pep_35E7UW, a natural peptide with cutaneous anti-ageing effects discovered within the Oryza sativa proteome through machine learning

Wall, Audrey

doi:10.15406/jdc.2020.04.00162

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…AI, which includes machine learning approaches, has been successful in deciphering the complexity of natural source proteomes in order to elucidate noteworthy bioactive peptides. Recent discoveries include single bioactive peptides derived from the rice proteome with antiaging properties [ 16 , 17 ], while networks of bioactive peptides or hydrolysates have been successfully investigated for the prevention of inflammaging [ 18 , 19 ] and muscle atrophy [ 20 ]. An AI approach to discovery represents a safe and effective method to produce benefits, which have already shown a great effect of risk reduction by nutritional intervention [ 21 ], in a manner that is compliant-friendly.…”

Section: Introductionmentioning

confidence: 99%

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

et al. 2021

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The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation.

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Section: Introductionmentioning

confidence: 99%

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

et al. 2021

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“…When considering peptides, deciphering scale and complexity becomes a major hurdle; for example, proteins can be broken down into peptides at a rate of 36 million per minute [ 21 ]. However, Artificial Intelligence (AI) and deep learning techniques are perfectly primed to extract previously indecipherable knowledge from disparate biological data streams; as such, machine learning is increasingly seen as a discovery tool in life science, with bioactive peptides being successfully predicted in the areas of inflammation and skin aging [ 31 , 32 , 33 , 34 ]. Here, similar machine learning methods were employed to identify a short linear novel peptide therapeutics for use in T2DM.…”

Section: Introductionmentioning

confidence: 99%

Discovery through Machine Learning and Preclinical Validation of Novel Anti-Diabetic Peptides

et al. 2021

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While there have been significant advances in drug discovery for diabetes mellitus over the past couple of decades, there is an opportunity and need for improved therapies. While type 2 diabetic patients better manage their illness, many of the therapeutics in this area are peptide hormones with lengthy sequences and a molecular structure that makes them challenging and expensive to produce. Using machine learning, we present novel anti-diabetic peptides which are less than 16 amino acids in length, distinct from human signalling peptides. We validate the capacity of these peptides to stimulate glucose uptake and Glucose transporter type 4 (GLUT4) translocation in vitro. In obese insulin-resistant mice, predicted peptides significantly lower plasma glucose, reduce glycated haemoglobin and even improve hepatic steatosis when compared to treatments currently in use in a clinical setting. These unoptimised, linear peptides represent promising candidates for blood glucose regulation which require further evaluation. Further, this indicates that perhaps we have overlooked the class of natural short linear peptides, which usually come with an excellent safety profile, as therapeutic modalities.

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

Corrochano

Cal

Kennedy

et al. 2021

Current Research in Food Science

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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.

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pep_35E7UW, a natural peptide with cutaneous anti-ageing effects discovered within the Oryza sativa proteome through machine learning

Cited by 4 publications

References 47 publications

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

Discovery through Machine Learning and Preclinical Validation of Novel Anti-Diabetic Peptides

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

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