AllerBase: a comprehensive allergen knowledgebase

Assessing the predictive ability of computational epitope prediction methods on Fel d 1 and other allergens

While computational epitope prediction methods have found broad application, their use, specifically in allergy-related contexts, remains relatively less explored. This study benchmarks several publicly available epitope prediction tools, focusing on the allergenic IgE and T-cell epitopes of Fel d 1, an extensively studied allergen. Using a variety of tools accessible via the Immune Epitope Database (IEDB) and other resources, we evaluate their ability to identify the known linear IgE and T-cell epitopes of Fel d 1. Our results show a limited effectiveness for B-cell epitope prediction methods, with most performing only marginally better than random selection. We also explored the general predictive abilities on other allergens, and the results were largely random. When predicting T-cell epitopes, ProPred successfully identified all known Fel d 1 T-cell epitopes, whereas the IEDB approach missed two known epitopes and demonstrated a tendency to over-predict. However, when applied to a larger test set, both methods performed only slightly better than random selection. Our findings show the limitations of current computational epitope prediction methods in accurately identifying allergenic epitopes, emphasizing the need for methodological advancements in allergen research.

Allergenic Biomarkers in the Molecular Diagnosis of IgE-Mediated Wheat Allergy

IgE-mediated wheat allergy can take on various forms, including childhood food allergy to wheat, wheat-dependent exercise-induced anaphylaxis in young adults, baker's respiratory allergy/asthma in workers exposed to wheat flour inhalation, and contact urticaria that is caused by hydrolyzed wheat proteins in some cosmetics, and that is sometimes associated with a food allergy. Singleplex and multiplex immunoassays detect specific IgE antibodies to wheat allergenic molecular biomarkers such as omega-5 gliadin Tri a 19, lipid transfer protein Tri a 14, and alpha-amylase inhibitors. The fluorescence enzyme immunoassay with capsulated cellulose polymer solid-phase coupled allergens is a commonly used singleplex assay. Multiplex methods include the ELISA-based macroarray immunoassay using nano-bead technology and a microarray immunoassay on polymer-coated slides. Another promising diagnostic tool is the basophil activation test performed with omega-5 gliadin and other wheat protein types. Detailed comprehension of the structural and immunological features of the numerous wheat allergens significant in clinical settings is imperative for advancing diagnostic biomarkers for IgE-mediated wheat allergies.

Prospects for developing allergen-depleted food crops

In addition to the challenge of meeting global demand for food production, there are increasing concerns about food safety and the need to protect consumer health from the negative effects of foodborne allergies. Certain bio-molecules (usually proteins) present in food can act as allergens that trigger unusual immunological reactions, with potentially life-threatening consequences. The relentless working lifestyles of the modern era often incorporate poor eating habits that include readymade prepackaged and processed foods, which contain additives such as peanuts, tree nuts, wheat, and soy-based products, rather than traditional home cooking. Of the predominant allergenic foods (soybean, wheat, fish, peanut, shellfish, tree nuts, eggs, and milk), peanuts (Arachis hypogaea) are the best characterized source of allergens, followed by tree nuts (Juglans regia, Prunus amygdalus, Corylus avellana, Carya illinoinensis, Anacardium occidentale, Pistacia vera, Bertholletia excels), wheat (Triticum aestivum), soybeans (Glycine max), and kidney beans (Phaseolus vulgaris). The prevalence of food allergies has risen significantly in recent years including chance of accidental exposure to such foods. In contrast, the standards of detection, diagnosis, and cure have not kept pace and unfortunately are often suboptimal. In this review, we mainly focus on the prevalence of allergies associated with peanut, tree nuts, wheat, soybean, and kidney bean, highlighting their physiological properties and functions as well as considering research directions for tailoring allergen gene expression. In particular, we discuss how recent advances in molecular breeding, genetic engineering, and genome editing can be used to develop potential low allergen food crops that protect consumer health.

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction

Motivation

Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes.

Results

To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited.

Availability and implementation

The web service NetAllergen and the datasets are available at https://services.healthtech.dtu.dk/services/NetAllergen-1.0/.

