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Record W2282542030 · doi:10.14288/1.0166186

Dietary proteins as precursors of dipeptidyl-peptidase IV inhibitors — allies to complement pharmacotherapy in the management of type 2 diabetes

2015· article· en· W2282542030 on OpenAlexaff
Isabelle M. E. Lacroix

Bibliographic record

VenuecIRcle (University of British Columbia) · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDipeptidyl peptidasePharmacotherapyComplement (music)Type 2 diabetesDipeptidyl peptidase-4MedicineDiabetes mellitusPharmacologySitagliptinChemistryEndocrinologyInternal medicineBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Inhibition of the enzyme dipeptidyl-peptidase IV (DPP-IV) is a promising approach for managing hyperglycemia in type 2 diabetes. The overall objective of this study was to produce, from dietary proteins, peptides able to inhibit the activity of DPP-IV and study their mechanisms of action. The potential of proteins from various food commodities to serve as precursors of DPP-IV inhibitors was first investigated using an in silico approach. Peptide sequences reported to have DPP-IV inhibitory activity were found in all proteins studied, including those from milk, a food product shown in several studies to have beneficial effects on glycemic regulation. Therefore, milk proteins were selected for hydrolysis using different proteases to release the active fragments. Fractionation of the most active hydrolysates, the peptic digests of whey protein isolate and α-lactalbumin, revealed a number of peptides with varying effectiveness and modes of inhibition. Among the sequences identified, the β-lactoglobulin-derived peptides ⁴⁶LKPTPEGDL⁵⁴ and ⁴⁶LKPTPEGDLEIL⁵⁷ displayed the greatest potency (IC₅₀ = 54 and 57 µM, respectively) and inhibited DPP-IV in an un-competitive manner. Peptide arrays were investigated as an alternative strategy to screen food proteins for the presence of DPP-IV inhibitory peptides within their sequences. Using SPOT technology, 114 deca-peptides spanning the entire sequence of α-lactalbumin were synthesized on cellulose membranes and their binding to and inhibition of DPP-IV were studied. SPOT- and traditionally-synthesized peptides displayed consistent trends in DPP-IV inhibitory activity, confirming that peptide arrays can be used to complement or support the traditional methods currently used to identify DPP-IV inhibitors. Lastly, the effect of protein-derived peptides on the activity of porcine and human DPP-IV, the two most commonly used species to assess DPP-IV activity in vitro, was compared. The enzymes differed in their susceptibility to inhibition by 43 of the 62 peptides investigated. Generally, porcine DPP-IV was inhibited more strongly than the human enzyme. Findings from this research showed that DPP-IV inhibitory peptides can be generated from dairy proteins. These natural inhibitors, although less effective than synthetic drugs, could be used to complement pharmacotherapy in the management of type 2 diabetes. Additional research to evaluate their efficacy in humans is, however, needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.234
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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