Allergen Sequences Research Articles

Techno-Functions and Safety Concerns of Plant-Based Peptides in Food Matrices.

Plant-based peptides (PBPs) benefit functional food development and environmental sustainability. Proteolysis remains the primary method of peptide production because it is a mild and nontoxic technique. However, potential safety concerns still emanate from toxic or allergenic sequences, amino acid racemization, iso-peptide bond formation, Maillard reaction, dose usage, and frequency. The main aim of this review is to investigate the techno-functions of PBPs in food matrices, as well as their safety concerns. The distinctive characteristics of PBPs exhibit their techno-functions for improving food quality and functionality by contributing to several crucial food formulations and processing. The techno-functions of PBPs include solubility, hydrophobicity, bitterness, foaming, oil-binding, and water-holding capacities, which subsequently affect food matrices. The safety and quality of foodstuff containing PBPs depend on the proper source of plant proteins, the selection of processing approaches, and compliance with legal regulations for allergen labeling and safety evaluations. The safety concerns in allergenicity and toxicity were discussed. The conclusion is that food technologists must apply safe limits and consider potential allergenic components generated during the development of food products with PBPs. Therefore, functional food products containing PBPs can be a promising strategy to provide consumers with wholesome health benefits.

In silico-based Safety Assessment of the EPSPS CP4 single event expressed in genetically modified crops

In silico-based Safety Assessment of the EPSPS CP4 single event expressed in genetically modified crops

T cells specific to multiple Bet v 1 peptides are highly cross-reactive toward the corresponding peptides from the homologous group of tree pollens.

Allergens from Fagales trees frequently cause spring allergy in Europe, North America, and some parts of Asia. The definition of the birch homologous group, which includes birch (Bet v), oak (Que a), alder (Aln g), hazel (Cor a), hornbeam (Car b), beech (Fag s), and chestnut (Cas s), is based on high allergen sequence identity and extensive IgE cross-reactivity. Clinical effect was seen during the alder/hazel, birch, and oak pollen seasons after treatment with tree SLIT-tablets containing only birch allergen extract. Here, we characterize T-cell reactivity with respect to epitope specificities and cross-reactivity toward various Bet v 1 family members, (PR-10/group 1 major allergens). This cross-reactivity may be part of the immunological basis of clinical effect or cross-protection when exposed to birch homologous tree species. T-cell lines were generated from 29 birch-allergic individuals through stimulation of peripheral blood mononuclear cells (PBMCs) with birch/Bet v or oak/Que a allergen extracts. T-cell responses to allergen extracts, purified group 1 allergens, and overlapping 20-mer peptides (Bet v 1, Aln g 1, Cor a 1, and Que a 1) were investigated by T-cell proliferation and cytokine production. Cross-reactivity was evaluated based on Pearson's correlations of response strength and further investigated by flow cytometry using tetramer staining for homologous peptide pairs. T-cell reactivity toward extracts and group 1 allergens from across the birch homologous group was observed for birch/Bet v as well as oak/Que a T-cell lines. T-cell lines responded to multiple Bet v 1 homologous peptides from Aln g 1 and Cor a 1 and a subset of Que a 1 peptides. Significant Pearson's correlations between frequently recognized peptides derived from Bet v 1 and the corresponding peptides derived from alder, hazel, and oak strongly supported the T-cell cross-reactivity toward these allergens. Cross-reactivity between birch and birch homologous peptides was confirmed by pMHCII tetramer staining. T cells from birch tree pollen allergic individuals respond to multiple trees within the birch homologous group in accordance with the level of sequence homology between Bet v 1 family members, (PR-10 allergens) from these allergen sources, confirming the basis for clinical cross-protection.

Computational prediction of allergenic proteins based on multi-feature fusion.

Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.

Identifying Similar Allergens and Potentially Cross-Reacting Areas Using Structural Database of Allergenic Proteins (SDAP) Tools and D-Graph.

The Structural Database of Allergenic Proteins (SDAP) provides rapid search tools to identify similarities among allergens, their IgE epitopes, and to determine the potential allergenicity of any novel protein. Many labs have identified IgE-binding proteins and their antibody binding or T cell epitopes using dotspots or microarrays. This chapter describes how to determine the relationship of these proteins and peptides to known allergens using the tools implemented in SDAP. One can also search with these smaller peptide similarity search tool implemented in SDAP to find similar sequences with low property distance (PD) values in the over 1500 sequences of allergens. The sequences can be compared by mapping on the surface of the protein structures provided for nearly all the allergens in SDAP. Once the user has a unique list of similar sequences, they can be graphed in 2D according to interpeptide PD values calculated automatically by the D-graph program. This chapter provides a step-by-step description of how to do this, starting from a protein similar to the Ole e 1 (olive pollen) allergen family.