Food allergen analysis: A review of current gaps and the potential to fill them by matrix-assisted laser desorption/ionization

Food allergy remains a public health, business, and regulatory challenge. Risk analysis (RA) and risk management (RM) of food allergens are of great importance and analysis for food allergens is necessary for both. The current workhorse techniques for allergen analysis (enzyme linked immunosorbent assay [ELISA] and real-time polymerase chain reaction) exhibit recognized challenges including variable and antibody specific responses and detection of species DNA rather than allergen protein, respectively. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables protein identification, with potential for multiplex analysis and traceability to the System of International units (SI), aiding global measurement standardization. In this review, recent literature has been systematically reviewed to assess progress in LC-MS/MS and define the potential and benefits of matrix-assisted laser desorption/ionization-time-of-flight MS (MALDI-ToF-MS) technology for allergen analysis. MALDI-ToF-MS of initially intact protein is already applied to verify in silico-derived peptide sequences for LC-MS/MS analysis. We describe the origins of MALDI and its future perspectives, including affinity bead-assisted assays coupled to MALDI. Based on the proliferation of reliable and reproducible MALDI-based clinical applications, the technique should emulate the detection capability (sensitivity) of established allergen detection techniques, whilst reducing technical support and having equivalent multiplexing potential to competing techniques, for example, LC-MS/MS and ELISA. Although unlikely to offer inherent SI traceability, MALDI-based allergen analysis will complement existing MS approaches for allergens. Affinity bead-MALDI appears capable of higher throughput at lower cost per sample than almost any existing technique, enabling repeated sub-sampling as a way to reduce representative sampling issues.

Allergenicity of wheat protein in diet: Mechanisms, modifications and challenges

Wheat is widely available in people's daily diets. However, some people are currently experiencing IgE-mediated allergic reactions to wheat-based foods, which seriously impact their quality of life. Thus, it is imperative to provide comprehensive knowledge and effective methods to reduce the risk of wheat allergy (WA) in food. In the present review, recent advances in WA symptoms, the major allergens, detection methods, opportunities and challenges in establishing animal models of WA are summarized and discussed. Furthermore, an updated overview of the different modification methods that are currently being applied to wheat-based foods is provided. This study concludes that future approaches to food allergen detection will focus on combining multiple tools to rapidly and accurately quantify individual allergens in complex food matrices. Besides, biological modification has many advantages over physical or chemical modification methods in the development of hypoallergenic wheat products, such as enzymatic hydrolysis and fermentation. It is worth noting that using biotechnology to edit wheat allergen genes to produce allergen-free food may be a promising method in the future which could improve the safety of wheat foods and the health of allergy sufferers.

DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins

Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify allergens were based on homology or conventional machine learning methods, which were inefficient and still had room to be improved for the detection of allergens with low homology. In addition, few methods based on deep learning were reported, although deep learning has been successfully applied to several tasks in protein sequence analysis. In the present work, a deep neural network-based model, called DeepAlgPro, was proposed to identify allergens. We showed its great accuracy and applicability to large-scale forecasts by comparing it to other available tools. Additionally, we used ablation experiments to demonstrate the critical importance of the convolutional module in our model. Moreover, further analyses showed that epitope features contributed to model decision-making, thus improving the model's interpretability. Finally, we found that DeepAlgPro was capable of detecting potential new allergens. Overall, DeepAlgPro can serve as powerful software for identifying allergens.

Advanced Proteomic and Bioinformatic Tools for Predictive Analysis of Allergens in Novel Foods

In recent years, novel food is becoming an emerging trend increasingly more demanding in developed countries. Food proteins from vegetables (pulses, legumes, cereals), fungi, bacteria and insects are being researched to introduce them in meat alternatives, beverages, baked products and others. One of the most complex challenges for introducing novel foods on the market is to ensure food safety. New alimentary scenarios drive the detection of novel allergens that need to be identified and quantified with the aim of appropriate labelling. Allergenic reactions are mostly caused by proteins of great abundance in foods, most frequently of small molecular mass, glycosylated, water-soluble and with high stability to proteolysis. The most relevant plant and animal food allergens, such as lipid transfer proteins, profilins, seed storage proteins, lactoglobulins, caseins, tropomyosins and parvalbumins from fruits, vegetables, nuts, milk, eggs, shellfish and fish, have been investigated. New methods for massive screening in search of potential allergens must be developed, particularly concerning protein databases and other online tools. Moreover, several bioinformatic tools based on sequence alignment, motif identification or 3-D structure predictions should be implemented as well. Finally, targeted proteomics will become a powerful technology for the quantification of these hazardous proteins. The ultimate objective is to build an effective and resilient surveillance network with this cutting-edge technology.

