Computational tools for the study of allergens

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.

Computational approach for the identification of putative allergens from Cucurbitaceae family members

Certain substances referred to as allergens, induce hypersensitivity (allergic reactions) which normally are considered to be innocuous, are small in size and incite IgE response. This study was focused to predict the putative allergens from other Cucurbitaceae family members using computational approach by analyzing the already reported allergens of the same family. The four reported allergens Cuc m 1, Cuc m 2, Cuc m 3 and Citr I 2 of Cucurbitaceae family were obtained from International Union of Immunological Societies, in which three were from Cucumis melo (Muskmelon) and one from Citrullus lanatus (Watermelon) respectively. BlastP analysis reported 44 similar sequences to these allergens from other members of Cucurbitaceae family namely Cucurbita moschata, Cucurbita pepo and Cucurbita maxima. The allergenicity of these sequences was predicted using AlgPred tool in which it revealed 26 protein sequences as putative allergens. These selected sequences were further analyzed for their physicochemical properties using ProtParam tool in which 13 sequences were found to satisfy the required parameters, and therefore further analyzed by AllerMatch™ and AllergenOnline tools to check the Codex Alimentarius rules for allergens. Finally, 13 sequences that were selected were structurally analyzed for similarity using PROMALS3D tool and phylogenetic relationship was established with the reported allergens using MEGA-X software. It was concluded that 13 sequences from Cucurbitaceae family belonging to different species of Pumpkin showed potential allergenicity based on the computational analysis that possibly can play a role in allergies and cross reactivity.

Specific IgY anti-group 1 dust mite allergens induced by unglycosylated synthetic oligopeptides

Introduction: The use of specific antibodies capable of detecting allergens of the group 1 of house dust mites represents a potential strategy to reduce exposure and clinical symptomatology associated with asthma and allergic rhinitis. Objective: To produce and purify chicken antibodies specific for the dust mites Dermatophagoides sp. and B. tropicalis using the IgY technology. Materials and methods: We designed and synthesized oligopeptides showing immunogenic epitopes of Der p1, Der f1, and Blo t1. These were used to produce IgY antibodies in Hy Line Brown chickens. IgY were extracted from egg yolk using thiophilic chromatography. The immunogenicity and specificity were assayed by indirect ELISA and Dot Blot. Results: We obtained high reactivity of IgY antibodies against epitopes of allergens present in whole body mites extracts of D. farinae, D. pteronyssinus, and B. tropicalis. The highest IgY levels were registered between days 32 and 40 after immunization. The antibodies showed high immunoreactivity and specificity towards D. farinae proteins with detection limits above 0.03 μg of mite proteins under the experimental conditions used. Purified IgY did not show significant reactivity when binding to Periplaneta americana extract. Conclusion: The IgY technology allowed the production of specific antibodies against house dust mites group 1 allergens using non-glycosylated synthetic peptides. To our knowledge, this is the first time that this immunochemicals are used in the detection of mites of medical relevance.

An Overview of Bioinformatics Tools and Resources in Allergy

The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

Identification of B-cell epitopes in an antigen for inducing specific class of antibodies

Background

In the past, numerous methods have been developed for predicting antigenic regions or B-cell epitopes that can induce B-cell response. To the best of authors’ knowledge, no method has been developed for predicting B-cell epitopes that can induce a specific class of antibody (e.g., IgA, IgG) except allergenic epitopes (IgE). In this study, an attempt has been made to understand the relation between primary sequence of epitopes and the class of antibodies generated.

Results

The dataset used in this study has been derived from Immune Epitope Database and consists of 14725 B-cell epitopes that include 11981 IgG, 2341 IgE, 403 IgA specific epitopes and 22835 non-B-cell epitopes. In order to understand the preference of residues or motifs in these epitopes, we computed and compared amino acid and dipeptide composition of IgG, IgE, IgA inducing epitopes and non-B-cell epitopes. Differences in composition profiles of different classes of epitopes were observed, and few residues were found to be preferred. Based on these observations, we developed models for predicting antibody class-specific B-cell epitopes using various features like amino acid composition, dipeptide composition, and binary profiles. Among these, dipeptide composition-based support vector machine model achieved maximum Matthews correlation coefficient of 0.44, 0.70 and 0.45 for IgG, IgE and IgA specific epitopes respectively. All models were developed on experimentally validated non-redundant dataset and evaluated using five-fold cross validation. In addition, the performance of dipeptide-based model was also evaluated on independent dataset.