Muscle proteome profile and potential allergens of premature hilsa shad (Tenualosa ilisha)

In this study, for the first time, we profile the actual protein sequences of known and potential allergens of premature hilsa shad (Tenualosa ilisha), an economically important fish of South Asian countries. Proteomics was employed to dissect the muscle tissue proteome collected from the riverine habitat hilsa samples. We successfully identified and quantified >600 proteins wherein many known allergens such as parvalbumins, beta-enolase, and creatine kinases were identified as abundant proteins in muscle tissue. Label-free quantitative proteomics revealed significant differences in the abundance of myosin, dynein, gamma-enolase, creatine kinase B-type, and collagens between the two hilsa samples. In silico analysis determined the allergen potential of these proteins. Notably, our findings indicate that many well-characterized allergen proteins are developmentally regulated in the muscle tissue. Thus, the profiling of a muscle tissue proteome map would further facilitate fundamental and applied research on hilsa fish and greatly improve our understanding of the crucial issue of hilsa customers' nutritional safety.

IgE and IgG4 Epitopes of Dermatophagoides and Blomia Allergens before and after Sublingual Immunotherapy

Sublingual immunotherapy (SLIT) is used worldwide to treat house dust mites (HDM) allergy. Epitope specific immunotherapy with peptide vaccines is used far less, but it is of great interest in the treatment of allergic reactions, as it precludes the drawbacks of allergen extracts. The ideal peptide candidates would bind to IgG, blocking IgE-binding. To better elucidate IgE and IgG4 epitope profiles during SLIT, sequences of main allergens, Der p 1, 2, 5, 7, 10, 23 and Blo t 5, 6, 12, 13, were included in a 15-mer peptide microarray and tested against pooled sera from 10 patients pre- and post-1-year SLIT. All allergens were recognized to some extent by at least one antibody isotype and peptide diversity was higher post-1-year SLIT for both antibodies. IgE recognition diversity varied among allergens and timepoints without a clear tendency. Der p 10, a minor allergen in temperate regions, was the molecule with more IgE-peptides and might be a major allergen in populations highly exposed to helminths and cockroaches, such as Brazil. SLIT-induced IgG4 epitopes were directed against several, but not all, IgE-binding regions. We selected a set of peptides that recognized only IgG4 or were able to induce increased ratios of IgG4:IgE after one year of treatment and might be potential targets for vaccines.

Comparative analysis of allergenicity and predicted linear epitopes in α and β parvalbumin from turbot (Scophthalmus maximus).

Parvalbumin (PV) can be subdivided into two phylogenetic lineages, αPV and βPV. The bony fish βPV is considered a major fish allergen. However, there is no available report on the immunological property and epitope mapping of bony fish αPV. To characterize the allergenic property of bony fish αPV and investigate the difference in allergenic property of bony fish αPV and βPV, turbot (Scophthalmus maximus) αPV and βPV were identified by mass spectrometry and were expressed in Escherichia coli system in this study. Spectra analysis and three-dimensional (3D) modeling showed the similar structure between αPV and βPV. However, αPV exhibited lower immunoglobulin E/immunoglobulin G (IgE/IgG) binding capacity than βPV. Three identified βPV epitopes possessed higher IgE reactivity and more hydrophobic residues than three identified αPV epitopes. In addition, less similarity in sequence homology of αPV epitopes was observed with allergen sequences in database. These finding expanded information on fish PV epitopes and substantiated the difference in allergenicity and epitope mapping between fish αPV and βPV, which will improve the epitope-based detection tools of PV and diagnostic of PV induced fish allergy. © 2023 Society of Chemical Industry.

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 neededto 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.

