The property distance index PD predicts peptides that cross-react with IgE antibodies

Beyond mutations: Accounting for quantitative changes in the analysis of protein evolution

Molecular phylogenetic research has relied on the analysis of the coding sequences by genes or of the amino acid sequences by the encoded proteins. Enumerating the numbers of mismatches, being indicators of mutation, has been central to pertinent algorithms. Specific amino acids possess quantifiable characteristics that enable the conversion from "words" (strings of letters denoting amino acids or bases) to "waves" (strings of quantitative values representing the physico-chemical properties) or to matrices (coordinates representing the positions in a comprehensive property space). The application of such numerical representations to evolutionary analysis takes into account not only the occurrence of mutations but also their properties as influences that drive speciation, because selective pressures favor certain mutations over others, and this predilection is represented in the characteristics of the incorporated amino acids (it is not born out solely by the mismatches). Besides being more discriminating sources for tree-generating algorithms than match/mismatch, the number strings can be examined for overall similarity with average mutual information, autocorrelation, and fractal dimension. Bivariate wavelet analysis aids in distinguishing hypermutable versus conserved domains of the protein. The matrix depiction is readily subjected to comparisons of distances, and it allows the generation of heat maps or graphs. This analysis preserves the accepted taxa order where tree construction with standard approaches yields conflicting results (for the protein S100A6). It also aids hypothesis generation about the origin of mitochondrial proteins. These analytical algorithms have been automated in R and are applicable to various processes that are describable in matrix format.

Development of an indirect competitive ELISA based on the common epitope of fish parvalbumin for its detection

A common epitope (AGSFDHKKFFKACGLSGKST) of parvalbumin from 16 fish species was excavated using bioinformatics tools combined with the characterization of fish parvalbumin binding profile of anti-single epitope antibody in this study. A competitive enzyme-linked immunosorbent assay (ELISA) based on the common epitope was established with a limit of detection of 10.15 ng/mL and a limit of quantification of 49.29 ng/mL. The developed ELISA exhibited a narrow range (71% to 107%) of related cross-reactivity of 15 fish parvalbumin. Besides, the recovery, the coefficient of variations for the intra-assay and the inter-assay were 84.3% to 108.2%, 7.4% to 13.9% and 8.5% to 15.6%. Our findings provide a novel idea for the development of a broad detection method for fish allergens and a practical tool for the detection of parvalbumin of economic fish species in food samples.

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.

The updated Structural Database of Allergenic Proteins (SDAP 2.0) provides 3D models for allergens and incorporated bioinformatics tools

Background

Allergenic proteins can cause IgE-mediated adverse reactions in sensitized individuals. Although the sequences of many allergenic proteins have been identified, bioinformatics data analysis with advanced computational methods and modeling is needed to identify the basis for IgE binding and cross-reactivity.

Objective

We aim to present the features and use of the updated Structural Database of Allergenic Proteins 2.0 (SDAP 2.0) webserver, a unique, publicly available resource to compare allergens using specially designed computational tools and new high-quality 3-D models for most known allergens.

Methods

Previously developed and novel software tools for identifying cross-reactive allergens using sequence and structure similarity are implemented in SDAP 2.0. A comprehensive set of high-quality 3-D models of most allergens was generated with the state-of-the-art AlphaFold 2 software. A graphics tool enables the interactive visualization of IgE epitopes on experimentally determined and modeled 3-D structures.

Results

A user can search for allergens similar to a given input sequence with the FASTA algorithm or the window-based World Health Organization/International Union of Immunological Societies (WHO/IUIS) guidelines on safety concerns of novel food products. Peptides similar to known IgE epitopes can be identified with the property distance tool and conformational epitopes by the Cross-React method. The updated database contains 1657 manually curated sequences including all allergens from the IUIS database, 334 experimentally determined X-ray or NMR structures, and 1565 3-D models. Each allergen/isoallergen is classified according to its protein family.

Conclusions

SDAP provides access to the steadily increasing information on allergenic structures and epitopes with integrated bioinformatics tools to identify and analyze their similarities. In addition to serving the research and regulatory community, it provides clinicians with tools to identify potential coallergies in a sensitive patient and can help companies to design hypoallergenic foods and immunotherapies.

Peptide immunotherapy for aeroallergens

Background: Allergen specific immunotherapy (SIT) has been used for more than a century. Researchers have been working to improve efficacy and reduce the side effects. Objective: We have reviewed the literature about peptides immunotherapy for inhaled allergens. The mechanism of SIT is to induce regulatory T (Treg) cells and to reduce T helper (Th)2 cells to induce class switching from IgE to IgG and induce blocking antibodies to inhibit allergen binding of IgE. Methods: The relevant published literatures on the peptide SIT for aeroallergens have been searched on the medline. Results: Modification of allergens and routes of treatment has been performed. Among them, many researchers were interested in peptide immunotherapy. T-cell epitope peptide has no IgE epitope, that is able to bind IgE, but rather induces Treg and reduces Th2 cells, which was considered an ideal therapy. Results from cellular and animal model studies have been successful. However, in clinical studies, T-cell peptide immunotherapy has failed to show efficacy and caused side effects, because of the high effective rate of placebo and the development of IgE against T-cell epitope peptides. Currently, the modifications of IgE-allergen binding by blocking antibodies are considered for successful allergen immunotherapy. Conclusion: Newly developed hypoallergenic B cell epitope peptides and computational identification methods hold great potential to develop new peptide immunotherapies.

