In silico tools for exploring potential human allergy to proteins

Allergenicity evaluation of quinoa proteins - A study in Brown Norway rats

The popularity of quinoa seeds has increased in the last decade due to their high nutritional value and natural gluten-free composition. Consumption of new proteins may pose a risk of introducing new allergies. In the present study the immunogenicity and sensitising capacity of quinoa proteins were assessed in a dose-response experiment in Brown Norway rats in comparison to proteins from spinach and peanut. Cross-reactivity between quinoa proteins and known allergens was evaluated by in silico analyses followed by analyses with 11 selected protein extracts and their anti-sera by means of ELISAs and immunoblotting. Further, an in vitro simulated gastro-duodenal digestion was performed. Quinoa proteins were found to have an inherent medium to high immunogenicity and sensitising capacity, being able to induce specific IgG1 and IgE levels higher than spinach but lower than peanut and elicit reactions of clinical relevance similar to peanut. Quinoa proteins were generally shown to resist digestion and retain capacity to bind quinoa-specific antibodies. Quinoa proteins were shown to be cross-reactive with peanut and tree nut allergens as high sequence homology and antibody cross-binding were demonstrated. Present study suggests that quinoa pose a medium to high level of allergenicity that should be further investigated in human studies.

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach

This Article proposes a novel chemometric approach to understanding and exploring the allergenic nature of food proteins. Using machine learning methods (supervised and unsupervised), this work aims to predict the allergenicity of plant proteins. The strategy is based on scoring descriptors and testing their classification performance. Partitioning was based on support vector machines (SVM), and a k-nearest neighbor (KNN) classifier was applied. A fivefold cross-validation approach was used to validate the KNN classifier in the variable selection step as well as the final classifier. To overcome the problem of food allergies, a robust and efficient method for protein classification is needed.

Peptides as Potentially Anticarcinogenic Agent from Functional Canned Meat Product with Willow Extract

The aim of the study was to demonstrate canned pork as a functional meat product due to the presence of potentially anti-cancer factors, e.g., (a) bioactive peptides with potential activity against cancer cells; (b) lowering the content of sodium nitrite and with willow herb extract. In silico (for assessing the anticancer potential of peptides) and in vitro (antiproliferation activity on L-929 and CT-26 cell lines) analysis were performed, and the obtained results confirmed the bioactive potential against cancer of the prepared meat product. After 24 h of incubation with peptides obtained from meat product containing lyophilized herb extract at a concentration of 150 mg/kg, the viability of both tested cell lines was slightly decreased to about 80% and after 72 h to about 40%. On the other hand, after 72 h of incubation with the peptides obtained from the variant containing 1000 mg/kg of freeze-dried willow herb extract, the viability of intestinal cancer cells was decreased to about 40%, while, by comparison, the viability of normal cells was decreased to only about 70%.

In silico proteolysis and molecular interaction of tilapia (Oreochromis niloticus) skin collagen-derived peptides for environmental remediation

Fish skin collagen hydrolyzate has demonstrated the potent inhibition of dipeptidyl peptidase-IV (DPP-IV), one of the treatments for type-2 diabetes mellitus (type-2 DM), but the precise mechanism is still unclear. This study used in silico method to evaluate the potential of the active peptides from tilapia skin collagen (Oreochromis niloticus) for DPP-IV inhibitor. The methodology includes collagen hydrolysis using BIOPEP, which is the database of bioactive peptides; active peptide selection; toxicity, allergenicity, sensory analysis of active peptides; and binding of active peptides to DPP-IV compared with linagliptin. The result indicated that in silico enzymatic hydrolysis of collagen produced active peptides with better prediction of biological activity than intact collagen. There are 13 active peptides were predicted as non-toxic and non-allergenic, some of which have a bitter, salty, and undetectable taste. Docking simulations showed all active peptides interacted with DPP-IV through hydrogen bonds, van der Waals force, hydrophobic interaction, electrostatic force, π-sulfur, and unfavorable interaction, where WF (Trp-Phe), VW (Val-Trp), WY (Trp-Tyr), and WG (Trp-Gly) displayed higher binding affinities of 0.8; 0.5; 0.4; and 0.3 kcal/mol compared with linagliptin. In this study, we successfully demonstrated antidiabetic type-2 DM potential of the active peptides from tilapia skin collagen. The obtained data provided preliminary data for further research in the utilization of fish skin waste as a functional compound to treat the type-2 DM patients. Alternatively, this treatment can be synergistically combined with the available antidiabetic drugs to improve the insulin secretion of the type-2 DM patients.

