Proteome mining for novel IgE-binding proteins from the German cockroach (Blattella germanica) and allergen profiling of patients

Although cockroaches are known to produce allergens that can cause IgE-mediated hypersensitivity reactions, including perennial rhinitis and asthma, the various cockroach allergens have not yet been fully studied. Many proteins from the German cockroach show high IgE reactivity, but have never been comprehensively characterized. To identify these potential allergens, proteins were separated by 2-DE and IgE-binding proteins were analyzed by nanoLC-MS/MS or N-terminal sequencing analysis. Using a combination of proteomic techniques and bioinformatic allergen database analysis, we identified a total of ten new B. germanica IgE-binding proteins. Of these, aldolase, arginine kinase, enolase, Hsp70, triosephosphate isomerase, and vitellogenin have been reported as allergens in species other than B. germanica. Analysis of the Food Allergy Research and Resource Program allergen database indicated that arginine kinase, enolase, and triosephosphate isomerase showed significant potential cross-reactivity with other related allergens. This study revealed that vitellogenin is an important novel B. germanica allergen. Personalized profiling and reactivity of IgE Abs against the panel of IgE-binding proteins varied between cockroach-allergic individuals. These findings make it possible to monitor the individual IgE reactivity profile of each patient and facilitate personalized immunotherapies for German cockroach allergy disorders.     

Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins

MOTIVATION: Identification of potentially allergenic proteins is needed for the safety assessment of genetically modified foods, certain pharmaceuticals and various other products on the consumer market. Current methods in bioinformatic allergology exploit common features among allergens for the detection of amino acid sequences of potentially allergenic proteins. Features for identification still unexplored include the motifs occurring commonly in allergens, but rarely in ordinary proteins. In this paper, we present an algorithm for the identification of such motifs with the purpose of biocomputational detection of amino acid sequences of potential allergens. RESULTS: Identification of allergen-representative peptides (ARPs) with low or no occurrence in proteins lacking allergenic properties is the essential component of our new method, designated DASARP (Detection based on Automated Selection of Allergen-Representative Peptide). This approach consistently outperforms the criterion based on identical peptide match for predicting allergenicity recommended by ILSI/IFBC and FAO/WHO and shows results comparable to the alignment-based criterion as outlined by FAO/WHO. AVAILABILITY: The detection software and the ARP set needed for the analysis of a query protein reported here are properties of the Swedish National Food Agency and are available upon request. The protein sequence sets used in this work are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp. Allergenicity assessment for specific protein sequences of interest is also possible via ulfh@slv.se     

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins

Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. Availability: AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.     

Evidence of a novel allergenic protein Narcin in the bulbs of Narcissus tazetta

Several plant-derived allergens have been identified which result in the formation of immunoglobulin E antibodies. Primarily, these allergens belong to the protein families including seed storage proteins, structural proteins and pathogenesis-related proteins. Several allergens are also reported from flower bulbs which cause contact dermatitis. Such symptoms are highly common with the bulb growers handling different species of Narcissus. Narcissus toxicity is also reported if the bulbs are consumed accidentally. The present study aimed to characterize the protein from the bulbs of Narcissus tazetta responsible for its allergenic response. A 13 kDa novel allergenic protein, Narcin was isolated from the bulbs of Narcissus tazetta. The protein was extracted using ammonium sulfate fractionation. The protein was further purified by anion exchange chromatography followed by gel filtration chromatography. The N-terminal sequence of the first 15 amino-acid residues was determined using Edman degradation. The allergenicity of the protein was measured by cytokine production using flow cytometry in peripheral blood mononuclear cells. Further estimation of total IgE was performed by ELISA method. This novel protein was found to induce pro-inflammatory cytokines and thus induce allergy by elevating total IgE level. The novel protein, Narcin isolated from Narcissus tazetta was found to exhibit allergenic properties.     

SiteSeek: Post-translational modification analysis using adaptive locality-effective kernel methods and new profiles

Background  

Post-translational modifications have a substantial influence on the structure and functions of protein. Post-translational phosphorylation is one of the most common modification that occur in intracellular proteins. Accurate prediction of protein phosphorylation sites is of great importance for the understanding of diverse cellular signalling processes in both the human body and in animals. In this study, we propose a new machine learning based protein phosphorylation site predictor, SiteSeek. SiteSeek is trained using a novel compact evolutionary and hydrophobicity profile to detect possible protein phosphorylation sites for a target sequence. The newly proposed method proves to be more accurate and exhibits a much stable predictive performance than currently existing phosphorylation site predictors.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