Updates on Databases of Allergens and Allergen-Epitopes

The increasing prevalence of allergic diseases is of great public health concern. Environmental and food allergens are the major triggers of allergic diseases via respiratory or gastrointestinal routes, respectively. A major setback in the clinical management of allergies is the unavailability of purified allergens required for diagnostic purposes. Furthermore, manipulation of allergen sequences and structures by employing protein-engineering approaches is needed to design immunotherapeutic vaccines. All these approaches rely upon the sequence, structure, and epitope location of allergens. A number of databases have therefore been developed that serve as repositories of molecular information of allergens. In this chapter, we discuss the five most important widely used allergen databases that might be helpful for the research community working on molecular allergology.

Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights

Environmental samples collected in Brindisi (Italy) by a Hirst-type trap and in Lecce (Italy) by a PM10 sampler were analysed by optical microscopy and DNA-metabarcoding, respectively, to identify airborne pollen and perform an exploratory study, highlighting the benefits and limits of both sampling/detection systems. The Hirst-type trap/optical-microscopy system allowed detecting pollen on average over the full bloom season, since whole pollen grains, whose diameter vary within 10–100 μm, are required for morphological detection with optical microscopy. Conversely, pollen fragments with an aerodynamic diameter ≤10 μm were collected in Lecce by the PM10 sampler. Pollen grains and fragments are spread worldwide by wind/atmospheric turbulences and can age in the atmosphere, but aerial dispersal, aging, and long-range transport of pollen fragments are favoured over those of whole pollen grains because of their smaller size. Twenty-four Streptophyta families were detected in Lecce throughout the sampling year, but only nine out of them were in common with the 21 pollen families identified in Brindisi. Meteorological parameters and advection patterns were rather similar at both study sites, being only 37 km apart in a beeline, but their impact on the sample taxonomic structure was different, likely for the different pollen sampling/detection systems used in the two monitoring areas.

Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics

The prevalence of allergic diseases is regarded as one of the key challenges in health worldwide. Although the precise mechanisms underlying this rapid increase in prevalence are unknown, emerging evidence suggests that genetic and environmental factors play a significant role. The immune system, microbiota, viruses, and bacteria have all been linked to the onset of allergy disorders in recent years. Avoiding allergen exposure is the best treatment option; however, steroids, antihistamines, and other symptom-relieving drugs are also used. Allergen bioinformatics encompasses both computational tools/methods and allergen-related data resources for managing, archiving, and analyzing allergological data. This study highlights allergy-promoting mechanisms, algorithms, and concepts in allergen bioinformatics, as well as major areas for future research in the field of allergology.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

The COMPARE Database: A Public Resource for Allergen Identification, Adapted for Continuous Improvement

Motivation: The availability of databases identifying allergenic proteins via a transparent and consensus-based scientific approach is of prime importance to support the safety review of genetically-modified foods and feeds, and public safety in general. Over recent years, screening for potential new allergens sequences has become more complex due to the exponential increase of genomic sequence information. To address these challenges, an international collaborative scientific group coordinated by the Health and Environmental Sciences Institute (HESI), was tasked to develop a contemporary, adaptable, high-throughput process to build the COMprehensive Protein Allergen REsource (COMPARE) database, a publicly accessible allergen sequence data resource along with bioinformatics analytical tools following guidelines of FAO/WHO and CODEX Alimentarius Commission.

Results: The COMPARE process is novel in that it involves the identification of candidate sequences via automated keyword-based sorting algorithm and manual curation of the annotated sequence entries retrieved from public protein sequence databases on a yearly basis; its process is meant for continuous improvement, with updates being transparently documented with each version; as a complementary approach, a yearly key-word based search of literature databases is added to identify new allergen sequences that were not (yet) submitted to protein databases; in addition, comments from the independent peer-review panel are posted on the website to increase transparency of decision making; finally, sequence comparison capabilities associated with the COMPARE database was developed to evaluate the potential allergenicity of proteins, based on internationally recognized guidelines, FAO/WHO and CODEX Alimentarius Commission

NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodies can help with vaccine production and food allergen detection in patients. Many studies have focused on predicting linear B-cell epitopes, but only two prediction tools are currently available to predict the sub-type of an epitope. NIgPred was developed as a prediction tool for IgA, IgE, and IgG. NIgPred integrates various heterologous features with machine-learning approaches. Differently from previous studies, our study considered peptide-characteristic correlation and autocorrelation features. Sixty kinds of classifier were applied to construct the best prediction model. Furthermore, the genetic algorithm and hill-climbing algorithm were used to select the most suitable features for improving the accuracy and reducing the time complexity of the training model. NIgPred was found to be superior to the currently available tools for predicting IgE epitopes and IgG epitopes on independent test sets. Moreover, NIgPred achieved a prediction accuracy of 100% for the IgG epitopes of a coronavirus data set. NIgPred is publicly available at our website.

