The anti‐ageing effects of a natural peptide discovered by artificial intelligence

Kennedy, Kathy; Cal, Roi; Casey, Rory; Lopez, Cyril; Adelfio, Alessandro; Molloy, Brendan; Wall, Audrey; Holton, Thérèse A.; Khaldi, Nora

doi:10.1111/ics.12635

Cited by 22 publications

(19 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…23 Artificial intelligence (AI and machine learning methods have been used recently to successfully discover a novel anti-inflammatory ingredient derived from rice 24,25 and a natural peptide with anti-aging properties derived from the pea proteome. 26 Here, we present in vitro and ex vivo evidence validating the efficacy of pep_35E7UW, a natural peptide identified in the proteome of Oryza sativa with anti-aging activity. Furthermore, we provide additional support to an in silico machine learning approach for discovery.…”

Section: Introductionmentioning

confidence: 70%

“…[31][32][33] Artificial Intelligence and machine learning approaches are increasingly seen as notable methodologies for discovery34with recent novel functional hydrolysates and peptides being described in the areas of inflammation and anti-aging. [24][25][26] For example, Kennedy and Cal et al Described for the first time the successful application of a deep learning approach for the discovery of a Pisum sativum (pea)-derived peptide with clinically proven functional skin anti-ageingproperties. 26 O. sativa, the staple food for approximately 3.5 billion people, 35 is a documented source of various functional anti-ageing ingredients36-38.…”

Section: Discussionmentioning

confidence: 99%

“…[24][25][26] For example, Kennedy and Cal et al Described for the first time the successful application of a deep learning approach for the discovery of a Pisum sativum (pea)-derived peptide with clinically proven functional skin anti-ageingproperties. 26 O. sativa, the staple food for approximately 3.5 billion people, 35 is a documented source of various functional anti-ageing ingredients36-38. Using a transferrable in silico approach, pep_35E7UW, a novel, natural peptide, was identified from the proteome of O. sativa.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wall¹

2020

JDC

View full text Add to dashboard Cite

Background: The effects of both chronological and exogenous skin ageing have a profound impact on components of the extracellular matrix (ECM), leading to visible changes in appearance over time. Natural ingredients aimed at alleviating and counteracting the effects of cutaneous ageing are of great interest. Here, we investigate the anti-ageing potential ofpep_35E7UW, a peptide within the rice proteome identified by machine learning. Aim: To examine the in vitro and ex vivo efficacy of pep_35E7UWon ECM neosynthesis in human dermal cells and skin explants. Methods: Using machine learning, pep_35E7UW was identified and its anti-ageing effects were evaluated in human dermal fibroblasts (HDF's) and keratinocytes (HaCaTs) by means of cell culture and wound healing assays. This was further assessed by real time qPCR, where the influence on key genes in the collagen and elastin synthesis pathways was examined. Finally, the ex vivo effect of pep_35E7UW was determined by immunostaining of human skin explants. Results: Cell collagen and elastin assays of HDF's treated with pep_35E7UWreported a 32% and 51% increase in each protein respectively. Gene expression analysis determined that pep_35E7UW significantly increased collagen and elastin expression at multiple time points following treatment. Cell migration was significantly increased with pep_35E7UW treatment in the wound healing assay. pep_35E7UW topical application resulted in significant increase in elastin expression (43%) in human skin explants. Conclusion: Here, we demonstrate that pep_35E7UW, an artificial intelligence (AI) discovered peptide, has the capacity to be both an in vitro and ex vivo stimulator of fundamental ECM components. Our results position pep_35E7UWas a functional ingredient for attenuating the effects of cutaneous ageing.

show abstract

Section: Introductionmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wall¹

2020

JDC

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Another AI discovery characterised a functional ingredient, again derived from rice, that significantly improved physical strength and mobility in a double-blind placebo with an immuno-impaired ageing population [33]. Additionally, a similar approach identified a peptide within the pea plant that reduced cellular ageing in a double-blind placebo clinical trial [34], while other research proved that a peptide hydrolysate from fava bean has the ability to prolong muscle health [35]. Such examples demonstrate that the complexity of understanding the clinical benefits of plants can begin to be solved by the latest ML algorithms and that plant-derived functional ingredients can now be developed in a scientific manner at a minute fraction of the costs traditionally associated with pharmaceutical development.…”

mentioning

confidence: 99%

Using Artificial Intelligence to Reduce Global Healthcare Costs through Discovery and Development of Nutritional Interventions

Doherty

Wall

Khaldi

2020

IJND

View full text Add to dashboard Cite

The human population is ageing and as age is a major risk factor for many of the top 10 causes of death worldwide, the ability to prevent age-related diseases and improve healthspan is of paramount importance. Nutritional interventions are cost-effective, easy to implement and scientifically proven approach to prevent disease and prolong healthspan. This research advocates evidence in the public domain proving nutrition can positively affect health. In relation to diabetes, plants belonging to the Fabaceae phylogenetic family have regularly demonstrated a positive influence on incidence and progression, in both humans and other animals. It would be advantageous for validated preventative nutritional strategies to be adopted by governments, globally. There are some barriers to the implementation of nutritional strategies as a mainstream form of treatment. In particular, the molecular complexity of food is great which adds a layer of difficulty in characterizing key actives within food and associating them to the relevant physiological target. In recent years, machine learning has been applied to different health areas, such as a functional ingredient derived from rice that significantly improves physical strength in an immune-impaired ageing population and a natural compound in pea that reduces cellular ageing. In summary, there is a major opportunity to use technology to identify novel, safe, natural compounds from food at a faster rate than ever before and to use this newfound understanding to prevent, treat and cure the most common serious diseases in the world today.

show abstract

The anti‐ageing effects of a natural peptide discovered by artificial intelligence

Cited by 22 publications

References 53 publications

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

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

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

Using Artificial Intelligence to Reduce Global Healthcare Costs through Discovery and Development of Nutritional Interventions

Contact Info

Product

Resources

About