Screening of B-cell epitopes of Der-p1 and Der-p2 major aeroallergens by computational approach for designing immunotherapeutics

Introduction and Aim: Allergic diseases are IgE-mediated hypersensitivity reactions affecting approximately 30% of the general population globally. Dermatophagoides pteronyssinus (Der-p) is the most prevalent house dust mite (HDM) species consisting of 23 mite allergen groups. Among these, group 1 and 2 are major allergenic proteins, which causes allergic asthma in 80% of sensitized individuals, with elevated IgE titres in the serum. This study involves in silico analysis of potential B-cell epitopes of group 1 and group 2 of Der-p, which can be utilized in designing immunotherapeutic vaccines. Materials and Methods: Allergen sequences obtained from the database- International Union of Immunological Societies (IUIS), for predicting of B-cell epitopes. The physiochemical properties and secondary structures of the obtained sequence were evaluated. The sequences were further subjected to determining antigenicity, surface accessibility, and prediction of linear and discontinuous B-cell epitope by utilizing IEDB tools. Results: The linear and discontinuous B-cell epitopes of Der-p1 and Der-p2 aeroallergen were predicted. Further, Der-p1 and Der-p2 showed 6 linear epitopes each respectively. Conformational epitopes predicted were 123 of Der-p1 and 72 of Der-p2 respectively, by the ElliPro tool. Based on the structure, antigenicity, and surface accessibility, only 10% of Der-p1 and Der-p2 which binds to B-cell epitopes are linear and the majority are discontinuous. Conclusion: The linear and conformational epitopes of Der-p1 and Der-p2 are predicted using in silico tools. These identified epitopes might be useful for developing epitope-based immunotherapeutics for HDM allergy.

IgE and IgG4 Epitope Mapping of Food Allergens with a Peptide Microarray Immunoassay.

Peptide microarrays are a powerful tool to identify linear epitopes of food allergens in a high-throughput manner. The main advantages of the microarray-based immunoassay are as follows: the possibility to assay thousands of targets simultaneously, the requirement of a low volume of serum, the more robust statistical analysis, and the possibility to test simultaneously several immunoglobulin subclasses. Among them, the last one has a special interest in the field of food allergy, because the development of tolerance to food allergens has been associated with a decrease in IgE and an increase in IgG4 levels against linear epitopes. However, the main limitation to the clinical use of microarray is the automated analysis of the data. Recent studies mapping the linear epitopes of food allergens with peptide microarray immunoassays have identified peptide biomarkers that can be used for early diagnosis of food allergies and to predict their severity or the self-development of tolerance. Using this approach, we have worked on epitope mapping of the two most important food allergens in the Spanish population, cow's milk, and chicken eggs. The final aim of these studies is to define subsets of peptides that could be used as biomarkers to improve the diagnosis and prognosis of food allergies. This chapter describes the protocol to produce microarrays using a library of overlapping peptides corresponding to the primary sequences of food allergens and data acquisition and analysis of IgE and IgG4 binding epitopes.

ProAll-D: protein allergen detection using long short term memory - a deep learning approach.

BackgroundAn allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens.MethodThis work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration.ResultsThe Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %.ConclusionAs the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data.

Comprehensive COMPARE database reduces allergenic risk of novel food proteins

ABSTRACT The comprehensiveness of the allergen database used to bioinformatically compare a novel food protein with known allergens is critical to the ability to assess the allergenic risk of newly expressed proteins in genetically engineered crops. The strength of the relationship between a candidate GE protein’s amino acid sequence and that of known allergens is used to predict cross-reactive risk. The number of truly novel allergen sequences added annually to the COMPARE database reflects on the comprehensiveness of our knowledge of allergen amino acid sequence diversity. Here, we investigated the most recent five years of updates to the COMPARE allergen database for truly novel entries. Results indicate that few truly novel sequences are added each year, suggesting that the database and our knowledge of allergen sequence diversity is currently quite comprehensive, and that current in silico prediction of allergenic risk for novel food proteins is robust.