Seasonal Variability of the Airborne Eukaryotic Community Structure at a Coastal Site of the Central Mediterranean

The atmosphere represents an underexplored temporary habitat for airborne microbial communities such as eukaryotes, whose taxonomic structure changes across different locations and/or regions as a function of both survival conditions and sources. A preliminary dataset on the seasonal dependence of the airborne eukaryotic community biodiversity, detected in PM10 samples collected from July 2018 to June 2019 at a coastal site representative of the Central Mediterranean, is provided in this study. Viridiplantae and Fungi were the most abundant eukaryotic kingdoms. Streptophyta was the prevailing Viridiplantae phylum, whilst Ascomycota and Basidiomycota were the prevailing Fungi phyla. Brassica and Panicum were the most abundant Streptophyta genera in winter and summer, respectively, whereas Olea was the most abundant genus in spring and autumn. With regards to Fungi, Botrytis and Colletotrichum were the most abundant Ascomycota genera, reaching the highest abundance in spring and summer, respectively, while Cryptococcus and Ustilago were the most abundant Basidiomycota genera, and reached the highest abundance in winter and spring, respectively. The genus community structure in the PM10 samples varied day-by-day, and mainly along with the seasons. The impact of long-range transported air masses on the same structure was also proven. Nevertheless, rather few genera were significantly correlated with meteorological parameters and PM10 mass concentrations. The PCoA plots and non-parametric Spearman's rank-order correlation coefficients showed that the strongest correlations generally occurred between parameters reaching high abundances/values in the same season or PM10 sample. Moreover, the screening of potential pathogenic fungi allowed us to detect seven potential pathogenic genera in our PM10 samples. We also found that, with the exception of Panicum and Physcomitrella, all of the most abundant and pervasive identified Streptophyta genera could serve as potential sources of aeroallergens in the studied area.

AllerScreener – A Server for Allergenicity and Cross-Reactivity Prediction

Allergenicity of proteins is a subtle property encoded in their structures. The prediction of allergenicity of novel proteins saves time and resources for subsequent experimental work. In the host antigen-presenting cells, the allergens are processed as antigens by the means of Human Leukocyte Antigens (HLA) class II proteins. Sometimes, people allergic to a given protein show allergic reaction to a different protein, even when the two proteins have different routes of exposure. This phenomenon is termed cross-reactivity. Here, we describe a server for allergenicity and cross-reactivity prediction based on the abilities of allergenic proteins to generate binders to HLA class II proteins. The generated peptides are compared to HLA binders originating from known allergens. As a result, the server returns a list of common binders, origin proteins, and species. Different species generate common HLA binders and this determines their cross-reactivity. The server is named AllerScreener and is freely accessible at: http://www.ddg-pharmfac.net/AllerScreener .

AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes

AlgPred 2.0 is a web server developed for predicting allergenic proteins and allergenic regions in a protein. It is an updated version of AlgPred developed in 2006. The dataset used for training, testing and validation consists of 10 075 allergens and 10 075 non-allergens. In addition, 10 451 experimentally validated immunoglobulin E (IgE) epitopes were used to identify antigenic regions in a protein. All models were trained on 80% of data called training dataset, and the performance of models was evaluated using 5-fold cross-validation technique. The performance of the final model trained on the training dataset was evaluated on 20% of data called validation dataset; no two proteins in any two sets have more than 40% similarity. First, a Basic Local Alignment Search Tool (BLAST) search has been performed against the dataset, and allergens were predicted based on the level of similarity with known allergens. Second, IgE epitopes obtained from the IEDB database were searched in the dataset to predict allergens based on their presence in a protein. Third, motif-based approaches like multiple EM for motif elicitation/motif alignment and search tool have been used to predict allergens. Fourth, allergen prediction models have been developed using a wide range of machine learning techniques. Finally, the ensemble approach has been used for predicting allergenic protein by combining prediction scores of different approaches. Our best model achieved maximum performance in terms of area under receiver operating characteristic curve 0.98 with Matthew's correlation coefficient 0.85 on the validation dataset. A web server AlgPred 2.0 has been developed that allows the prediction of allergens, mapping of IgE epitope, motif search and BLAST search (https://webs.iiitd.edu.in/raghava/algpred2/).