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

2S albumin g13 polypeptide, less related to Fag e 2, can be eliminated in common buckwheat (Fagopyrum esculentum Moench) seeds

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2S albumin (g11, g13, g14, and g28) is an important allergen in common buckwheat.

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g13 is hydrophobic, scarce, and less related to g14 than g11/g28 is related to g14.

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g13_null allele homozygote produced no g13 protein in seeds.

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Insert-like sequence of g13_null allele resided frequently in buckwheat genome.

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g13_null homozygote lowered allergenicity in common buckwheat.

2S albumin (g11, g13, g14, and g28) is an important allergen in common buckwheat (Fagopyrum esculentum). g13 is hydrophobic, rare in seeds, and may show distinct allergenicity from the others; therefore, we tried to eliminate this protein. Phylogenetic and property distance analyses indicated g13 is less related to g14 (Fag e 2) than g11/g28 is related to g14, particularly in the second domain containing the II and III α-helices. A null allele with a 531 bp insertion in the coding region was found for g13 at an allele frequency of 2 % in natural populations of common buckwheat. The g13_null allele homozygote accumulated no g13 protein. A BLAST search for the 531 bp insertion suggested the insert-like sequence resided frequently in the buckwheat genome, including the self-incompatibility responsible gene ELF3 in Fagopyrum tataricum. The g13_null insert-like sequence could, therefore, help in producing hypoallergenic cultivars, and expand the genetic diversity of buckwheat.

A Computational Algorithm to Assess the Physiochemical Determinants of T Cell Receptor Dissociation Kinetics

The rational design of T Cell Receptors (TCRs) for immunotherapy has stagnated due to a limited understanding of the dynamic physiochemical features of the TCR that elicit an immunogenic response. The physiochemical features of the TCR-peptide major histocompatibility complex (pMHC) bond dictate bond lifetime which, in turn, correlates with immunogenicity. Here, we: i) characterize the force-dependent dissociation kinetics of the bond between a TCR and a set of pMHC ligands using Steered Molecular Dynamics (SMD); and ii) implement a machine learning algorithm to identify which physiochemical features of the TCR govern dissociation kinetics. Our results demonstrate that the total number of hydrogen bonds between the CDR2β-MHC⍺(β), CDR1α-Peptide, and CDR3β-Peptide are critical features that determine bond lifetime.

A new paradigm to search for allergenic proteins in novel foods by integrating proteomics analysis and in silico sequence homology prediction: Focus on spirulina and chlorella microalgae

Since novel nutrient sources with high protein content, such as yeast, fungi, bacteria, algae, and insects, are increasingly introduced in the consumer market, safety evaluation studies on their potentially allergenic proteins are required. A pipeline for in silico establishing the sequence-based homology between proteins of spirulina (Arthrospira platensis) and chlorella (Chlorella vulgaris) micro-algae and those included in the AllergenOnline (AO) database (AllergenOnline.org) is described. The extracted proteins were first identified through tryptic peptides analysis by reversed-phase liquid chromatography and high resolution/accuracy Fourier-transform tandem mass spectrometry (RPLC-ESI-FTMS/MS), followed by a quest on the UniProt database. The AO database was subsequently interrogated to assess sequence similarity between identified microalgal proteins and known allergens, based on criteria established by the World Health Organization (WHO) and Food and Agriculture Organization (FAO). A direct search for microalgal proteins already included in allergen databases was also performed using the Allergome database. Six proteins exhibiting a significant homology with food allergens were identified in spirulina extracts. Five of them, i.e., two thioredoxins (D4ZSU6, K1VP15), a superoxide dismutase (C3V3P3), a glyceraldehyde-3-phosphate dehydrogenase (K1W168), and a triosephosphate isomerase (D5A635), resulted from the search on AO. The sixth protein, C-phycocyanin beta subunit (P72508), was directly obtained after examining the Allergome database. Two proteins exhibiting significant sequence homology with food allergens were retrieved in chlorella extracts, viz. calmodulin (A0A2P6TFR8), which is related to troponin c (D7F1Q2), and fructose-bisphosphate aldolase (A0A2P6TDD0). Specific serum screenings based on immunochemical tests should be undertaken to confirm or rule out the allergenicity of the identified proteins.

Structure, Immunogenicity, and IgE Cross-Reactivity among Walnut and Peanut Vicilin-Buried Peptides

Vicilin-buried peptides (VBPs) from edible plants are derived from the N-terminal leader sequences (LSs) of seed storage proteins. VBPs are defined by a common α-hairpin fold mediated by conserved CxxxCx(10-14)CxxxC motifs. Here, peanut and walnut VBPs were characterized as potential mediators of both peanut/walnut allergenicity and cross-reactivity despite their low (∼17%) sequence identity. The structures of one peanut (AH1.1) and 3 walnut (JR2.1, JR2.2, JR2.3) VBPs were solved using solution NMR, revealing similar α-hairpin structures stabilized by disulfide bonds with high levels of surface similarity. Peptide microarrays identified several peptide sequences primarily on AH1.1 and JR2.1, which were recognized by peanut-, walnut-, and dual-allergic patient IgE, establishing these peanut and walnut VBPs as potential mediators of allergenicity and cross-reactivity. JR2.2 and JR2.3 displayed extreme resilience against endosomal digestion, potentially hindering epitope generation and likely contributing to their reduced allergic potential.