Food allergen detection by mass spectrometry: From common to novel protein ingredients

Food allergens are molecules, mainly proteins, that trigger immune responses in susceptible individuals upon consumption even when they would otherwise be harmless. Symptoms of a food allergy can range from mild to acute; this last effect is a severe and potentially life-threatening reaction. The European Union (EU) has identified 14 common food allergens, but new allergens are likely to emerge with constantly changing food habits. Mass spectrometry (MS) is a promising alternative to traditional antibody-based assays for quantifying multiple allergenic proteins in complex matrices with high sensitivity and selectivity. Here, the main allergenic proteins and the advantages and drawbacks of some MS acquisition protocols, such as multiple reaction monitoring (MRM) and data-dependent analysis (DDA) for identifying and quantifying common allergenic proteins in processed foodstuffs are summarized. Sections dedicated to novel foods like microalgae and insects as new sources of allergenic proteins are included, emphasizing the significance of establishing stable marker peptides and validated methods using database searches. The discussion involves the in-silico digestion of allergenic proteins, providing insights into their potential impact on immunogenicity. Finally, case studies focussing on microalgae highlight the value of MS as an effective analytical tool for ensuring regulatory compliance throughout the food control chain.     

Novel families of toxin-like peptides in insects and mammals: a computational approach

Most animal toxins are short proteins that appear in venom and vary in sequence, structure and function. A common characteristic of many such toxins is their apparent structural stability. Sporadic instances of endogenous toxin-like proteins that function in non-venom context have been reported. We have utilized machine learning methodology, based on sequence-derived features and guided by the notion of structural stability, in order to conduct a large-scale search for toxin and toxin-like proteins. Application of the method to insect and mammalian sequences revealed novel families of toxin-like proteins. One of these proteins shows significant similarity to ion channel inhibitors that are expressed in cone snail and assassin bug venom, and is surprisingly expressed in the bee brain. A toxicity assay in which the protein was injected to fish induced a strong yet reversible paralytic effect. We suggest that the protein may function as an endogenous modulator of voltage-gated Ca(2+) channels. Additionally, we have identified a novel mammalian cluster of toxin-like proteins that are expressed in the testis. We suggest that these proteins might be involved in regulation of nicotinic acetylcholine receptors that affect the acrosome reaction and sperm motility. Finally, we highlight a possible evolutionary link between venom toxins and antibacterial proteins. We expect our methodology to enhance the discovery of additional novel protein families.     

Mapping of fruit allergens by 2D electrophoresis and immunodetection

Proteomic analyses of fruits are confronted with a series of specific obstacles: a general low protein content in plant tissues, allergen extraction from highly complex matrices and protein determination in the presence of interfering compounds. Different methods are currently being introduced to achieve higher protein yields and a simultaneous removal of interfering substances, such as polyphenols and polysaccharides. However, no universal protocol suitable for protein purification from any given plant species is available. Protein profiling by 2DE-western blotting offers a powerful tool for the detection and characterization of known and novel plant allergens. Moreover, the detection of IgE-reactive proteins from fruits is improved by combining western blot and alternative visualization techniques. The recent developments in bioinformatics and databases facilitate the interpretation of profiling studies with regard to novel potential fruit allergens.     

Mapping of fruit allergens by 2D electrophoresis and immunodetection

Proteomic analyses of fruits are confronted with a series of specific obstacles: a general low protein content in plant tissues, allergen extraction from highly complex matrices and protein determination in the presence of interfering compounds. Different methods are currently being introduced to achieve higher protein yields and a simultaneous removal of interfering substances, such as polyphenols and polysaccharides. However, no universal protocol suitable for protein purification from any given plant species is available. Protein profiling by 2DE-western blotting offers a powerful tool for the detection and characterization of known and novel plant allergens. Moreover, the detection of IgE-reactive proteins from fruits is improved by combining western blot and alternative visualization techniques. The recent developments in bioinformatics and databases facilitate the interpretation of profiling studies with regard to novel potential fruit allergens.     

