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      Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

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          Abstract

          QSAR studies may help to better understand structural requirements for peptide bioactivity and therefore to develop potent BAPs.

          Abstract

          The generation of bioactive peptides (BAPs) from dietary proteins has been widely studied. One of the main limitations of a broader application of BAPs in functional foods may arise from their low potency. Therefore, the search for more potent structures is crucial. Quantitative structure–activity relationship (QSAR) has been widely applied in drug discovery and some examples may also be found in the study of BAPs. The aim of this review was to assess the efficiency of QSAR for the discovery of novel and potent BAPs, derived from food protein sources. A wide range of bioactive properties including antioxidant, antimicrobial, angiotensin converting enzyme (ACE), renin and dipeptidyl peptidase IV (DPP-IV) inhibition as well as bitter peptides has been investigated with QSAR. Some studies have identified structural requirements for specific bioactivities, which generally confirmed findings from earlier studies carried out on those BAPs. However, discrepancies are found across analyses, possibly due to the quality of the peptide datasets as well as the descriptors used to build QSAR models. It appears to date that only a limited number of QSAR studies conducted with BAPs have subsequently carried out confirmatory studies and evaluated promising peptide sequences in vivo. This suggests that more research is needed in order to advance knowledge in the area of BAP discovery using QSAR.

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          Most cited references80

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          New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids.

          In this study 87 amino acids (AA.s) have been characterized by 26 physicochemical descriptor variables. These descriptor variables include experimentally determined retention values in seven thin-layer chromatography (TLC) systems, three nuclear magnetic resonance (NMR) shift variables, and 16 calculated variables, namely six semiempirical molecular orbital indices, total, polar, and nonpolar surface area, van der Waals volume of the side chain, log P, molecular weight, and four indicator variables describing hydrogen bond donor and acceptor properties, and side chain charge. In the present study, the data from a previous characterization of 55 AA.s from our laboratory have been extended with data for 32 additional AA.s and 14 new descriptor variables. The new 32 AA.s were selected to represent both intermediate and more extreme physicochemical properties, compared to the 20 coded AA.s. The new extended and updated principal property scales, the z-scales, were calculated and aligned to previously reported z(old)-scales. The appropriateness of the extended z-scales were validated by the use in quantitative sequence-activity modeling (QSAM) of 89 elastase substrate analogues and in a QSAM of 29 neurotensin analogues.
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            Bioactive peptides and protein hydrolysates: research trends and challenges for application as nutraceuticals and functional food ingredients

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              Structural requirements of Angiotensin I-converting enzyme inhibitory peptides: quantitative structure-activity relationship study of di- and tripeptides.

              A database consisting of 168 dipeptides and 140 tripeptides was constructed from published literature to study the quantitative structure--activity relationships of angiotensin I-converting enzyme (ACE) inhibitory peptides. Two models were computed using partial least squares regression based on the three z-scores of 20 coded amino acids and further validated by cross-validation and permutation tests. The two-component model could explain 73.2% of the Y-variance (inhibitor concentration that reduced enzyme activity by 50%, IC50) with the predictive ability of 71.1% for dipeptides, while the single-component model could explain 47.1% of the Y-variance with the predictive ability of 43.3% for tripeptides. Amino acid residues with bulky side chains as well as hydrophobic side chains were preferred for dipeptides. For tripeptides, the most favorable residues for the carboxyl terminus were aromatic amino acids, while positively charged amino acids were preferred for the middle position, and hydrophobic amino acids were preferred for the amino terminus. According to the models, the IC50 values of seven new peptides with matchable primary sequences within pea protein, bovine milk protein, and soybean were predicted. The predicted peptides were synthesized, and their IC50 values were validated through laboratory determination of inhibition of ACE activity.
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                Author and article information

                Journal
                RSCACL
                RSC Advances
                RSC Adv.
                Royal Society of Chemistry (RSC)
                2046-2069
                2016
                2016
                : 6
                : 79
                : 75400-75413
                Affiliations
                [1 ]Department of Life Sciences and Food for Health Ireland (FHI)
                [2 ]University of Limerick
                [3 ]Limerick
                [4 ]Ireland
                Article
                10.1039/C6RA12738J
                2b0d8347-591c-45cb-86cf-b2cc8ef15c5f
                © 2016
                History

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