Cross-reactivity between tick and wasp venom can contribute to frequent wasp sensitization in patients with the α-Gal syndrome

BACKGROUND

α-Gal syndrome (AGS) is a food allergy with severe delayed allergic reactions, mediated by IgE-reactivity to galactose-α1,3-galactose (α-Gal). AGS is strongly associated with tick bites. An increased incidence of venom sensitization has been found in AGS patients. Here, we evaluated the frequency of wasp sensitization in Swedish AGS patients and the possible cross-reactivity between wasp venom and tick proteins.

METHODS

Sera from 136 Swedish AGS patients and 29 wasp-positive non-AGS control sera were analyzed for IgE-reactivity against wasp venom (Vespula spp.), the European tick Ixodes ricinus (Streptavidin ImmunoCAP), α-Gal and total IgE by ImmunoCAP. The presence of α-Gal on wasp venom proteins (Vespula vulgaris) was investigated by western blot (WB), and possible cross-reactivity between wasp venom and tick proteins by enzyme-linked immunosorbent assay and WB. Involvement of cross-reactive carbohydrate domains (CCDs) was also assessed.

RESULTS

Wasp sensitization was present in 54% of AGS patients, although the IgE levels were low. Wasp sensitized patients had higher IgE levels to α-Gal and total IgE levels compared to non-wasp sensitized AGS patients. α-Gal was not detected in wasp venom, but cross-reactivity between wasp and tick proteins was demonstrated which was not dependent on CCDs. The same cross-reactivity was also observed in the control sera. Furthermore, 17 putative cross-reactive peptides were identified using an in silico approach.

CONCLUSIONS

For the first time, cross-reactivity between wasp venom and tick proteins has been described. This may be a reason why the majority of Swedish AGS patients, who have all been tick bitten, are also sensitized against wasp.

Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy

About half of the US population is sensitized to one or more allergens, as found by a National Health and Nutrition Examination Survey (NHANES). The most common treatment for seasonal allergic responses is the daily use of oral antihistamines, which can control some of the symptoms, but are not effective for nasal congestion, and can be debilitating in many patients. Peptide immunotherapy is a promising new approach to treat allergic airway diseases. The small size of the immunogens cannot lead to an unwanted allergic reaction in sensitized patients, and the production of peptides with sufficient amounts for immunotherapy is time- and cost-effective. However, it is not known what peptides are the most effective for an immunotherapy of allergens. We previously produced a unique monoclonal antibody (mAb) E58, which can inhibit the binding of multiple groups of mAbs and human IgEs from patients affected by the major group 1 allergens of ragweed (Amb a 1) and conifer pollens (Jun a 1, Cup s 1, and Cry j 1). Here, we demonstrated that a combined approach, starting from two linear E58 epitopes of the tree pollen allergen Jun a 1 and the ragweed pollen allergen Amb a 1, and residue modifications suggested by molecular docking calculations and peptide design could identify a large number of high affinity binding peptides. We propose that this combined experimental and computational approach by structural analysis of linear IgE epitopes and peptide design, can lead to potential new candidates for peptide immunotherapy.

Identification of B cell epitopes of Per a 5 allergen using bioinformatic approach

BACKGROUND

Immune epitopes of allergens are pivotal for development of novel diagnostic and therapeutic modalities. Present study aims to identify antigenic determinants of Per a 5, a clinically relevant cross reactive cockroach allergen.

METHODS

The three dimensional structure of Per a 5 was modelled using Modeller 9v11 software. A combination of sequence and structure based computational tools were employed for predicting B cell epitopes. Epitopes were synthesized and immunoreactivity was assessed by ELISA using cockroach hypersensitive patient's sera. Cross-reactivity potential of predicted epitopes was assessed with SDAP and ConSurf and validated by IgE ELISA with fungal and mite hypersensitive patient's sera.

RESULTS

Per a 5 structure exhibited good quality factor in ERRAT and high stereochemical stability. In silico analysis revealed six B cell epitopes (BC-P1 to P6). BC-P3 demonstrated significant IgE binding followed by BC-P2 and BC-P1 with cockroach hypersensitive patient's sera. Per a 5 epitopes demonstrate considerable similarity with broad spectrum of allergens from fungal, mites, helminths, fruits and nuts. Analysis of PD values indicate BC-P4 to be well conserved among dust mite and helminth GSTs (8.89, 10.63 and 10.69 with D. pteronyssinus, W. bancrofti and F. hepatica respectively). ConSurf analysis of Per a 5 revealed specific enrichment of evolutionarily similar amino acid residues in BC-P2 (with fungal and mite GSTs) and BC-P4 (with mite and helminth GSTs). Further, IgE binding analysis of epitopes demonstrate BC-P2, BC-P3 and BC-P5 as high IgE binders in fungal hypersensitive sera while BC-P1, BC-P2, BC-P4 and BC-P5 demonstrated significant IgE binding with mite hypersensitive sera.