In silico and in vivo approaches to recombinant multi-epitope immunogen of GroEL provides efficient cross protection against S. Typhimurium, S. flexneri, and S. dysenteriae

ObjectivesStress or Heat Shock Proteins (HSPs) have been included in various operations like protein folding, autophagy, and apoptosis. HSP families recognize as protective antigens in a wide range of bacteria because they have been conserved through evolution. Due to their homology as well as antigenicity they are competent for applying in cross-protection against bacterial diseases. MethodsIn the present study, bioinformatics approaches utilized to design epitope-based construction of Hsp60 (or GroEL) protein. In this regard, potential B-cell and T-cell epitopes except for allergenic sequences were selected by immunoinformatic tools. The structural and functional aspects of the DNA, RNA, and protein levels were assessed by bioinformatics software. Following in silico investigations, recombinant GroEL multi-epitope of Salmonella typhi was expressed, purified, and validated. Mouse groups were immunized with recombinant protein and humoral immune response was measured by enzyme linked immunosorbent assay (ELISA). Animal challenge against Salmonella Typhimurium, Shigella flexneri, and Shigella dysenteriae was evaluated. Resultsrecombinant protein expression and purification with 14.3 kilodaltons (kDa) was confirmed by SDS-PAGE and western blotting. After animal administration, the immunoglobulins evaluated increase after each immunization. Immunized antisera exhibited 80%, 40%, and 40% protection against the lethal dose infection by S. Typhimurium, S. flexneri, and S. dysenteriae respectively. Passive immunization conferred 50%, 30%, and 30% protection in mice against S. Typhimurium, S. flexneri and S. dysentery respectively. In addition, bacterial organ load had exhibited a significant decrease in colony forming unit (CFU) in the liver and spleen of the immunized mice compared to the control. ConclusionOur study demonstrates the efficacy of S. Typhi recombinant GroEL multi-epitope to consider as a universal immunogen candidate versus multiple bacterial pathogens.

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

The effect of nitrogen fertilization on the expression of protein in wheat and tritordeum varieties using a proteomic approach

Wheat, an essential ingredient for several bakery preparations, is also responsible for gluten-related diseases in sensitive subjects. The effect of the N fertilization rate (80 vs 160 kg N ha−1) on gluten protein expression profile has been evaluated considering two soft wheats (landrace and modern) and one tritordeum cultivar (cv), grown in the same experimental field in North Italy. The proteins of refined flour were characterized through advanced proteomic approaches, including chromatography (RP-HPLC) and electrophoresis. A static model system was used to simulate in vitro digestion and the digestome peptides were examined by mass spectrometry and in silico approaches, to investigate the celiac and allergenic sequences. The CD-toxic epitopes in the digested samples were quantified by means of a R5 ELISA assay.The N fertilization rate increased the grain protein content, but it did not lead to any difference in gluten composition, with exception of glu/glia ratio in the modern wheat cv. Moreover, the gluten composition and the occurrence of toxic/allergenic epitopes varied to a great extent, according mostly to the genotype. A lower immunoreactivity, determined using R5 ELISA, was detected for the digested tritordeum flours than for the landrace (-51%) or modern (-58%) cvs, while no significant difference was observed for the N rates between each genotype. In silico analysis showed that tritordeum has fewer CD epitopes belonging to the ω-gliadins and a lower LMW-GS than the landrace or modern cv. Tritordeum presented fewer α-gliadin allergenic epitopes than the modern wheat cv. The lower frequency of celiac epitopes in tritordeum, compared to the old and the modern wheat, is probably due to the absence of a D genome.

Prediction of Allergen and Non-Allergen Proteins Sequence via Chou's 5-Step Rule

Some specific kinds of proteins are responsible for the risk of immediate type I allergic reaction. Therefore, the proteins that are made to use in the consumer product should be checked for their allergic reactions before introducing them in the market. The FAO/WHO instructions for the assessment of allergic proteins depend on the linear sequence window identity and short peptide hits misclassify many proteins as allergen proteins. This study introduces the AllerPredictor model that predicts the allergen & non-allergen proteins depending on the sequence of proteins. Data was downloaded from two major databases, FARRP and UniProtKB. The results of this model were validated with the help of self-consistency testing, independence testing, and jackknife testing. The accuracy for self-consistency validation is 99.89%, for the independence testing is 74.23%, and for 10-fold cross-validation, it is 97.17%. To predict the allergen and non-allergen proteins, this AllerPredictor model has a better accuracy than other existing methods.

A proteomic analysis of peanut seed at different stages of underground development to understand the changes of seed proteins.

