Predictive modelling of angiotensin converting enzyme inhibitory dipeptides

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Title: Predictive modelling of angiotensin converting enzyme inhibitory dipeptides
Authors: Norris, Roseanne
Casey, Fergal
FitzGerald, Richard J.
Shields, Denis C.
Mooney, Catherine
Permanent link: http://hdl.handle.net/10197/3748
Date: 14-Aug-2012
Abstract: The ability of docking to predict angiotensin converting enzyme (ACE) inhibitory dipeptide sequences was assessed using AutoDock Vina. All potential dipeptides and phospho-dipeptides were docked and scored. Peptide intestinal stability was assessed using a prediction amino acid clustering model. Selected dipeptides, having AutoDock Vina scores −8.1 and predicted to be ‘stable’ intestinally, were characterised, using LIGPLOT and for ACE-inhibitory potency. Two newly identified ACE-inhibitory dipeptides, Asp-Trp and Trp-Pro, having Vina scores of −8.3 and −8.6 gave IC50 values of 258 ± 4.23 and 217 ± 15.7 μM, respectively. LIGPLOT analysis indicated no zinc interaction for these dipeptides. Phospho-dipeptides were predicted to have a good affinity for ACE. However, the experimentally determined IC50 results did not correlate since, for example, Trp-pThr and Pro-pTyr, having Vina scores of −8.5 and −8.1, respectively, displayed IC50 values of >500 μM. While docking allowed identification of new ACE inhibitory dipeptides, it may not be a fully reliable predictive tool in all cases.
Funding Details: Science Foundation Ireland
Irish Research Council for Science, Engineering and Technology
Type of material: Journal Article
Publisher: Elsevier
Copyright (published version): 2012 Elsevier Ltd.
Keywords: Predictive modelling;Predictive modelling;ACE inhibition;Dipeptides;AutoDock Vina;Intestinal stability
Subject LCSH: Angiotensin converting enzyme--Inhibitors
Pharmacology--Computer programs
Pharmacology--Computer simulation
DOI: 10.1016/j.foodchem.2012.02.023
Language: en
Status of Item: Peer reviewed
Appears in Collections:Conway Institute Research Collection
CASL Research Collection
Medicine Research Collection

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