CONCLUSIONS

Among the predicted epitopes, BC-P3 demonstrates maximal IgE binding ability. Computational analysis suggests strong evolutionary conservation and cross reactive potential of BC-P4 with allergens in dust mite and helminths. ELISA highlights predictive potential of analysing evolutionarily conserved residues for uncovering potentially cross reactive antigenic determinants.

GENERAL SIGNIFICANCE

Immune epitopes of Per a 5 were identified for aiding molecular diagnosis and potential cross reactivity.

DGraph Clusters Flaviviruses and β-Coronaviruses According to Their Hosts, Disease Type, and Human Cell Receptors

Motivation

There is a need for rapid and easy-to-use, alignment-free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is an application developed to generate 2-dimensional (2D) maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such "PD-graphs" show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities.

Results

Property distance graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors, and disease phenotypes. Property distance graphs separate the tick- from the mosquito-borne FV, cluster viruses that infect bats, camels, seabirds, and humans separately. The clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and Middle East respiratory syndrome (MERS) sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.

Insights into Peptide Mediated Antibiofilm Treatment in Chronic Wound: A Bench to Bedside Approach

Chronic wound biofilm infections are a threat to the population with respect to morbidity and mortality. The presence of multidrug-resistant bacterial pathogens in chronic wound renders the action of antibiotics and antibiofilm agents difficult. Therefore an alternative therapy is essential for reducing bacterial biofilm burden. In this scenario, the peptide-based antibiofilm therapy for chronic wound biofilm management seeks more attention. A synthetic peptide with a broad range of antibiofilm activity against preformed and established biofilms, having the ability to kill multispecies bacteria within biofilms and possessing combinatorial activity with other antimicrobial agents, provides significant insights. In this review, we portray the possibilities and difficulties of peptide-mediated treatment in chronic wounds biofilm management and how it can be clinically translated into a product.

The importance of the 2S albumins for allergenicity and cross-reactivity of peanuts, tree nuts, and sesame seeds

Allergies to peanuts, tree nuts, and sesame seeds are among the most important food-related causes of anaphylaxis. Important clinical questions include: Why is there a variable occurrence of coallergy among these foods and Is this immunologically mediated? The clinical and immunologic data summarized here suggest an immunologic basis for these coallergies that is based on similarities among the 2S albumins. Data from component resolved diagnostics have highlighted the relationship between IgE binding to these allergens and the presence of IgE-mediated food allergy. Furthermore, in vitro and in vivo experiments provide strong evidence that the 2S albumins are the most important allergens in peanuts for inducing an allergic effector response. Although the 2S albumins are diverse, they have a common disulfide-linked core with similar physicochemical properties that make them prime candidates to explain much of the observed coallergy among peanuts, tree nuts, and sesame seeds. The well-established frequency of cashew and pistachio nut coallergy (64%-100%) highlights how the structural similarities among their 2S albumins may account for observed clinical cross-reactivity. A complete understanding of the physicochemical properties of the 2S albumins in peanuts, tree nuts, and sesame seeds will enhance our ability to diagnose, treat, and ultimately prevent these allergies.

Evaluation of cross-reactivity between casein components using inhibition assay and in silico analysis

BACKGROUND

We previously reported that the specific IgE levels to αs1-casein (CN) and β-CN in patients with cow's milk allergy decreased with similar dynamics during oral immunotherapy. Therefore, we hypothesized that αs1- and β-CN have strong cross-reactivity among CN components, despite the low similarity in the full-length amino acid sequences.

METHODS

The αs1-, β-, and κ-CN were purified from commercial cow's milk. We recruited 39 patients with cow's milk allergy, and the serum IgE levels for each CN component were measured by enzyme-linked immunosorbent assay (ELISA). Cross-reactivity between CN components was investigated by competitive ELISA against αs1-CN. Sequence homology between CN components at the peptide level was calculated using in silico analysis and quantified by the property distance (PD) value.

RESULTS

The αs1-CN-specific IgE levels exhibited a strong positive correlation with the β-CN-specific IgE (r = 0.945, P < .001). Complete competition was observed by β-CN against αs1-CN, suggesting the presence of common epitopes between them. In silico analysis detected 24 peptide sets with PD values lower than 10 between αs1- and β-CN, and 14 sets between αs1- and κ-CN. The amino acid sequences of αs1-CN (E61-E70) and β-CN (I12-E21) that showed the lowest PD value (5.30) were present in the characteristic sequence known as casein phosphopeptide (CPP).

CONCLUSION

We detected strong cross-reactivity between CN components. Furthermore, we found highly homologous sequences in the CPP region, which contains a core sequence of "SSSEE" with phosphorylated serine residues.

D-graph clusters flaviviruses and β-coronaviruses according to their hosts, disease type and human cell receptors

Motivation:

There is a need for rapid and easy to use, alignment free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is a dynamic programming application developed to generate 2D-maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such “PD-graphs” show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities.

Results:

PD-Graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors and disease phenotypes. PD-graphs separate the tick- from the mosquito-borne FV, clusters viruses that infect bats, camels, seabirds and humans separately and the clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of SARS, SARS-CoV-2, and MERS sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.