Dipeptidyl Peptidase IV Inhibitory Peptides Generated in Dry-Cured Pork Loin during Aging and Gastrointestinal Digestion

The ability of peptides from an aqueous and salt-soluble protein extract of dry-cured pork loins to inhibit the action of dipeptidyl peptidase IV was determined. This activity was assessed at different times of the production process, i.e., 28, 90, 180, 270 and 360 days. The resistance of the biological property during the simulated digestive process was also assessed. For this, the extracts were hydrolyzed with pepsin and pancreatin as a simulated digestion step of the gastrointestinal tract and fractionated (>7 kDa) as an intestinal absorption step. The results indicate that dried-pork-loin peptides may have potential as functional food ingredients in the prevention and treatment of type 2 diabetes mellitus. In particular, the APPPPAEV, APPPPAEVH, KLPPLPL, RLPLLP, VATPPPPPPK, VPIPVPLPM and VPLPVPVPI sequences show promise as natural food compounds helpful in maintaining good health.

Bioactive Peptides in Preventative Healthcare: An Overview of Bioactivities and Suggested Methods to Assess Potential Applications

Food derived bioactive peptides can be generated from various protein sources and usually consist of between 2-30 amino acids with bulky, side-chain aromatic amino acids preferred in the ultimate and penultimate positions at the C-terminal end of the amino acid chain. They are reported to impart a myriad of preventative health beneficial effects to the consumer once ingested and these include heart health benefits through inhibition of enzymes including renin (EC 3.4.23.15) and angiotensin- I-converting enzyme (ACE-1; EC 3.4.15.1) within the renin angiotensin aldosterone system (RAAS) anti-inflammatory (due to inhibition of ACE-I and other enzymes) and anti-cancer benefits, prevention of type-2 diabetes through inhibition of dipeptidyl peptidase IV (DPP-IV), bone and dental strength, antimicrobial and immunomodulatory effects and several others. Peptides have also reported health benefits in the treatment of asthma, neuropathic pain, HIV and wound healing. However, the structure, amino acid composition and length of these peptides, along with the quantity of peptide that can pass through the gastrointestinal tract and often the blood-brain barrier (BBB), intact and reach the target organ, are important for the realisation of these health effects in an in vivo setting. This paper aims to collate recent important research concerning the generation and detection of peptides in the laboratory. It discusses products currently available as preventative healthcare peptide options and relevant legislation barriers to place a food peptide product on the market. The review also highlights useful in silico computer- based methods and analysis that may be used to generate specific peptide sequences from proteins whose amino acid sequences are known and also to determine if the peptides generated are unique and bioactive. The topic of food-derived bioactive peptides for health is of great interest to scientific research and industry due to evolving drivers in food product innovation, including health and wellness for the elderly, infant nutrition and optimum nutrition for sports athletes and the humanisation of pets. This paper provides an overview of what is required to generate bioactive peptide containing hydrolysates, what methods should be used in order to characterise the beneficial health effects of these hydrolysates and the active peptide sequences, potential applications of bioactive peptides and legislative requirements in Europe and the United States. It also highlights success stories and barriers to the development of peptide-containing food products that currently exist.

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned some drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model-transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the area under the receiver operating characteristic curve (AUC) of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training time is the longest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides

There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.