Artificial intelligence identified peptides modulate inflammation in healthy adults

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Evidence of Immunomodulatory Food-Protein Derived Peptides in Human Nutritional Interventions: Review on the Outcomes and Potential Limitations

The immune system is somehow related to all the metabolic pathways, in a bidirectional way, and the nutritional interventions affecting these pathways might have a relevant impact on the inflammatory status of the individuals. Food-derived peptides have been demonstrated to exert several bioactivities by in vitro or animal studies. Their potential to be used as functional food is promising, considering the simplicity of their production and the high value of the products obtained. However, the number of human studies performed until now to demonstrate effects in vivo is still scarce. Several factors must be taken into consideration to carry out a high-quality human study to demonstrate immunomodulatory-promoting properties of a test item. This review aims to summarize the recent human studies published in which the purpose was to demonstrate bioactivity of protein hydrolysates, highlighting the main results and the limitations that can restrict the relevance of the studies. Results collected are promising, although in some studies, physiological changes could not be observed. When responses were observed, they sometimes did not refer to relevant parameters and the immunomodulatory properties could not be clearly established with the current evidence. Well-designed clinical trials are needed in order to evaluate the role of protein hydrolysates in immunonutrition.

Opportunities and challenges in application of artificial intelligence in pharmacology

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

A comprehensive review on the glucoregulatory properties of food-derived bioactive peptides

Diabetes mellitus, a group of metabolic disorders characterized by persistent hyperglycemia, affects millions of people worldwide and is on the rise. Dietary proteins, from a wide range of food sources, are rich in bioactive peptides with antidiabetic properties. Notable examples include AGFAGDDAPR, a black tea-derived peptide, VRIRLLQRFNKRS, a β-conglycinin-derived peptide, and milk-derived peptide VPP, which have shown antidiabetic effects in diabetic rodent models through variety of pathways including improving beta-cells function, suppression of alpha-cells proliferation, inhibiting food intake, increasing portal cholecystokinin concentration, enhancing insulin signaling and glucose uptake, and ameliorating adipose tissue inflammation. Despite the immense research on glucoregulatory properties of bioactive peptides, incorporation of these bioactive peptides in functional foods or nutraceuticals is widely limited due to the existence of several challenges in the field of peptide research and commercialization. Ongoing research in this field, however, is fundamental to pave the road for this purpose.

How Healthy Are Non-Traditional Dietary Proteins? The Effect of Diverse Protein Foods on Biomarkers of Human Health

Future food security for healthy populations requires the development of safe, sustainably-produced protein foods to complement traditional dietary protein sources. To meet this need, a broad range of non-traditional protein foods are under active investigation. The aim of this review was to evaluate their potential effects on human health and to identify knowledge gaps, potential risks, and research opportunities. Non-traditional protein sources included are algae, cereals/grains, fresh fruit and vegetables, insects, mycoprotein, nuts, oil seeds, and legumes. Human, animal, and in vitro data suggest that non-traditional protein foods have compelling beneficial effects on human health, complementing traditional proteins (meat/poultry, soy, eggs, dairy). Improvements in cardiovascular health, lipid metabolism, muscle synthesis, and glycaemic control were the most frequently reported improvements in health-related endpoints. The mechanisms of benefit may arise from their diverse range of minerals, macro- and micronutrients, dietary fibre, and bioactive factors. Many were also reported to have anti-inflammatory, antihypertensive, and antioxidant activity. Across all protein sources examined, there is a strong need for quality human data from randomized controlled intervention studies. Opportunity lies in further understanding the potential effects of non-traditional proteins on the gut microbiome, immunity, inflammatory conditions, DNA damage, cognition, and cellular ageing. Safety, sustainability, and evidence-based health research will be vital to the development of high-quality complementary protein foods that enhance human health at all life stages.

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.

Preservatives from food-For food: Pea protein hydrolysate as a novel bio-preservative against Escherichia coli O157:H7 on a lettuce leaf

Fresh-cut fruits and vegetables are becoming particularly popular as healthy fast-food options; however, they present challenges such as accelerated rates of decay and increased risk for contamination when compared to whole produce. Given that food safety must remain paramount for producers and manufacturers, research into novel, natural food preservation solutions which can help to ensure food safety and protect against spoilage is on the rise. In this work, we investigated the potential of using a novel protein hydrolysate, produced by the enzymatic hydrolysis of Pisum sativum (PSH), as a novel bio-preservative and its abilities to reduce populations of Escherichia coli O157:H7 after inoculation on a lettuce leaf. While unhydrolyzed P. sativum proteins show no antimicrobial activity, once digested, and purified, the enzymatically released peptides induced in vitro bactericidal effects on the foodborne pathogen at 8 mg/ml. When applied on an infected lettuce leaf, the PSH significantly reduced the number of bacteria recovered after 2 hr of treatment. PSH may be preferred over other preservation strategies based on its natural, inexpensive, sustainable source, environmentally friendly process, nontoxic nature, good batch to batch consistency, and ability to significantly reduce counts of E. coli both in vitro and in a lettuce leaf.

How can artificial intelligence be used for peptidomics?

INTRODUCTION

Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases.

AREAS COVERED

The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1).[Figure: see text].

EXPERT OPINION

Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).

Artificial intelligence and the future of life sciences

Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.