Conclusion

Present study utilizes the amino acid sequence information for predicting the tendencies of antigens to induce different classes of antibodies. For the first time, in silico models have been developed for predicting B-cell epitopes, which can induce specific class of antibodies. A web service called IgPred has been developed to serve the scientific community. This server will be useful for researchers working in the field of subunit/epitope/peptide-based vaccines and immunotherapy (http://crdd.osdd.net/raghava/igpred/).

Reviewers

This article was reviewed by Dr. M Michael Gromiha, Dr Christopher Langmead (nominated by Dr Robert Murphy) and Dr Lina Ma (nominated by Dr Zhang Zhang).

AllerTOP--a server for in silico prediction of allergens

Background

Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.

Results

A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z₁, z₂ and z₃) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.

Conclusions

AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin.

The group 10 allergen of Dermatophagoides farinae (Acari: Pyroglyphidae): cDNA cloning, sequence analysis, and expression in Escherichia coli BL21

Dermatophagoides farinae Hughes, American house dust mite, is highly allergenic, producing symptoms in people worldwide. Identifying and cloning the allergens in this species may enable better diagnostic and therapeutic approaches. Here, we cloned, sequenced, and expressed the full-length cDNA encoding D. farinae group 10 allergen (Der f 10) isolated from dust mites in China. Bioinformatic analysis indicated that the 888 bp sequence encoded a cytoskeleton protein 295 amino acids long, with a molecular weight of approximately equal 34 kDa. Sequence alignment with the group 10 allergens of Pyroglyphidae, Acaridae, and Glycyphagidae families revealed that the group 10 allergen from D. farinae is 95% similar to D. pteronyssinus Trouessart and Psoroptes ovis (Hering). These findings lay the groundwork for future studies, including large-scale production of recombinant Der f 10 allergen for diagnostic and therapeutic agents.

Brachypodium distachyon as a model for defining the allergen potential of non-prolamin proteins

Epitope databases and the protein sequences of published plant genomes are suitable to identify some of the proteins causing food allergies and sensitivities. Brachypodium distachyon, a diploid wild grass with a sequenced genome and low prolamin content, is the closest relative of the allergen cereals, such as wheat or barley. Using the Brachypodium genome sequence, a workflow has been developed to identify potentially harmful proteins which may cause either celiac disease or wheat allergy-related symptoms. Seed tissue-specific expression of the potential allergens has been determined, and intact epitopes following an in silico digestion with several endopeptidases have been identified. Molecular function of allergen proteins has been evaluated using Gene Ontology terms. Biologically overrepresented proteins and potentially allergen protein families have been identified.

Molecular cloning, expression, sequence analyses of dust mite allergen Der f 6 and its IgE-binding reactivity with mite allergic asthma patients in southeast China

We report the cloning and molecular characterization of a full-length cDNA encoding house dust mite allergen, Der f 6 from D. farinae isolated in China. The full-length Der f 6 cDNA was obtained with 840 nucleotides long. Nucleotide sequencing analyses showed a total of 36 mutations in five Der f 6 cDNA clones, corresponding to 23 incompatible amino acid residues. Recombinant Der f 6 (rDer f 6) protein was successfully expressed in and purified from E. coli BL21. Among 20 asthmatic patients, 45% was positive to rDer f 6 by ELISA. Bioinformatics analyses revealed that the mature Der f 6 was a hydrophobic and extracellular protein with chymotrypsin-like serine protease activity, its secondary structure was composed of alpha helix (7.69%), extended strand (34.62%), random coils (57.69%), and the similarity of Der f 6 to Blo t 6, Sui m 6, Der f 3 and Der f 9 was 64, 65, 35, and 38%, respectively.

Cloning, expression, and characterization of Der f 7, an allergen of Dermatophagoides farinae from China

A full-length cDNA encoding house dust mite allergen Der f 7 from Dermatophagoides farina (Acari: Pyroglyphidae) from China was cloned, sequenced, and successfully expressed. A reference sequence (GenBank accession AY283292) was used to design polymerase chain reaction primers. Analysis revealed eight mismatched nucleotides in five Der f 7 cDNA clones, and the projected amino acid sequence contained six incompatible residues. These results suggest that the sequence of Der f 7 may be polymorphic. Further bioinformatic analysis revealed that the mature Der f 7 allergen had a molecular mass of approximately 21.88 kDa and a theoretical isoelectric point of 4.90. Der f 7 protein secondary structure was composed of a helix (56.63%), extended strand (5.10%), and random coil (38.27%). Group 7 allergens are present in Pyroglyphidae, Acaridae, and Glycyphagidae families, and homology analysis revealed a 86% similarity between Der f 7 and Der p 7. Furthermore, a phylogenetic tree constructed of group 7 allergens from different mite species revealed that Der f 7 and Der p 7 clustered with 100% bootstrap support. Bioinformatics-driven characterization of Der f 7 allergen as conducted in this study may contribute to diagnostic and therapeutic applications for dust mite allergies.

