Prediction Of Allergenicity Research Articles

The Value of Short Amino Acid Sequence Matches for Prediction of Protein Allergenicity

Typically, genetically engineered crops contain traits encoded by one or a few newly expressed proteins. The allergenicity assessment of newly expressed proteins is an important component in the safety evaluation of genetically engineered plants. One aspect of this assessment involves sequence searches that compare the amino acid sequence of the protein to all known allergens. Analyses are performed to determine the potential for immunologically based cross-reactivity where IgE directed against a known allergen could bind to the protein and elicit a clinical reaction in sensitized individuals. Bioinformatic searches are designed to detect global sequence similarity and short contiguous amino acid sequence identity. It has been suggested that potential allergen cross-reactivity may be predicted by identifying matches as short as six to eight contiguous amino acids between the protein of interest and a known allergen. A series of analyses were performed, and match probabilities were calculated for different size peptides to determine if there was a scientifically justified search window size that identified allergen sequence characteristics. Four probability modeling methods were tested: (1) a mock protein and a mock allergen database, (2) a mock protein and genuine allergen database, (3) a genuine allergen and genuine protein database, and (4) a genuine allergen and genuine protein database combined with a correction for repeating peptides. These analyses indicated that searches for short amino acid sequence matches of eight amino acids or fewer to identify proteins as potential cross-reactive allergens is a product of chance and adds little value to allergy assessments for newly expressed proteins.

GEL: Grid execution language

We consider the problem of programming parallel applications for a Grid environment, in the presence of the two main challenges (i) high-latency communications and (ii) heterogeneity. We describe a new scripting language, GEL, whose semantics have been designed for execution on a heterogeneous, distributed computer. The language provides syntactic constructs for while loops, conditionals and explicitly parallel execution. The language is designed to work well given these two challenges, and to allow succinct representation of parallel programs, resulting in easier-to-maintain code. The programs can use legacy applications without re-engineering, and do not explicitly refer to resource names or use middleware-specific references. This middleware-independence allows us to execute the same script on an SMP machine, cluster or Grid. We describe three example applications written in GEL: an optimisation problem solved using a swarm algorithm; an allergenicity prediction pipeline; and transcript analysis for tissue-specific gene expression. We have run these scripts unchanged on an SMP machine, on PBS, SGE and LSF clusters, and on a Globus-based Grid.

A novel approach for investigation of specific and cross-reactive IgE epitopes on Bet v 1 and homologous food allergens in individual patients

Background: A clinically relevant allergic reaction requires recognition of at least two different epitopes on the surface of the allergen by IgE. These epitopes may be specific or cross-reactive. Moreover, patterns of IgE reactivity may be patient-specific. The aim of our study was to compare specific and cross-reactive IgE epitopes and epitope patterns between individual patients. We used Bet v 1-related food allergy as a model. Methods: Five patients were investigated by cross-competitive ELISA for specific and cross-reacting IgE to Bet v 1, and its homologues Gly m 4 (soybean), Ara h 8 (peanut), and Pru av 1 (cherry). Allergen-specific as well as cross-reactive IgE epitopes were assessed by competitive immunoscreening of a phage-displayed random 7-mer peptide library using polyclonal purified IgE from individual sera. The resulting peptide mimics were mapped on the surface of the 3D-structure of the allergens using a computer-based algorithm. Results: Competitive immunoscreening and epitope mapping identified patient-specific IgE epitope patterns. However, one IgE-binding surface area that was recognized by all patients and two recognized by three patients were identified on all four proteins. These results are consistent with the determination of IgE cross-reactivity of the individual patients’ sera against the four recombinant allergens by cross-competitive ELISA. Conclusions: Selection of phage-displayed peptide mimics with serum IgE from allergic patients in combination with computer-based mapping of the peptide mimics onto the surface of the three-dimensional allergen structure is a promising novel tool to investigate IgE epitope specificity in individual patients. Such basic information on epitope structure may be used for prediction of cross-reactivity and potential allergenicity of novel foods.

Allergies alimentaires : mécanismes physiopathologiques, identification des allergènes alimentaires

Food allergy is an immunologically mediated adverse reaction to foods which only affects genetically predisposed individuals. In the general population of adults, the prevalence is about 3.5% while it is 5–8% in children but an increase is observed and the severity of the clinical manifestations is also increasing. The allergic reaction develops in two steps. The sensitization occurs at the first contact with the allergen without any symptoms. A second contact elicits the reaction, then the release of mediators and the onset of clinical manifestations. Allergenic foods are numerous and vary according to the dietary habits of the groups of population who are affected but about 12 foods are responsible for most of the food allergies. Each allergenic food contains several constituents, i.e. proteins, that are responsible for the allergenicity and within each allergen many immunoreactive regions, i.e. epitopes, are spread all over the protein molecule. The variability of the immune response of allergic humans to those different compounds makes the prediction of allergenicity of a new food uncertain but the characterization of allergenic structures in foods allows to better understand some of the pathophysiological mechanisms of the allergic reaction.

