and provided valuable information, it seems warranted to extend it to larger peptides and with variables more directly linked to physico-chemical properties than the z-scores.
The objective of this study was to use hydrophobicity/-philicity, molecular size and charge properties as descriptors of the amino acids in QSAR-modelling of ACE-inhibiting peptides derived from milk proteins and compare these results with modelling using z-scores. Based on obtained models, biochemical interpretation of the relationship between structure and activity may be expected.
Materials and methods
Peptide fragments from milk proteins and their ACE inhibition expressed as log IC50% (μmol/l) (from literature review by Fitzgerald and Meisel )
Physico-chemical properties of amino acids used as descriptor variables
Partial least square regression was performed with the Unscrambler software, version 8.0 (Camo Prosess AS, Oslo, Norway) and statistical test of regression coefficient with the Minitab Statistical Software, release 13.1 (Minitab Inc., State College, PA, USA).
Results and discussion
Fitzgerald and Meisel  reviewed properties of milk protein hydrolysates and bioactive peptides and compiled a group of ACE-inhibitory peptides derived from milk proteins (Table 1). A set of variables describing physico-chemical properties of amino acids (Table 2) was chosen. Previous research on structure-activity relationship between ACE-inhibitory peptides have pointed out that hydrophobic amino acid residues and charged side groups influence inhibitory potential. To cover different effects of hydrophobicity three descriptive variables were included in the QSAR-modelling. The Van der Waals volume describes molecular volume and thereby steric effects of amino acid side-chains. Initially, the molecular weight of amino acids was also included in the models, but since van der Waals volume and molecular mass of amino acids are highly correlated (r=0.96) this descriptor was later neglected in the modeling work. Three categorical variables describing aromatic (i.e. Trp, Tyr, Phe) and positively (i.e. Lys, Arg, His) or negatively (i.e. Asp, Glu) charged side chains were also included with a value of 1 for presence of given type of amino acid and value of 0 for absence.
Modelling work with the data set revealed that a logarithmic transformation of IC50%-values improved the models. This is in agreement with previous QSAR-modelling using so-called z-scores to amino acids on a set of ACE-inhibiting dipeptides . The ACE-inhibition was also in that study expressed as a logarithmic transformation of IC50%-values. The data set of ACE-inhibitory peptides derived from milk proteins is made up of different research work over a 15-year period, and the ACE inhibition was measured using different assays and expressions of measurement uncertainty are lacking in several of the studies. It was therefore difficult to derive the precise uncertainty measurement based on those data. However, recently in our laboratory a study on ACE-inhibition by ethanol-soluble peptides from fish was conducted using an extract of rabbit lung acetone powder as source of ACE and furanacryloyl-Phe-Gly-Gly (FAPGG) as substrate . The standard deviation between replicates for log IC50% based on the data from that study was 0.06. At such a low measurement uncertainty, the difference between RAP and squared multivariate correlation coefficient was neglectible. RAP is one if measurement uncertainty is equivalent to the value of RMSEP.
QSAR-models on N- or C-terminal dipeptides for sub-sets of ACE-inhibiting peptides with increasing length were calculated by PLSR and the predictive ability assessed by the multivariate correlation coefficient (R) (Table 3). A highly significant correlation (p<0.001) was found for the QSAR-model based on the C-terminal dipeptide for peptides up to six amino acids in length. The low R for the data set comprising only di- and tripeptides are likely a result of a low number of samples used in the model. When the data set was increased with longer peptides, the prediction ability improved. The lower R-value when peptides with seven and eight amino acids were included, might reflect that for smaller peptides the ACE inhibition potential is primarily a result of the C-terminal structure, but as length increases steric effects that are not expressed by this QSAR-model begins to interfere with the results. In contrast to C-terminus, in which the composition of the two most external amino acids had a clear influence on the ACE inhibitory potency, the two amino acids at N-terminus had no apparent influence on inhibition.
Based on the regression coefficient of the weighted variables, the three terms are of relatively similar importance for predicting ACE inhibition by peptides. A biochemical interpretation of this QSAR-model is that increased side chain hydrophobicity of the amino acid and absence of positive charge in the C-terminal position enhance ACE-inhibitory potential (decreased log IC50%), while increased side chain size of the amino acid next to the C-terminal position decreases ACE-inhibitory potential. Such an interpretation of the QSAR-model is supported by previous research on the structure-activity relationship of ACE-inhibitory peptides pointing out that the C-terminal tripeptide region of the substrate influences binding to ACE significantly and that peptides containing hydrophobic amino acid residues in the C-terminal region display a high potency for inhibition [12, 13]. Some studies have suggested that the presence of amino acids with a positively charged side group contributes significantly to ACE inhibitory potency  The effect of positively charged side groups needs further investigation. Omitting that variable from the model presented in Eq. (5) somewhat reduced the predictive ability (RMSEP=0.61, R=0.71, p<0.001) without altering the relative effect and biochemical interpretation of the two remaining variables.
The multivariate correlation coefficient (R) for QSAR-models on the two most external amino acids (aa) in the N- or C-terminal position using physio-chemical descriptor variables (Table 2) for subsets ( n =number of peptides in subset) including peptides of increasing length
The multivariate correlation coefficient (R) for QSAR-models on the two most external amino acids (aa) in the N- or C-terminal region using z-scores (from reference  for subsets ( n =number of peptides in subset) including peptides of increasing length
QSAR-modelling of ACE-inhibitory peptides derived from milk proteins found a relationship between the compositions of the C-terminal region and inhibiting potency. Such a relationship was not apparent for the N-terminal region. A correlation was found between ACE-inhibition and structural properties related to hydrophobicity, positive charge and molecular volume of the amino acids at the C-terminal region covering the two last amino acids for peptides containing up to six amino acid residues. For longer peptides, steric effects not taken into account in the QSAR-model may be important. Measuring ACE-inhibition of peptides predicted to have different inhibitory potential should further validate the findings of the QSAR-model. Predicting bioactivity of peptides can help to identify food proteins containing encrypted peptides of potential for functional foods.
This work was funded by the Norwegian Research Council through project “Proteolysis in Foods: Quality and Biological Functions”.