AllerHunter: a SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins

Allergy is a major health problem in industrialized countries. The number of transgenic food crops is growing rapidly creating the need for allergenicity assessment before they are introduced into human food chain. While existing bioinformatic methods have achieved good accuracies for highly conserved sequences, the discrimination of allergens and non-allergens from allergen-like non-allergen sequences remains difficult. We describe AllerHunter, a web-based computational system for the assessment of potential allergenicity and allergic cross-reactivity in proteins. It combines an iterative pairwise sequence similarity encoding scheme with SVM as the discriminating engine. The pairwise vectorization framework allows the system to model essential features in allergens that are involved in cross-reactivity, but not limited to distinct sets of physicochemical properties. The system was rigorously trained and tested using 1,356 known allergen and 13,449 putative non-allergen sequences. Extensive testing was performed for validation of the prediction models. The system is effective for distinguishing allergens and non-allergens from allergen-like non-allergen sequences. Testing results showed that AllerHunter, with a sensitivity of 83.4% and specificity of 96.4% (accuracy = 95.3%, area under the receiver operating characteristic curve AROC = 0.928+/-0.004 and Matthew's correlation coefficient MCC = 0.738), performs significantly better than a number of existing methods using an independent dataset of 1443 protein sequences. AllerHunter is available at (http://tiger.dbs.nus.edu.sg/AllerHunter).

Introduction to bioinformatics

This article introduces the field of bioinformatics and describes bioinformatic approaches and their application to the study of protein allergens. The predominant bioinformatics tools and resources are listed and discussed.

AlgPred: prediction of allergenic proteins and mapping of IgE epitopes

In this study a systematic attempt has been made to integrate various approaches in order to predict allergenic proteins with high accuracy. The dataset used for testing and training consists of 578 allergens and 700 non-allergens obtained from A. K. Bjorklund, D. Soeria-Atmadja, A. Zorzet, U. Hammerling and M. G. Gustafsson (2005) Bioinformatics, 21, 39-50. First, we developed methods based on support vector machine using amino acid and dipeptide composition and achieved an accuracy of 85.02 and 84.00%, respectively. Second, a motif-based method has been developed using MEME/MAST software that achieved sensitivity of 93.94 with 33.34% specificity. Third, a database of known IgE epitopes was searched and this predicted allergenic proteins with 17.47% sensitivity at specificity of 98.14%. Fourth, we predicted allergenic proteins by performing BLAST search against allergen representative peptides. Finally hybrid approaches have been developed, which combine two or more than two approaches. The performance of all these algorithms has been evaluated on an independent dataset of 323 allergens and on 101 725 non-allergens obtained from Swiss-Prot. A web server AlgPred has been developed for the predicting allergenic proteins and for mapping IgE epitopes on allergenic proteins (http://www.imtech.res.in/raghava/algpred/). AlgPred is available at www.imtech.res.in/raghava/algpred/.

Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties

BACKGROUND

Computational methods have been developed for predicting allergen proteins from sequence segments that show identity, homology, or motif match to a known allergen. These methods achieve good prediction accuracies, but are less effective for novel proteins with no similarity to any known allergen.

METHODS

This work tests the feasibility of using a statistical learning method, support vector machines, as such a method. The prediction system is trained and tested by using 1005 allergen proteins from the Allergome database and 22,469 non-allergen proteins from 7871 Pfam families.

RESULTS

Testing results by an independent set of 229 allergen and 6717 non-allergen proteins from 7871 Pfam families show that 93.0% and 99.9% of these are correctly predicted, which are comparable to the best results of other methods. Of the 18 novel allergen proteins non-homologous to any other proteins in the Swissprot database, 88.9% is correctly predicted. A further screening of 168,128 proteins in the Swissprot database finds that 2.9% of the proteins are predicted as allergen proteins, which is consistent with the estimated numbers from motif-based methods.

CONCLUSIONS

Our study suggests that SVM is a potentially useful method for predicting allergen proteins and it has certain capability for predicting novel allergen proteins. Our software can be accessed at .