In order to obtain more valuable insights into the protein dynamics and accumulation of allergens in seeds during underground development, we performed a proteomic study on developing peanut seeds at seven different stages. A total of 264 proteins with altered abundance and contained at least one unique peptide was detected by matrix-assisted laser desorption ionization time-of-flight/time-of-flight mass spectrometry (MALDI-TOF/TOF MS). All identified proteins were classified into five functional categories as level 1 and 20 secondary functional categories as level 2. Among them, 88 identified proteins (IPs) were related to carbohydrate/ amino acid/ lipid transport and metabolism, indicating that carbohydrate/amino acid/ lipid metabolism played a key role in the underground development of peanut seeds. Hierarchical cluster analysis showed that all IPs could be classified into eight cluster groups according to the abundance profiles, suggesting that the modulatory patterns of these identified proteins were complicated during seed development. The largest group contained 41 IPs, the expression of which decreased at R 2 and reached a maximum at R3 but gradually decreased from R4. A total of 14 IPs were identified as allergen-like proteins by BLAST with A genome (Arachis duranensis) or B genome (Arachis ipaensis) translated allergen sequences. Abundance profile analysis of 14 identified allergens showed that the expression of all allergen proteins was low or undetectable by 2-DE at the early stages (R1 to R4), and began to accumulate from the R5 stage and gradually increased. Network analysis showed that most of the significant proteins were involved in active metabolic pathways in early development. Real time RT-PCR analysis revealed that transcriptional regulation was approximately consistent with expression at the protein level for 8 selected identified proteins. In addition, some amino acid sequences that may be associated with new allergens were also discussed.

Variation in IgE binding potencies of seven Artemisia species depending on content of major allergens

BackgroundArtemisia weed pollen allergy is important in the northern hemisphere. While over 350 species of this genus have been recorded, there has been no full investigation into whether different species may affect the allergen diagnosis and treatment. This study aimed to evaluate the variations in amino acid sequences and the content of major allergens, and how these affect specific IgE binding capacity in representative Artemisia species.MethodsSix representative Artemisia species from China and Artemisia vulgaris from Europe were used to determine allergen amino acid sequences by transcriptome, gene sequencing and mass spectrometry of the purified allergen component proteins. Sandwich ELISAs were developed and applied for Art v 1, Art v 2 and Art v 3 allergen quantification in different species. Aqueous pollen extracts and purified allergen components were used to assess IgE binding by ELISA and ImmunoCAP with mugwort allergic patient serum pools and individual sera from five areas in China.ResultsThe Art v 1 and Art v 2 homologous allergen sequences in the seven Artemisia species were highly conserved. Art v 3 type allergens in A. annua and A. sieversiana were more divergent compared to A. argyi and A. vulgaris. The allergen content of Art v 1 group in the seven extracts ranged from 3.4% to 7.1%, that of Art v 2 from 1.0% to 3.6%, and Art v 3 from 0.3% to 10.5%. The highest IgE binding potency for most Chinese Artemisia allergy patients was with A. annua pollen extract, followed by A. vulgaris and A. argyi, with A. sieversiana significantly lower. Natural Art v 1-3 isoallergens from different species have almost equivalent IgE binding capacity in Artemisia allergic patients from China.Conclusion and clinical relevanceThere was high sequence similarity but different content of the three group allergens from different Artemisia species. Choice of Artemisia annua and A. argyi pollen source for diagnosis and immunotherapy is recommended in China.

Effect of Instant Controlled Pressure Drop (DIC) Treatment on the Detection of Nut Allergens by Real Time PCR.

Tree nuts show nutritional properties and human health benefits. However, they contain allergenic proteins, which make them harmful to the sensitised population. The presence of tree nuts on food labelling is mandatory and, consequently, the development of suitable analytical methodologies to detect nuts in processed foods is advisable. Real-Time PCR allowed a specific and accurate amplification of allergen sequences. Some food processing methods could induce structural and/or conformational changes in proteins by altering their allergenic capacity, as well as produce the fragmentation and/or degradation of genomic DNA. In this work, we analysed by means of Real-Time PCR, the influence of pressure and thermal processing through Instant Controlled Pressure Drop (DIC) on the detectability of hazelnut, pistachio and cashew allergens. The detection of targets in hazelnut, pistachio and cashew (Cor a 9, Pis v 1 and Ana o 1, respectively) is affected by the treatment to different extents depending on the tree nut. Results are compared to those previously obtained by our group in the analysis of different treatments on the amplificability of the same targets. Reduction in amplificability is similar to that reported for some autoclave conditions. Our assays might allow for the detection of up to 1000 mg/kg of hazelnut, pistachio and cashew flours after being submitted to DIC treatment in food matrices.