Current Trends in Proteomic Advances for Food Allergen Analysis

Food allergies are a global food challenge. For correct food labelling, the detection and quantification of allergens are necessary. However, novel product formulations and industrial processes produce new scenarios, which require much more technological developments. For this purpose, OMICS technologies, especially proteomics, seemed to be relevant in this context. This review summarises the current knowledge and studies that used proteomics to study food allergens. In the case of the allergenic proteins, a wide variety of isoforms, post-translational modifications and other structural changes during food processing can increase or decrease the allergenicity. Most of the plant-based food allergens are proteins with biological functions involved in storage, structure, and plant defence. The allergenicity of these proteins could be increased by the presence of heavy metals, air pollution, and pesticides. Targeted proteomics like selected/multiple reaction monitoring (SRM/MRM) have been very useful, especially in the case of gluten from wheat, rye and barley, and allergens from lentil, soy, and fruit. Conventional 1D and 2-DE immunoblotting have been further widely used. For animal-based food allergens, the widely used technologies are 1D and 2-DE immunoblotting followed by MALDI-TOF/TOF, and more recently LC-MS/MS, which is becoming useful to assess egg, fish, or milk allergens. The detection and quantification of allergenic proteins using mass spectrometry-based proteomics are promising and would contribute to greater accuracy, therefore improving consumer information.

Potential Role of the Micro-Immunotherapy Medicine 2LALERG in the Treatment of Pollen-Induced Allergic Inflammation

In this study, we evaluated the efficacy of a micro-immunotherapy medicine (MIM), 2LALERG, in a preclinical model of allergic respiratory disease sensitized with birch pollen extract (BPE). BALB/c mice were immunized with BPE, or saline solution, and were then challenged. Micro-immunotherapy medicine pillules were diluted in water, and 3 doses (0.75; 1.5; 3 mg/mouse) were tested and compared to vehicle control (3 mg/mouse). Treatments and vehicle were orally administered by gavage for 10 days. Micro-immunotherapy medicine (0.75 mg/mouse) reduced the number of total cells as well as the levels of interleukin (IL)-13 in bronchoalveolar lavage fluid (BALF) compared to vehicle control. Eosinophils in BALF tended to be lower compared to vehicle group, and the difference is close to significance. Histological analysis in the lungs confirms a moderate effect of MIM (0.75 mg/mice) on inflammatory infiltration and mucus production. Serum levels of IL-5 in MIM (0.75 mg/mouse)-treated mice were lower compared to vehicle; IL-4 levels tended to be lower too. Total immunoglobulin E (IgE) decreased in serum of MIM (1.5 and 0.75 mg/mouse) groups compared to vehicle control. Micro-immunotherapy medicine exerted the highest effect at the lowest dose tested. Micro-immunotherapy medicine resolved the local and systemic inflammation, even if partially, in a model of pollen-induced, IgE-mediated inflammation.

Progress in the Analysis of Food Allergens through Molecular Biology Approaches

Food allergies associated with class E immunoglobulins (IgE) are a serious health problem that affects between 1% and 10% of the population of developing countries, with a variability that depends on the geographical area and age range considered. These allergies are caused by a cross-link reaction between a specific food protein (the allergen) and the host IgE. Allergic reactions can range from mild itching to anaphylactic shock and there are no clues to predict the effects of an allergen. Strict avoidance of allergenic food is the only way to avoid possible serious allergic reactions. In the last 30 years a growing number of molecular studies have been conducted to obtain information on the diffusion of food allergens and to establish the structural basis of their allergenicity. At the same time, these studies have also allowed the development of molecular tools (mainly based on synthetic peptides and recombinant allergens) that can be of great help for diagnostic and therapeutic approaches of food allergies. Accordingly, this review focuses on advances in the study of food allergens made possible by molecular technologies and how results and technologies can be integrated for the development of a systematic food molecular allergology. The review may be of interest both to scientists approaching this field of investigation and to physicians who wish to have an update on the progress of research in diagnosis and therapy of food allergies.