The potential of proteomics and peptidomics for allergy and asthma research

Progress in the field of proteomics, the branch of biology that studies the full set of proteins derived from a given genome, is moving fast. Two-dimensional gel electrophoresis (2DG) separation of complex protein mixtures and the subsequent analysis of isolated protein spots by mass spectrometry allow fast and accurate identification of proteins. The comparison of spots from different samples separated on customized 2D gels allows the detection of punctual differences in their mobility and facilitates tracing back differences in protein expression, presence of isoforms, splice variants and posttranslational modifications by mass spectrometry. In spite of significant analytical challenges owing to the high complexity of the proteome and the challenge deriving from the necessity to process huge amounts of raw data generated by mass spectrometric profiling, proteomics has evolved to an indispensable tool in life sciences. A restricted window of the proteome that consists of peptides and small proteins not easily manageable by conventional gel electrophoresis prompted the development of separation methods based on liquid chromatography. This new research field termed peptidomics already contributed, together with proteomics to enlarge our knowledge about biological processes and supported by sophisticated bioinformatics tools, to the discovery of new diagnostic and therapeutic targets. The technological capabilities of biophysical separation, mass spectrometry and bioinformatics form the basis of discovery programs that aim at mining the proteome starting from microgram amounts of protein extracts derived from body fluids and tissues. Proteomics and peptidomics have a great potential to speed up allergy and asthma research, where disease- and tissue-specific samples are easy to obtain.

Engineered allergens for immunotherapy

PURPOSE OF REVIEW

Specific immunotherapy is a clinically effective causative treatment for allergic conditions. However, the reagents used for immunotherapy are crude extracts prepared from natural sources with potential life-threatening anaphylactic side effects. Molecular cloning of allergens has made it feasible to design novel therapeutic approaches for improved and safer forms of allergen-specific immunotherapy. The purpose of this review is to examine recent advances made in the last 2 years in genetic engineering of allergens for specific immunotherapy.

RECENT FINDINGS

Genetic engineering of allergen with nil or low IgE reactivity but retained T-cell reactivity offers a novel therapeutic approach to improving safety and efficacy of allergen-specific immunotherapy. Hypoallergenic forms of major allergens have been produced, with reduced IgE epitopes while preserving other characteristics of the molecule to induce a protective response.

SUMMARY

Hypoallergenic forms of major allergens are potential candidates for allergen-specific immunotherapy in the future. These genetically engineered hypoallergens now need to be tested in clinical trials before being widely used. Safer and more efficacious vaccines would increase patient compliance leading to extensive use of immunotherapy.

Bridging PCR and partially overlapping primers for novel allergen gene cloning and expression insert decoration

AIM: To obtain the entire gene open reading frame (ORF) and to construct the expression vectors for recombinant allergen production.

METHODS: Gene fragments corresponding to the gene specific region and the cDNA ends of pollen allergens of short ragweed (Rg, Ambrosia artemisiifolia L.) were obtained by pan-degenerate primer-based PCR and rapid amplification of the cDNA ends (RACE), and the products were mixed to serve as the bridging PCR (BPCR) template. The full-length gene was then obtained. Partially overlapping primer-based PCR (POP-PCR) method was developed to overcome the other problem, i.e., the non-specific amplification of the ORF with routine long primers for expression insert decoration. Northern blot was conducted to confirm pollen sources of the gene. The full-length coding region was evaluated for its gene function by homologue search in GenBank database and Western blotting of the recombinant protein Amb a 8 (D106) expressed in Escherichia coli pET-44 system.

RESULTS: The full-length cDNA sequence of Amb a 8(D106) was obtained by using the above procedure and deduced to encode a 131 amino acid polypeptide. Multiple sequence alignment exhibited the gene D106 sharing a homology as high as 54%-89% and 79%-89% to profilin from pollen and food sources, respectively. The expression vector of the allergen gene D106 was successfully constructed by employing the combined method of BPCR and POP-PCR. Recombinant allergen rAmb a 8(D106) was then successfully generated. The allergenicity was hallmarked by immunoblotting with the allergic serum samples and its RNA source was confirmed by Northern blot.

CONCLUSION: The combined procedure of POP-PCR and BPCR is a powerful method for full-length allergen gene retrieval and expression insert decoration, which would be useful for recombinant allergen production and subsequent diagnosis and immunotherapy of pollen and food allergy.

Allergen databases

Allergies represent a significant medical and industrial problem. Molecular and clinical data on allergens are growing exponentially and in this article we have reviewed nine specialized allergen databases and identified data sources related to protein allergens contained in general purpose molecular databases. An analysis of allergens contained in public databases indicates a high level of redundancy of entries and a relatively low coverage of allergens by individual databases. From this analysis we identify current database needs for allergy research and, in particular, highlight the need for a centralized reference allergen database.