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

Abstract

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.

Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides

“…Conversely, even if a potential bioactive within a functional ingredient is identified, assessing stability and bioavailability, attributing functionality, and validating effects in vitro/in vivo has proven to be difficult (Corrochano et al, 2019;Feng et al, 2019). The latter challenge of assigning a specific effect to a molecule is characteristic for food bioactives, because-unlike pharmaceutical compounds-food bioactives exert multiple, subtle, long-term effects in a concerted fashion, rather than a single, strong, immediate effect conferred by a single molecule (Cal et al, 2020;Corrochano et al, 2021). In addition, the food matrix adds to the mode-of-action of food bioactives (Udenigwe and Fogliano, 2017;Sun et al, 2020).…”

“…K.;Sharma et al, 2017;Kathy;Kennedy et al, 2020b;K. ;Kennedy et al, 2020a;Chauhan et al, 2021;Corrochano et al, 2021;Cal et al, 2020). With a focus on peptides, we argue here for complementing, if not replacing, the traditional sequence of FFI characterisation (Figure 1) with an AI-powered alternative ("benefit definition → bioactive prediction → food source identification → bioactive release → bioactive validation"; Figure 2) by placing artificial intelligence upfront in the entire process.…”

“…They contain large complex networks of interacting bioactive peptides with attributable health benefits (Hernández-Ledesma et al, 2014;Nasri 2017). To untangle these networks and characterise key bioactives and functions within, an integrated peptidomics-AI platform has recently been proven to be successful by our laboratories (Corrochano et al, 2021;Casey et al, 2021;Kathy Kennedy et al, 2020b;K. Kennedy et al, 2020a;Chauhan et al, 2021).…”

“…Like the predicted and synthesised individual peptides, produced FFIs are also validated for efficacy in vitro, creating more valuable positive and negative data feedback for the AI platform. In addition to bioactivity feedback, the safety and toxicity of predicted peptides and FFIs as well as peptide stability across oral ingestion and gastro-intestinal digestion are also assessed, using cell viability assays (Cal et al, 2020) and in vitro digestion models (Corrochano et al, 2021) that are incorporated as part of the iterative learning process (Figure 3).…”

“…has the ability to predict peptides with gastro-intestinal resistance (Corrochano et al, 2021). Therefore, further simulated gastrointestinal digestion and stability experiments in vitro and ex vivo using FFIs bears the potential for future AI applications to identify key latent features, and therefore, predict the bioavailability profile of bioactive peptides and FFIs.…”