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

Autor:	Corrochano AR; Nuritas Ltd., D02 RY95, Dublin, Ireland., Cal R; Nuritas Ltd., D02 RY95, Dublin, Ireland., Kennedy K; Nuritas Ltd., D02 RY95, Dublin, Ireland., Wall A; Nuritas Ltd., D02 RY95, Dublin, Ireland., Murphy N; Nuritas Ltd., D02 RY95, Dublin, Ireland., Trajkovic S; Nuritas Ltd., D02 RY95, Dublin, Ireland., O'Callaghan S; Nuritas Ltd., D02 RY95, Dublin, Ireland., Adelfio A; Nuritas Ltd., D02 RY95, Dublin, Ireland., Khaldi N; Nuritas Ltd., D02 RY95, Dublin, Ireland.
Jazyk:	angličtina
Zdroj:	Current research in food science [Curr Res Food Sci] 2021 Apr 03; Vol. 4, pp. 224-232. Date of Electronic Publication: 2021 Apr 03 (Print Publication: 2021).
DOI:	10.1016/j.crfs.2021.03.008
Abstrakt:	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. Competing Interests: All authors are employees of Nuritas Limited and declare no competing interests. The design of the study; the collection, analyses, and interpretation of data; the writing of the manuscript and the decision to publish the results was performed by Nuritas. (© 2021 The Author(s).)
Databáze:	MEDLINE
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