In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors

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Title: In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors
Authors: Nongonierma, Alice B.Mooney, CatherineShields, Denis C.FitzGerald, Richard J.
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Date: Jul-2014
Online since: 2022-08-05T08:57:56Z
Abstract: Molecular docking of a library of all 8000 possible tripeptides to the active site of DPP-IV was used to determine their binding potential. A number of tripeptides were selected for experimental testing, however, there was no direct correlation between the Vina score and their in vitro DPP-IV inhibitory properties. While Trp-Trp-Trp, the peptide with the best docking score, was a moderate DPP-IV inhibitor (IC50 216 μM), Lineweaver and Burk analysis revealed its action to be non-competitive. This suggested that it may not bind to the active site of DPP-IV as assumed in the docking prediction. Furthermore, there was no significant link between DPP-IV inhibition and the physicochemical properties of the peptides (molecular mass, hydrophobicity, hydrophobic moment (μH), isoelectric point (pI) and charge). LIGPLOTs indicated that competitive inhibitory peptides were predicted to have both hydrophobic and hydrogen bond interactions with the active site of DPP-IV. DPP-IV inhibitory peptides generally had a hydrophobic or aromatic amino acid at the N-terminus, preferentially a Trp for non-competitive inhibitors and a broader range of residues for competitive inhibitors (Ile, Leu, Val, Phe, Trp or Tyr). Two of the potent DPP-IV inhibitors, Ile-Pro-Ile and Trp-Pro (IC 50 values of 3.5 and 44.2 μM, respectively), were predicted to be gastrointestinally/intestinally stable. This work highlights the needs to test the assumptions (i.e. competitive binding) of any integrated strategy of computational and experimental screening, in optimizing screening. Future strategies targeting allosteric mechanisms may need to rely more on structure-activity relationship modeling, rather than on docking, in computationally selecting peptides for screening.
Funding Details: Enterprise Ireland
Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Peptides
Volume: 57
Start page: 43
End page: 51
Copyright (published version): 2014 Elsevier
Keywords: Bioactive peptidesDipeptidyl peptidase IV inhibitorsHydrophobicityMilkMolecular dockingPredictive modeling
DOI: 10.1016/j.peptides.2014.04.018
Language: en
Status of Item: Peer reviewed
ISSN: 0196-9781
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Appears in Collections:Conway Institute Research Collection
Computer Science Research Collection
CASL Research Collection

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