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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2021-01-01
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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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