Statistical Evaluation of Local Alignment Features Predicting Allergenicity Using Supervised Classification Algorithms

Background: Recently, two promising alignment-based features predicting food allergenicity using the k nearest neighbor (kNN) classifier were reported. These features are the alignment score and alignment length of the best local alignment obtained in a database of known allergen sequences. Methods: In the work reported here a much more comprehensive statistical evaluation of the potential of these features was performed, this time for the prediction of allergenicity in general. The evaluation consisted of the following four key components. (1) A new high quality database consisting of 318 carefully selected, non-redundant allergens and 1,007 sequences carefully selected to be non-allergens. (2) Three different supervised algorithms: the kNN classifier, the Bayesian linear Gaussian classifier, and the Bayesian quadratic Gaussian classifier. (3) A large set of local alignment procedures defined using the FASTA3 alignment program by means of a wide range of different parameter settings. (4) Novel performance curves, alternative to conventional receiver-operating characteristic curves, to display not only average behaviors but also statistical variations due to small data sets. Results: The linear Gaussian classifier proved most useful among the tested supervised machine learning algorithms, closely followed by the quadratic Gaussian equivalent and kNN. The overall best classification results were obtained with a novel feature vector consisting of the combined alignment scores derived from local alignment procedures using different substitution matrices. Conclusions: The models reported here should be useful as a part of an integrated assessment scheme for potential protein allergenicity and for future comparisons with alternative bioinformatic approaches.

Allermatch™, a webtool for the prediction of potential allergenicity according to current FAO/WHO Codex alimentarius guidelines

BackgroundNovel proteins entering the food chain, for example by genetic modification of plants, have to be tested for allergenicity. Allermatch™ is a webtool for the efficient and standardized prediction of potential allergenicity of proteins and peptides according to the current recommendations of the FAO/WHO Expert Consultation, as outlined in the Codex alimentarius.DescriptionA query amino acid sequence is compared with all known allergenic proteins retrieved from the protein databases using a sliding window approach. This identifies stretches of 80 amino acids with more than 35% similarity or small identical stretches of at least six amino acids. The outcome of the analysis is presented in a concise format. The predictive performance of the FAO/WHO criteria is evaluated by screening sets of allergens and non-allergens against the Allermatch databases. Besides correct predictions, both methods are shown to generate false positive and false negative hits and the outcomes should therefore be combined with other methods of allergenicity assessment, as advised by the FAO/WHO.ConclusionsAllermatch™ provides an accessible, efficient, and useful webtool for analysis of potential allergenicity of proteins introduced in genetically modified food prior to market release that complies with current FAO/WHO guidelines.

Bioinformatics for characterisation of allergens, allergenicity and allergic crossreactivity

Allergies are a major cause of chronic ill health in industrialised countries with the incidence of reported cases steadily increasing. This Research Focus details how bioinformatics is transforming the field of allergy through providing databases for management of allergen data, algorithms for characterisation of allergic crossreactivity, structural motifs and B- and T-cell epitopes, tools for prediction of allergenicity and techniques for genomic and proteomic analysis of allergens.

Prediction of allergenicity of gene-modified foods by serum-based testing.

On the basis of applying the IFBC/ILSI decision tree in a number of cases, a refinement of the scheme is suggested. Large differences in allergenic potential may be obtained by altering the route of administration of an allergen. Because an inhalation allergen can induce symptoms at different threshold doses depending on whether it is introduced via the inhalation or oral route, we propose that double-blind, placebo-controlled food challenges be performed in all cases of inhalation allergens being present in foods. Even proteins never previously ingested may cause IgE binding and elicit clinically relevant symptoms.

Food allergy—towards predictive testing for novel foods

The risks associated with IgE-mediated food allergy highlight the need for methods to screen for potential food allergens. Clinical and immunological tests are available for the diagnosis of food allergy to known food allergens, but this does not extend to the evaluation, or prediction of allergenicity in novel foods. This category includes foods produced using novel processes, genetically modified (GM) foods, and foods that might be used as alternatives to traditional foods. Through the collation and analysis of the protein sequences of known allergens and their epitopes, it is possible to identify related groups which correlate with observed clinical cross-reactivities. 3-D modelling extends the use of sequence data and can be used to display eptiopes on the surface of a molecule. Experimental models support sequence analysis and 3-D modelling. Observed crossreactivities can be examined by Western blots prepared from native 2-D gels of a whole food preparation (e.g. hazelnut, peanut), and common proteins identified. IgEs to novel proteins can be raised in Brown Norway rat (a high IgE responder strain), and the proteins tested in simulated digest to determine epitope stability. Using the CSL serum bank, epitope binding can be examined through the ability of an allergen to cross-link the high affinity IgE receptor and thereby release mediators using in vitro cell-based models. This range of methods, in combination with data mining, provides a variety of screening options for testing the potential of a novel food to be allergenic, which does not involve prior exposure to the consumer.