Epitopes with similar physicochemical properties contribute to cross reactivity between peanut and tree nuts

Many individuals with peanut (PN) allergy have severe reactions to tree nuts (TN) such as walnuts or cashews. Although allergenic proteins in TN and PN have overall low identity, they share discrete sequences similar in physicochemical properties (PCP) to known IgE epitopes. Here, PCP-consensus peptides (cp, 13 aa and 31 aa) were identified from an alignment of epitope rich regions of walnut vicilin, Jug r 2, leader sequence (J2LS) and cross-reactive epitopes in the 2S albumins of peanut and synthesized. A peptide similarity search in the Structural Database of Allergenic Proteins (SDAP) revealed a network of peptides similar (low property distance, PD) to the 13 aa cp (13cp) in many different plant allergens. Peptides similar to the 13cp in PN and TN allergens bound IgE from sera of patients allergic to PN and TN in peptide microarray analysis. The 13cp was used to produce a rabbit consensus peptide antibody (cpAB) that detected proteins containing repeats similar to the 13cp in western blots of various nut extracts, in which reactive proteins were identified by mass spectrometry. The cpAB bound more specifically to allergens and nut extracts containing multiple repeats similar to the 13 cp, such as almond (Pru du 6), peanut (Ara h 2) and walnut (Jug r 2). IgE binding to various nut extracts is inhibited by recombinant J2LS sequence and synthetic 31cp. Thus, several repeated sequences similar to the 13cp are bound by IgE. Multiple similar repeats in several allergens could account for reaction severity and clinically relevant cross-reactivity to PN and TN. These findings may help improve detection, diagnostic, and therapeutic tools.

Antibacterial Peptides in Dermatology-Strategies for Evaluation of Allergic Potential

During recent decades, the market for peptide-based drugs, including antimicrobial peptides, has vastly extended and evolved. These drugs can be useful in treatment of various types of disorders, e.g., cancer, autoimmune diseases, infections, and non-healing wounds. Although peptides are less immunogenic than other biologic therapeutics, they can still induce immune responses and cause allergies. It is important to evaluate the immunogenic and allergic potential of peptides before they are forwarded to the expensive stages of clinical trials. The process of the evaluation of immunogenicity and cytotoxicity is complicated, as in vitro models and bioinformatics tools cannot fully simulate situations in the clinic. Nevertheless, several potentially promising tests for the preclinical evaluation of peptide drugs have been implemented (e.g., cytotoxicity assays, the basophil activation test, and lymphocyte activation assays). In this review, we focus on strategies for evaluation of the allergic potential of peptide-based therapeutics.

Cross-React: a new structural bioinformatics method for predicting allergen cross-reactivity

The phenomenon of cross-reactivity between allergenic proteins plays an important role to understand how the immune system recognizes different antigen proteins. Allergen proteins are known to cross-react if their sequence comparison shows a high sequence identity which also implies that the proteins have a similar 3D fold. In such cases, linear sequence alignment methods are frequently used to predict cross-reactivity between allergenic proteins. However, the prediction of cross-reactivity between distantly related allergens continues to be a challenging task. To overcome this problem, we developed a new structure-based computational method, Cross-React, to predict cross-reactivity between allergenic proteins available in the Structural Database of Allergens (SDAP). Our method is based on the hypothesis that we can find surface patches on 3D structures of potential allergens with amino acid compositions similar to an epitope in a known allergen. We applied the Cross-React method to a diverse set of seven allergens, and successfully identified several cross-reactive allergens with high to moderate sequence identity which have also been experimentally shown to cross-react. Based on these findings, we suggest that Cross-React can be used as a predictive tool to assess protein allergenicity and cross-reactivity.

Availability and Implementation

: Cross-React is available at: http://curie.utmb.edu/Cross-React.html.

Contact

ssnegi@utmb.edu.

Navigating through the Jungle of Allergens: Features and Applications of Allergen Databases

The increasing number of available data on allergenic proteins demanded the establishment of structured, freely accessible allergen databases. In this review article, features and applications of 6 of the most widely used allergen databases are discussed. The WHO/IUIS Allergen Nomenclature Database is the official resource of allergen designations. Allergome is the most comprehensive collection of data on allergens and allergen sources. AllergenOnline is aimed at providing a peer-reviewed database of allergen sequences for prediction of allergenicity of proteins, such as those planned to be inserted into genetically modified crops. The Structural Database of Allergenic Proteins (SDAP) provides a database of allergen sequences, structures, and epitopes linked to bioinformatics tools for sequence analysis and comparison. The Immune Epitope Database (IEDB) is the largest repository of T-cell, B-cell, and major histocompatibility complex protein epitopes including epitopes of allergens. AllFam classifies allergens into families of evolutionarily related proteins using definitions from the Pfam protein family database. These databases contain mostly overlapping data, but also show differences in terms of their targeted users, the criteria for including allergens, data shown for each allergen, and the availability of bioinformatics tools.

Serological, genomic and structural analyses of the major mite allergen Der p 23

BACKGROUND

Der p 23 was recently identified in a European population as a major allergen and potentially a chitin binding protein.

OBJECTIVE

This study sought to assess the importance of Der p 23 among other Dermatophagoides allergens in a North American population and to determine the structure for functional characterization.

METHODS

IgE binding to Der p 23, Der p 1, Der p 2, Der p 5, Der p 7 and Der p 8 was measured by ELISA. RNA-seq data from D. pteronyssinus were compared as estimates of allergen expression levels. The structure was analysed by X-ray crystallography and NMR.

