Predictive modelling of angiotensin converting enzyme inhibitory dipeptides

DSpace/Manakin Repository

Show simple item record

dc.contributor.author Norris, Roseanne
dc.contributor.author Casey, Fergal
dc.contributor.author FitzGerald, Richard
dc.contributor.author Shields, Denis C.
dc.contributor.author Mooney, Catherine
dc.date.accessioned 2012-08-17T15:22:47Z
dc.date.available 2012-08-17T15:22:47Z
dc.date.copyright 2012 Elsevier Ltd. en
dc.date.issued 2012-08-14
dc.identifier.citation Food Chemistry en
dc.identifier.uri http://hdl.handle.net/10197/3748
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland en
dc.description.sponsorship Irish Research Council for Science, Engineering and Technology en
dc.format.extent 151238 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Elsevier en
dc.rights This is the author’s version of a work that was accepted for publication in Food Chemistry. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Food Chemistry (VOL 133, ISSUE 4, (2012)) DOI:10.1016/j.foodchem.2012.02.023 Elsevier Ltd. en
dc.subject Predictive modelling en
dc.subject Predictive modelling en
dc.subject ACE inhibition en
dc.subject Dipeptides en
dc.subject AutoDock Vina en
dc.subject Intestinal stability en
dc.subject.lcsh Angiotensin converting enzyme--Inhibitors en
dc.subject.lcsh Pharmacology--Computer programs en
dc.subject.lcsh Pharmacology--Computer simulation en
dc.title Predictive modelling of angiotensin converting enzyme inhibitory dipeptides en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.status Peer reviewed en
dc.identifier.volume 133 en
dc.identifier.issue 4 en
dc.identifier.startpage 1349 en
dc.identifier.endpage 1354 en
dc.identifier.doi 10.1016/j.foodchem.2012.02.023
dc.neeo.contributor Norris|Roseanne|aut| en
dc.neeo.contributor Casey|Fergal|aut| en
dc.neeo.contributor FitzGerald|Richard|aut| en
dc.neeo.contributor Shields|Denis C.|aut| en
dc.neeo.contributor Mooney|Catherine|aut| en


Files in this item

This item appears in the following Collection(s)

Show simple item record

This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.

Search Research Repository


Advanced Search

Browse