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.

Peptide-like and small-molecule inhibitors against Covid-19

Coronavirus disease strain (SARS-CoV-2) was discovered in 2019, and it is spreading very fast around the world causing the disease Covid-19. Currently, more than 1.6 million individuals are infected, and several thousand are dead across the globe because of Covid-19. Here, we utilized the in-silico approaches to identify possible protease inhibitors against SARS-CoV-2. Potential compounds were screened from the CHEMBL database, ZINC database, FDA approved drugs and molecules under clinical trials. Our study is based on 6Y2F and 6W63 co-crystallized structures available in the protein data bank (PDB). Seven hundred compounds from ZINC/CHEMBL databases and fourteen hundred compounds from drug-bank were selected based on positive interactions with the reported binding site. All the selected compounds were subjected to standard-precision (SP) and extra-precision (XP) mode of docking. Generated docked poses were carefully visualized for known interactions within the binding site. Molecular mechanics-generalized born surface area (MM-GBSA) calculations were performed to screen the best compounds based on docking scores and binding energy values. Molecular dynamics (MD) simulations were carried out on four selected compounds from the CHEMBL database to validate the stability and interactions. MD simulations were also performed on the PDB structure 6YF2F to understand the differences between screened molecules and co-crystallized ligand. We screened 300 potential compounds from various databases, and 66 potential compounds from FDA approved drugs. Cobicistat, ritonavir, lopinavir, and darunavir are in the top screened molecules from FDA approved drugs. The screened drugs and molecules may be helpful in fighting with SARS-CoV-2 after further studies.Communicated by Ramaswamy H. Sarma.

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.

Human milk and infant formulae: Peptide differences and the opportunity to address the functional gap

Bovine-derived formula milk (FM) is a common substitute to human milk (HM), but lacks key functional benefits associated with HM. Accordingly, there have been significant efforts to humanise FM. Recent research has demonstrated that HM-derived peptides convey an array of beneficial bioactivities. Given that peptides serve as important signalling molecules offering high specificity and potency, they represent a prime opportunity to humanise FM. To further understand how HM-derived peptides contribute to infant health, we used peptidomics and bioinformatics to compare the peptide profile of HM to commercially available FM. We found clear and substantial differences between HM and FM in terms of peptide physicochemical properties, protein coverage and abundance. We additionally identified 618 peptides specific to HM that represent an important untapped source to be explored for novel bioactivities. While further study is required, our findings highlight the potential of a peptide-based approach to address the functional gap in FM.

An Artificial Intelligence Characterised Functional Ingredient, Derived from Rice, Inhibits TNF-α and Significantly Improves Physical Strength in an Inflammaging Population

Food-derived bioactive peptides offer great potential for the treatment and maintenance of various health conditions, including chronic inflammation. Using in vitro testing in human macrophages, a rice derived functional ingredient natural peptide network (NPN) significantly reduced Tumour Necrosis Factor (TNF)-α secretion in response to lipopolysaccharides (LPS). Using artificial intelligence (AI) to characterize rice NPNs lead to the identification of seven potentially active peptides, the presence of which was confirmed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Characterization of this network revealed the constituent peptides displayed anti-inflammatory properties as predicted in vitro. The rice NPN was then tested in an elderly "inflammaging" population with a view to subjectively assess symptoms of digestive discomfort through a questionnaire. While the primary subjective endpoint was not achieved, analysis of objectively measured physiological and physical secondary readouts showed clear significant benefits on the ability to carry out physical challenges such as a chair stand test that correlated with a decrease in blood circulating TNF-α. Importantly, the changes observed were without additional exercise or specific dietary alterations. Further health benefits were reported such as significant improvement in glucose control, a decrease in serum LDL concentration, and an increase in HDL concentration; however, this was compliance dependent. Here we provide in vitro and human efficacy data for a safe immunomodulatory functional ingredient characterized by AI.

Preclinical Evaluation of a Food-Derived Functional Ingredient to Address Skeletal Muscle Atrophy

Skeletal muscle is the metabolic powerhouse of the body, however, dysregulation of the mechanisms involved in skeletal muscle mass maintenance can have devastating effects leading to many metabolic and physiological diseases. The lack of effective solutions makes finding a validated nutritional intervention an urgent unmet medical need. In vitro testing in murine skeletal muscle cells and human macrophages was carried out to determine the effect of a hydrolysate derived from vicia faba (PeptiStrong: NPN_1) against phosphorylated S6, atrophy gene expression, and tumour necrosis factor alpha (TNF-α) secretion, respectively. Finally, the efficacy of NPN_1 on attenuating muscle waste in vivo was assessed in an atrophy murine model. Treatment of NPN_1 significantly increased the phosphorylation of S6, downregulated muscle atrophy related genes, and reduced lipopolysaccharide-induced TNF-α release in vitro. In a disuse atrophy murine model, following 18 days of NPN_1 treatment, mice exhibited a significant attenuation of muscle loss in the soleus muscle and increased the integrated expression of Type I and Type IIa fibres. At the RNA level, a significant upregulation of protein synthesis-related genes was observed in the soleus muscle following NPN_1 treatment. In vitro and preclinical results suggest that NPN_1 is an effective bioactive ingredient with great potential to prolong muscle health.