RESULTS

Despite a high prevalence of Der p 23, (75% vs. 87% and 79% for Der p 1 and Der p 2, respectively), the anti-Der p 23 IgE levels were relatively low. The patient response to the 6 allergens tested was variable (n = 47), but on average anti-Der p 1 and anti-Der p 2 together accounted for 85% of the specific IgE. In terms of abundance, the RNA expression level of Der p 23 is the lowest of the major allergens, thirty fold less than Der p 1 and sevenfold less than Der p 2. The structure of Der p 23 is a small, globular protein stabilized by two disulphide bonds, which is structurally related to allergens such as Blo t 12 that contain carbohydrate binding domains that bind chitin. Functional assays failed to confirm chitin binding by Der p 23.

CONCLUSIONS AND CLINICAL RELEVANCE

Der p 23 accounts for a small percentage of the IgE response to mite allergens, which is dominated by Der p 1 and Der p 2. The prevalence and amount of specific IgE to Der p 23 and Der p 2 are disproportionately high compared to the expression of other Dermatophagoides allergens.

Cross-reactivity between major IgE core epitopes on Cry j 2 allergen of Japanese cedar pollen and relevant sequences on Cha o 2 allergen of Japanese cypress pollen

BACKGROUND

Cry j 2 and Cha o 2 are major allergens in Japanese cedar (Cryptomeria japonica; CJ) and Japanese cypress (Chamaecyparis obtusa; CO) pollen, respectively. Here, we assessed the epitopes related to the cross-reactivity between Cry j 2 and Cha o 2 using in vitro analyses.

METHODS

Peptides were synthesized based on Cry j 2 sequential epitopes and relevant Cha o 2 amino acid sequences. Four representative monoclonal antibodies (mAbs) against Cry j 2 were used according to their epitope recognitions. Serum samples were collected from 31 patients with CJ pollinosis. To investigate cross-reactivity between Cry j 2 and Cha o 2, ELISA and inhibition ELISA were performed with mAbs and sera from patients with CJ pollinosis.

RESULTS

Two of four mAbs had reactivity to both Cry j 2 and Cha o 2. Of these two mAbs, one mAb (T27) recognized the amino acid sequence (169)KVVNGRTV(176) on Cha o 2. This is related to the core epitope (169)KWVNGREI(176) on Cry j 2, which is an important IgE epitope. In addition, we found that these correlative sequences and purified allergens showed cross-reactivity between Cry j 2 and Cha o 2 in IgE of CJ patients.

CONCLUSIONS

We demonstrated the importance of (169)KVVNGRTV(176) in Cha o 2 for cross-reactivity with the Cry j 2 epitope (169)KWVNGREI(176), which plays an important role in allergenicity in CJ pollinosis. Our results are useful for the development of safer and more efficient therapeutic strategies for the treatment of CJ and CO pollen allergies.

A single mouse monoclonal antibody, E58 modulates multiple IgE epitopes on group 1 cedar pollen allergens

We recently described a dominant role for conformational epitopes on the group 1 allergen of the mountain cedar (Juniperus ashei, Cupressaceae), Jun a 1, in pollen hypersensitivity in South Central U.S.A. Since these epitopes are surface exposed and are likely to be flexible, they may be susceptible to molecular or physical perturbations. This may make Jun a 1 a potential target for new forms of therapy for cedar pollinosis. Here, we describe a mouse monoclonal antibody, termed E58, which binds to the group 1 allergens of the cedar pollens from three highly populated regions of the world (central U.S.A., France and Japan). Upon binding to these allergens, E58 strongly reduces the binding of patient's IgE antibodies to these dominant allergens. This characteristic of E58, and potentially other similar antibodies, suggests an opportunity to develop preventative or therapeutic agents that may inhibit cedar pollen sensitization or prevent their allergic reactions.

The Major Soybean Allergen Gly m Bd 28K Induces Hypersensitivity Reactions in Mice Sensitized to Cow's Milk Proteins

Reactions to soy have been reported in a proportion of patients with IgE-mediated cow's milk allergy (CMA). In this work, we analyzed if Gly m Bd 28K/P28, one of the major soybean allergens, is a cross-reactive allergen with cow milk proteins (CMP). We showed that P28 was recognized by IgE sera from CMA patients and activated human peripheral basophils degranulation. Moreover, IgE sera of mice exclusively sensitized to CMP recognized P28. Splenocytes from sensitized animals secreted IL-5 and IL-13 when incubated with CMP or soy proteins, but only IL-13 when treated with P28. In addition, a skin test was strongly positive for CMP and weakly positive for P28. Remarkably, milk-sensitized mice showed hypersensitivity symptoms following sublingual challenge with P28 or CMP. With the use of bioinformatics' tools seven putative cross-reactive epitopes were identified. In conclusion, using in vitro and in vivo tests we demonstrated that P28 is a novel cross-reactive allergen with CMP.

In vitro assessment of allergenicity features and localization of probable IgE binding regions

Rice is cultivated as a staple grain crop in many countries, especially in Asia. In the present study, recombinant rice chitinase was expressed, purified and characterized by in silico and immunobiochemical methods. Rice chitinase was affinity purified and it resolved at 24 kDa on SDS-PAGE. Purified protein was analyzed for pepsin resistance, heat stability, and IgE binding using atopic patients' sera. Chitinase was resistant to pepsin digestion and heat treatment at 90 °C for 1 h. It showed significant IgE binding with 7 of 110 patients' sera positive to different food allergens. Homology modeled 3D structure of rice chitinase was used for B cell epitope prediction. In silico predicted B cell peptides were assessed for IgE binding by ELISA using food allergic patients' sera, epitope RC2 showed IgE binding comparable to chitinase. In conclusion, chitinase was identified as a potential allergen and may share cross reactive epitopes with food allergens.

