Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient

Characterising key components within functional ingredients as well as assessing efficacy and bioavailability is an important step in validating nutritional interventions. Machine learning can assess large and complex data sets, such as proteomic data from plants sources, and so offers a prime oppor...

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Main Authors: Alberto R. Corrochano, Roi Cal, Kathy Kennedy, Audrey Wall, Niall Murphy, Sanja Trajkovic, Sean O’Callaghan, Alessandro Adelfio, Nora Khaldi
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:Current Research in Food Science
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2665927121000216
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spelling doaj-5c8f719cf1ef40cd94810bd00f1d94702021-04-16T04:54:52ZengElsevierCurrent Research in Food Science2665-92712021-01-014224232Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredientAlberto R. Corrochano0Roi Cal1Kathy Kennedy2Audrey Wall3Niall Murphy4Sanja Trajkovic5Sean O’Callaghan6Alessandro Adelfio7Nora Khaldi8Nuritas Ltd., D02 RY95, Dublin, IrelandNuritas Ltd., D02 RY95, Dublin, IrelandNuritas Ltd., D02 RY95, Dublin, IrelandCorresponding author.; Nuritas Ltd., D02 RY95, Dublin, IrelandNuritas Ltd., D02 RY95, Dublin, IrelandNuritas Ltd., D02 RY95, Dublin, IrelandNuritas Ltd., D02 RY95, Dublin, IrelandNuritas Ltd., D02 RY95, Dublin, IrelandNuritas Ltd., D02 RY95, Dublin, IrelandCharacterising key components within functional ingredients as well as assessing efficacy and bioavailability is an important step in validating nutritional interventions. Machine learning can assess large and complex data sets, such as proteomic data from plants sources, and so offers a prime opportunity to predict key bioactive components within a larger matrix. Using machine learning, we identified two potentially bioactive peptides within a Vicia faba derived hydrolysate, NPN_1, an ingredient which was previously identified for preventing muscle loss in a murine disuse model. We investigated the predicted efficacy of these peptides in vitro and observed that HLPSYSPSPQ and TIKIPAGT were capable of increasing protein synthesis and reducing TNF-α secretion, respectively. Following confirmation of efficacy, we assessed bioavailability and stability of these predicted peptides and found that as part of NPN_1, both HLPSYSPSPQ and TIKIPAGT survived upper gut digestion, were transported across the intestinal barrier and exhibited notable stability in human plasma. This work is a first step in utilising machine learning to untangle the complex nature of functional ingredients to predict active components, followed by subsequent assessment of their efficacy, bioavailability and human plasma stability in an effort to assist in the characterisation of nutritional interventions.http://www.sciencedirect.com/science/article/pii/S2665927121000216Protein synthesisAnti-inflammatoryBioactive peptideSimulated gastrointestinal digestionIntestinal absorptionMachine learning
collection DOAJ
language English
format Article
sources DOAJ
author Alberto R. Corrochano
Roi Cal
Kathy Kennedy
Audrey Wall
Niall Murphy
Sanja Trajkovic
Sean O’Callaghan
Alessandro Adelfio
Nora Khaldi
spellingShingle Alberto R. Corrochano
Roi Cal
Kathy Kennedy
Audrey Wall
Niall Murphy
Sanja Trajkovic
Sean O’Callaghan
Alessandro Adelfio
Nora Khaldi
Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient
Current Research in Food Science
Protein synthesis
Anti-inflammatory
Bioactive peptide
Simulated gastrointestinal digestion
Intestinal absorption
Machine learning
author_facet Alberto R. Corrochano
Roi Cal
Kathy Kennedy
Audrey Wall
Niall Murphy
Sanja Trajkovic
Sean O’Callaghan
Alessandro Adelfio
Nora Khaldi
author_sort Alberto R. Corrochano
title Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient
title_short Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient
title_full Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient
title_fullStr Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient
title_full_unstemmed Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient
title_sort characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a vicia faba-derived functional ingredient
publisher Elsevier
series Current Research in Food Science
issn 2665-9271
publishDate 2021-01-01
description Characterising key components within functional ingredients as well as assessing efficacy and bioavailability is an important step in validating nutritional interventions. Machine learning can assess large and complex data sets, such as proteomic data from plants sources, and so offers a prime opportunity to predict key bioactive components within a larger matrix. Using machine learning, we identified two potentially bioactive peptides within a Vicia faba derived hydrolysate, NPN_1, an ingredient which was previously identified for preventing muscle loss in a murine disuse model. We investigated the predicted efficacy of these peptides in vitro and observed that HLPSYSPSPQ and TIKIPAGT were capable of increasing protein synthesis and reducing TNF-α secretion, respectively. Following confirmation of efficacy, we assessed bioavailability and stability of these predicted peptides and found that as part of NPN_1, both HLPSYSPSPQ and TIKIPAGT survived upper gut digestion, were transported across the intestinal barrier and exhibited notable stability in human plasma. This work is a first step in utilising machine learning to untangle the complex nature of functional ingredients to predict active components, followed by subsequent assessment of their efficacy, bioavailability and human plasma stability in an effort to assist in the characterisation of nutritional interventions.
topic Protein synthesis
Anti-inflammatory
Bioactive peptide
Simulated gastrointestinal digestion
Intestinal absorption
Machine learning
url http://www.sciencedirect.com/science/article/pii/S2665927121000216
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