Identification of bioactive milk peptides could improve food technology through improved selection of food supplements with a focus on antihypertensiveproperties. We hypothesized that angiotensin I–converting enzyme (ACE) inhibitory activities of milk di- and tripeptides could be predicted using 3-dimensional quantitative structure activity relationship methods and that these activities could be explained through evaluation of structural features (hydrogen bond donor/acceptor, hydrophobic, steric, and electrostatic) that are responsible for this bioactivity. We aimed to build comparative molecular field analysis(CoMFA) models combined with in silico digestion to predict the peptide sequences released from enzymatic digestion and to evaluate peptides without experimental data. Furthermore, molecular docking simulation was performed with the aim to evaluate structural features. Molecular docking simulations revealed that the most potent inhibitory peptides contain hydrophobic amino acids that enter deep into the hydrophobic pocket of the ACE active site and make interactions with its residues. CoMFA results point out favorable steric interactions and electronegativity at the C-terminus of the milk dipeptides. The CoMFA model appears to favor electropositive amino acids at the second place in tripeptides and electronegative interaction with Tyr520. Furthermore, predicted values of ACE inhibitory activity of dipeptides obtained by peptide cutter are relatively high, which recommend them for application as functional food supplements and natural alternatives to ACE inhibitory drugs. This research suggests that obtained 3-dimensional quantitative structure activity relationship models are able to successfully identify milk-derived di- and tripeptides that have significant antihypertensive activity and provide information for screening and design of novel ACE inhibitors that could be used as supplements in human nutrition.
|DOI - pysyväislinkit|
|Tila||Julkaistu - 2017|
|OKM-julkaisutyyppi||A1 Julkaistu artikkeli, soviteltu|
- Molecular modeling