Allerdictor: fast allergen prediction using text classification techniques

MOTIVATION

Accurately identifying and eliminating allergens from biotechnology-derived products are important for human health. From a biomedical research perspective, it is also important to identify allergens in sequenced genomes. Many allergen prediction tools have been developed during the past years. Although these tools have achieved certain levels of specificity, when applied to large-scale allergen discovery (e.g. at a whole-genome scale), they still yield many false positives and thus low precision (even at low recall) due to the extreme skewness of the data (allergens are rare). Moreover, the most accurate tools are relatively slow because they use protein sequence alignment to build feature vectors for allergen classifiers. Additionally, only web server implementations of the current allergen prediction tools are publicly available and are without the capability of large batch submission. These weaknesses make large-scale allergen discovery ineffective and inefficient in the public domain.

RESULTS

We developed Allerdictor, a fast and accurate sequence-based allergen prediction tool that models protein sequences as text documents and uses support vector machine in text classification for allergen prediction. Test results on multiple highly skewed datasets demonstrated that Allerdictor predicted allergens with high precision over high recall at fast speed. For example, Allerdictor only took ∼6 min on a single core PC to scan a whole Swiss-Prot database of ∼540 000 sequences and identified <1% of them as allergens.

AVAILABILITY AND IMPLEMENTATION

Allerdictor is implemented in Python and available as standalone and web server versions at http://allerdictor.vbi.vt.edu CONTACT: lawrence@vbi.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

BACKGROUND

Assessment of potential allergenicity of protein is necessary whenever transgenic proteins are introduced into the food chain. Bioinformatics approaches in allergen prediction have evolved appreciably in recent years to increase sophistication and performance. However, what are the critical features for protein's allergenicity have been not fully investigated yet.

RESULTS

We presented a more comprehensive model in 128 features space for allergenic proteins prediction by integrating various properties of proteins, such as biochemical and physicochemical properties, sequential features and subcellular locations. The overall accuracy in the cross-validation reached 93.42% to 100% with our new method. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) procedure were applied to obtain which features are essential for allergenicity. Results of the performance comparisons showed the superior of our method to the existing methods used widely. More importantly, it was observed that the features of subcellular locations and amino acid composition played major roles in determining the allergenicity of proteins, particularly extracellular/cell surface and vacuole of the subcellular locations for wheat and soybean. To facilitate the allergen prediction, we implemented our computational method in a web application, which can be available at http://gmobl.sjtu.edu.cn/PREAL/index.php.

CONCLUSIONS

Our new approach could improve the accuracy of allergen prediction. And the findings may provide novel insights for the mechanism of allergies.

Evaluation and integration of existing methods for computational prediction of allergens

Background

Allergy involves a series of complex reactions and factors that contribute to the development of the disease and triggering of the symptoms, including rhinitis, asthma, atopic eczema, skin sensitivity, even acute and fatal anaphylactic shock. Prediction and evaluation of the potential allergenicity is of importance for safety evaluation of foods and other environment factors. Although several computational approaches for assessing the potential allergenicity of proteins have been developed, their performance and relative merits and shortcomings have not been compared systematically.

Results

To evaluate and improve the existing methods for allergen prediction, we collected an up-to-date definitive dataset consisting of 989 known allergens and massive putative non-allergens. The three most widely used allergen computational prediction approaches including sequence-, motif- and SVM-based (Support Vector Machine) methods were systematically compared using the defined parameters and we found that SVM-based method outperformed the other two methods with higher accuracy and specificity. The sequence-based method with the criteria defined by FAO/WHO (FAO: Food and Agriculture Organization of the United Nations; WHO: World Health Organization) has higher sensitivity of over 98%, but having a low specificity. The advantage of motif-based method is the ability to visualize the key motif within the allergen. Notably, the performances of the sequence-based method defined by FAO/WHO and motif eliciting strategy could be improved by the optimization of parameters. To facilitate the allergen prediction, we integrated these three methods in a web-based application proAP, which provides the global search of the known allergens and a powerful tool for allergen predication. Flexible parameter setting and batch prediction were also implemented. The proAP can be accessed at http://gmobl.sjtu.edu.cn/proAP/main.html.

Conclusions

This study comprehensively evaluated sequence-, motif- and SVM-based computational prediction approaches for allergens and optimized their parameters to obtain better performance. These findings may provide helpful guidance for the researchers in allergen-prediction. Furthermore, we integrated these methods into a web application proAP, greatly facilitating users to do customizable allergen search and prediction.

Computationally predicted IgE epitopes of walnut allergens contribute to cross-reactivity with peanuts

BACKGROUND

Cross-reactivity between peanuts and tree nuts implies that similar immunoglobulin E (IgE) epitopes are present in their proteins.

OBJECTIVE

To determine whether walnut sequences similar to known peanut IgE-binding sequences, according to the property distance (PD) scale implemented in the Structural Database of Allergenic Proteins, react with IgE from sera of patients with allergy to walnut and/or peanut.

