Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 167

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

An in silico model to predict the potential of dietary proteins as sources of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides

Open access
Nongonierma, Alice B., FitzGerald, Richard J.·2014·Food Chemistry

Abstract

An in silico approach was developed to predict the potential of 72 dietary proteins to act as a source of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. The model takes 68 DPP-IV inhibitory peptides (having an IC50 value <2000 μM) and the specific contribution of their amino acids into account. Bovine α-lactalbumin (α-La) and κ-casein (CN) displayed the highest protein coverage (PC, 43.9%) and potency index (PI, 17.9 10(-6) μM(-1)g(-1)), respectively for DPP-IV inhibitory peptides. Sequence alignment of 39 DPP-IV inhibitory peptides having IC50's<200 μM revealed the frequent occurrence of Trp at the N-terminus and Pro at position 2. Canola, chicken egg, oat and wheat were identified as potential sources of DPP-IV inhibitory peptides. In silico approaches may assist in the selection of food proteins for the enzymatic release of DPP-IV inhibitory peptides. The results are relevant to the generation of biofunctional ingredients for glycaemic management.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.foodchem.2014.05.090

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 167

Metadata

Authors
Nongonierma, A. B., & FitzGerald, R. J.
Venue
Food Chemistry
Publisher
Elsevier BV
Published
2014-12
Volume
165
Pages
489-498
ISSN
0308-8146 (ISSN-L)
OpenAlex
W1985628945
Cited by
167
References
1
FWCI
5.84
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: An in silico model to predict the potential of dietary proteins as sources of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. https://app.cochat.ai/papers/10.1016/j.foodchem.2014.05.090
The paper
Nongonierma, A. B., & FitzGerald, R. J. (2014). An in silico model to predict the potential of dietary proteins as sources of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. Food Chemistry. https://doi.org/10.1016/j.foodchem.2014.05.090

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.