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

Mass spectrometry as a lens into molecular human nutrition and health

Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.

Agro-Industrial Plant Proteins in Electrospun Materials for Biomedical Application

Plant proteins are receiving a lot of attention due to their abundance in nature, customizable properties, biodegradability, biocompatibility, and bioactivity. As a result of global sustainability concerns, the availability of novel plant protein sources is rapidly growing, while the extensively studied ones are derived from byproducts of major agro-industrial crops. Owing to their beneficial properties, a significant effort is being made to investigate plant proteins' application in biomedicine, such as making fibrous materials for wound healing, controlled drug release, and tissue regeneration. Electrospinning technology is a versatile platform for creating nanofibrous materials fabricated from biopolymers that can be modified and functionalized for various purposes. This review focuses on recent advancements and promising directions for further research of an electrospun plant protein-based system. The article highlights examples of zein, soy, and wheat proteins to illustrate their electrospinning feasibility and biomedical potential. Similar assessments with proteins from less-represented plant sources, such as canola, pea, taro, and amaranth, are also described.

Smart systems in producing algae-based protein to improve functional food ingredients industries

Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.

A nanohybrid synthesized by polymeric assembling Au(I)-peptide precursor for anti-wrinkle function

A major sign of aging is wrinkles (dynamic lines and static lines) on the surface of the skin. In spite of Botulinum toxin's favorable therapeutic effect today, there have been several reports of its toxicity and side effects. Therefore, the development of an effective and safe wrinkle-fighting compound is imperative. An antioxidant-wrinkle effect was demonstrated by the peptide that we developed and synthesized, termed Skin Peptide. Aiming at the intrinsic defects of the peptide such as hydrolysis and poor membrane penetration, we developed a general approach to transform the Skin Peptide targeting intracellular protein-protein interaction into a bioavailable peptide-gold spherical nano-hybrid, Skin Pcluster. As expected, the results revealed that Skin Pcluster reduced the content of acetylcholine released by neurons in vitro, and then inhibit neuromuscular signal transmission. Additionally, human experiments demonstrated a significant de-wrinkle effect. Moreover, Skin Pcluster is characterized by a reliable safety profile. Consequently, anti-wrinkle peptides and Skin Pcluster nanohybrids demonstrated innovative anti-wrinkle treatments and have significant potential applications.

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

Scientific research consistently demonstrates that diseases may be delayed, treated, or even prevented and, thereby, health may be maintained with health-promoting functional food ingredients (FFIs). Consumers are increasingly demanding sound information about food, nutrition, nutrients, and their associated health benefits. Consequently, a nutrition industry is being formed around natural foods and FFIs, the economic growth of which is increasingly driven by consumer decisions. Information technology, in particular artificial intelligence (AI), is primed to vastly expand the pool of characterised and annotated FFIs available to consumers, by systematically discovering and characterising natural, efficacious, and safe bioactive ingredients (bioactives) that address specific health needs. However, FFI-producing companies are lagging in adopting AI technology for their ingredient development pipelines for several reasons, resulting in a lack of efficient means for large-scale and high-throughput molecular and functional ingredient characterisation. The arrival of the AI-led technological revolution allows for the comprehensive characterisation and understanding of the universe of FFI molecules, enabling the mining of the food and natural product space in an unprecedented manner. In turn, this expansion of bioactives dramatically increases the repertoire of FFIs available to the consumer, ultimately resulting in bioactives being specifically developed to target unmet health needs.

Measuring the oral bioavailability of protein hydrolysates derived from food sources: A critical review of current bioassays

BACKGROUND

Food proteins are a source of hydrolysates with potentially useful biological attributes. Bioactive peptides from food-derived proteins are released from hydrolysates using exogenous industrial processes or endogenous intestinal enzymes. Current in vitro permeability assays have limitations in predicting the oral bioavailability (BA) of bioactive peptides in humans. There are also difficulties in relating the low blood levels of food-derived bioactive peptides detected in preclinical in vivo models to pharmacodynamic read-outs relevant for humans.

SCOPE AND APPROACH

In this review, we describe in vitro assays of digestion, permeation, and metabolism as indirect predictors of the potential oral BA of hydrolysates and their constituent bioactive peptides. We discuss the relationship between industrial hydrolysis processes and the oral BA of hydrolysates and their peptide by-products.

KEY FINDINGS

Hydrolysates are challenging for analytical detection methods due to capacity for enzymatic generation of peptides with novel sequences and also new modifications of these peptides during digestion. Mass spectrometry and peptidomics can improve the capacity to detect individual peptides released from complex hydrolysates in biological milieu.

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review

Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods.

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

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.

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 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.

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

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.

Using Peptidomics and Machine Learning to Assess Effects of Drying Processes on the Peptide Profile within a Functional Ingredient

Bioactive peptides are known to have many health benefits beyond nutrition; yet the peptide profile of high protein ingredients has been largely overlooked when considering the effects of different processing techniques. Therefore, to investigate whether drying conditions could affect the peptide profile and bioactivity within a functional ingredient, we examined the effects of spray (SD) and freeze (FD) drying on rice natural peptide network (NPN), a characterised functional ingredient sourced from the Oryza sativa proteome, which has previously been shown to effectively modulate circulating cytokines and improve physical performance in humans. In the manufacturing process, rice NPN was either FD or SD. Employing a peptidomic approach, we investigated the physicochemical characteristics of peptides common and unique to FD and SD preparations. We observed similar peptide profiles regarding peptide count, amino acid distribution, weight, charge, and hydrophobicity in each sample. Additionally, to evaluate the effects of drying processes on functionality, using machine learning, we examined constituent peptides with predicted anti-inflammatory activity within both groups and identified that the majority of anti-inflammatory peptides were common to both. Of note, key bioactive peptides validated within rice NPN were recorded in both SD and FD samples. The present study provides an important insight into the overall stability of the peptide profile and the use of machine learning in assessing predicted retention of bioactive peptides contributing to functionality during different types of processing.

Electrospinning Proteins for Wound Healing Purposes: Opportunities and Challenges

With the growth of the aging population worldwide, chronic wounds represent an increasing burden to healthcare systems. Wound healing is complex and not only affected by the patient's physiological conditions, but also by bacterial infections and inflammation, which delay wound closure and re-epithelialization. In recent years, there has been a growing interest for electrospun polymeric wound dressings with fiber diameters in the nano- and micrometer range. Such wound dressings display a number of properties, which support and accelerate wound healing. For instance, they provide physical and mechanical protection, exhibit a high surface area, allow gas exchange, are cytocompatible and biodegradable, resemble the structure of the native extracellular matrix, and deliver antibacterial agents locally into the wound. This review paper gives an overview on cytocompatible and biodegradable fibrous wound dressings obtained by electrospinning proteins and peptides of animal and plant origin in recent years. Focus is placed on the requirements for the fabrication of such drug delivery systems by electrospinning as well as their wound healing properties and therapeutic potential. Moreover, the incorporation of antimicrobial agents into the fibers or their attachment onto the fiber surface as well as their antimicrobial activity are discussed.