METHODS

  Patient sera were characterized by western blotting for IgE binding to nut protein extracts and to peptides from walnut and peanut allergens, similar to known peanut epitopes as defined by low PD values, synthesized on membranes. Competitive enzyme-linked immunosorbent assay (ELISA) was used to show that peanut and predicted walnut epitope sequences compete with purified Ara h 2 for binding to IgE in serum from a cross-reactive patient.

RESULTS

Sequences from the vicilin walnut allergen Jug r 2, which had low PD values to epitopes of the peanut allergen Ara h 2, a 2S albumin, bound to IgE in sera from five patients who reacted to either walnut or peanut or both. A walnut epitope recognized by sera from six patients mapped to a surface-exposed region on a model of the N-terminal pro-region of Jug r 2. This predicted walnut epitope competed for IgE binding to Ara h 2 in serum as well as the known IgE epitope from Ara h 2.

CONCLUSIONS

Sequences with low PD value (< 8.5) to known IgE epitopes could contribute to cross-reactivity between allergens. This further validates the PD scoring method for predicting cross-reactive epitopes in allergens.

AllerML: markup language for allergens

Many concerns have been raised about the potential allergenicity of novel, recombinant proteins into food crops. Guidelines, proposed by WHO/FAO and EFSA, include the use of bioinformatics screening to assess the risk of potential allergenicity or cross-reactivities of all proteins introduced, for example, to improve nutritional value or promote crop resistance. However, there are no universally accepted standards that can be used to encode data on the biology of allergens to facilitate using data from multiple databases in this screening. Therefore, we developed AllerML a markup language for allergens to assist in the automated exchange of information between databases and in the integration of the bioinformatics tools that are used to investigate allergenicity and cross-reactivity. As proof of concept, AllerML was implemented using the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/) database. General implementation of AllerML will promote automatic flow of validated data that will aid in allergy research and regulatory analysis.

An Allergen Portrait Gallery: Representative Structures and an Overview of IgE Binding Surfaces

Recent progress in the biochemical classification and structural determination of allergens and allergen-antibody complexes has enhanced our understanding of the molecular determinants of allergenicity. Databases of allergens and their epitopes have facilitated the clustering of allergens according to their sequences and, more recently, their structures. Groups of similar sequences are identified for allergenic proteins from diverse sources, and all allergens are classified into a limited number of protein structural families. A gallery of experimental structures selected from the protein classes with the largest number of allergens demonstrate the structural diversity of the allergen universe. Further comparison of these structures and identification of areas that are different from innocuous proteins within the same protein family can be used to identify features specific to known allergens. Experimental and computational results related to the determination of IgE binding surfaces and methods to define allergen-specific motifs are highlighted.

Safety assessment of biotechnology products for potential risk of food allergy: implications of new research

Food allergy is a potential risk associated with use of transgenic proteins in crops. Currently, safety assessment involves consideration of the source of the introduced protein, in silico amino acid sequence homology comparisons to known allergens, physicochemical properties, protein abundance in the crop, and, when appropriate, specific immunoglobulin E binding studies. Recently conducted research presented at an International Life Sciences Institute/Health and Environmental Sciences Institute-hosted workshop adds to the scientific foundation for safety assessment of transgenic proteins in five areas: structure/activity, serum screening, animal models, quantitative proteomics, and basic mechanisms. A web-based tool is now available that integrates a database of allergenic proteins with a variety of computational tools which could be used to improve our ability to predict allergenicity based on structural analysis. A comprehensive strategy and model protocols have been developed for conducting meaningful serum screening, an extremely challenging process. Several animal models using oral sensitization with adjuvant and one dermal sensitization model have been developed and appear to distinguish allergenic from non-allergenic food extracts. Data presented using a mouse model suggest that pepsin resistance is indicative of allergenicity. Certain questions remain to be addressed before considering animal model validation. Gel-free mass spectrometry is a viable alternative to more labor-intensive approaches to quantitative proteomics. Proteomic data presented on four nontransgenic varieties of soy suggested that if known allergen expression in genetically modified crops falls within the range of natural variability among commercial varieties, there appears to be no need to test further. Finally, basic research continues to elucidate the etiology of food allergy.

Structural analysis of linear and conformational epitopes of allergens

In many countries regulatory agencies have adopted safety guidelines, based on bioinformatics rules from the WHO/FAO and EFSA recommendations, to prevent potentially allergenic novel foods or agricultural products from reaching consumers. We created the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/) to combine data that had previously been available only as flat files on Web pages or in the literature. SDAP was designed to be user friendly, to be of maximum use to regulatory agencies, clinicians, as well as to scientists interested in assessing the potential allergenic risk of a protein. We developed methods, unique to SDAP, to compare the physicochemical properties of discrete areas of allergenic proteins to known IgE epitopes. We developed a new similarity measure, the property distance (PD) value that can be used to detect related segments in allergens with clinical observed cross-reactivity. We have now expanded this work to obtain experimental validation of the PD index as a quantitative predictor of IgE cross-reactivity, by designing peptide variants with predetermined PD scores relative to known IgE epitopes. In complementary work we show how sequence motifs characteristic of allergenic proteins in protein families can be used as fingerprints for allergenicity.