Human health concerns with GM crops

Biotechnology was used in the first generation of so-called 'GM' crops to provide growers with complimentary and sometimes alternative crop management solutions to pesticides. Selected host genes or genes identified from other plants or non-plant sources are modified or transferred to a crop plant. The new or altered protein expression resulting from these modifications confer on the plant a desired physiological trait, such as resistance to particular herbicides or insect pests. Second generation modifications provide traits such as enhanced nutritional or health-promoting characteristics that are of benefit to consumers. The commonly raised concerns about possible implications for human health are: inherent toxicity of the novel gene and their products, the potential to express novel antigenic proteins or alter levels of existing protein allergens, the potential for unintended effects resulting from alterations of host metabolic pathways or over expression of inherently toxic or pharmacologically active substances and the potential for nutrient composition in the new food occur differing significantly from a conventional counterpart. Foods produced using biotechnology are subjected to far greater levels of scrutiny than foods produced by traditional plant breeding techniques. The accepted analytical, nutritional and toxicological methods employed to support this scrutiny and to assess and assure that a 'GM' food is a safe and nutritious as its 'non-GM' counterpart are discussed. The challenges associated with identifying unintended effects in whole GM foods and the promise new (proteomics/genomic) technologies offer opposite traditional toxicity testing paradigms are appraised.     

PPI_SVM: Prediction of protein-protein interactions using machine learning,domain-domain affinities and frequency tables

Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: .     

Bet v 1 proteins, the major birch pollen allergens and members of a family of conserved pathogenesis-related proteins, show ribonuclease activity in vitro

The Bet v. 1 gene family of birch encodes the major pollen allergens as well as pathogenesis-related (PR) proteins that are induced by microbes in somatic tissues. These PR proteins belong to a group of conserved intracellular defense-related proteins that have been termed 'ribonuclease-like' PR proteins, on the basis of the partial sequence homology observed between PR1, a Bet v 1-homologue from parsley, and a recently characterized ginseng ribonuclease. However, this enzymatic activity has not yet been demonstrated, not for any of the members of this family of PR proteins, nor for the related pollen allergens. We have investigated the possible nuclease activity of Bet v 1 using apparently homogeneous preparations of natural Bet v 1 purified from birch pollen, and a recombinant non-fusion protein purified from E. coli extracts. We report here that Bet v 1 proteins indeed possess an intrinsic ribonucleolytic activity as they can digest different RNA substrates in vitro, but show no activity on single or double-stranded DNA.     

WebAllergen: a web server for predicting allergenic proteins

WebAllergen is a web server that predicts the potential allergenicity of proteins. The query protein will be compared against a set of prebuilt allergenic motifs that have been obtained from 664 known allergen proteins. The query will also be compared with known allergens that do not have detectable allergenic motifs. Moreover, users are allowed to upload their own allergens as alternative training sequences on which a new set of allergenic motifs will be built. The query sequences can also be compared with these motifs. AVAILABILITY: http://weballergen.bii.a-star.edu.sg/     

Novel classes of fatty acid and retinol binding protein from nematodes

Parasitic nematodes have recently been found to produce proteins which represent two new classes of fatty acid and retinoid binding protein. The first is the nematode polyprotein allergens/antigens (NPAs) which, as their name suggests, are synthesised as large polyproteins which are subsequently cleaved at regularly spaced sites to form multiple copies of a fatty acid binding protein of approximately 14.5 kDa. Binding studies using molecular environment-sensitive fluorescent ligands have shown that the binding site is highly unusual, producing blue-shifting in fluorescence to an unprecedented degree, suggesting a remarkably non-polar environment and isolation from solvent water. Computer-based structural predictions and biophysical observations have identified the NPAs as highly helical proteins which might form a four helix bundle, so constitute a new class of lipid binding protein from animals. The second class, like the NPAs, binds both fatty acids and retinol, but with a higher affinity for the latter. These are also highly helical but are structurally distinct from the NPAs. The biological function of these new classes of protein are discussed in the context of both the metabolic requirements of the parasites and the possible role of the proteins in control of the immune and inflammatory environment of the tissue sites parasitised.     

Selection of cryptic B-cell epitopes using informational analysis of protein sequences

Sub-unit vaccines are synthetic or recombinant peptides representing T- or B-cell epitopes of major protein antigens from a particular pathogen. Epitope selection requires the synthesis of peptides that overlap the protein sequences and screening for the most effective ones. In this study a new method of immunogenic peptide selection based on the analysis of information structure of protein sequences is suggested. The analysis of known B-cell epitope location in the information structure of Aspergillus fumigatus proteins Asp f 2 and Asp f 3 has shown that epitopes are scattered along the sequences of proteins for the exception of sites with Increased Degree Information Coordination (IDIC). Based on these results peptides from different allergens such as Asp f 2, Der p 1, and Fel d 1 were selected and produced in a recombinant form in the context of yeast virus-like particles (VLPs). Immunization of mice with VLPs containing peptides form allergens has induced the production of IgG able to recognize full-length antigens. This result suggests that the analysis of information structure of proteins can be used for the selection of peptides possessing cryptic B-cell epitope activity.     

An application of capsid-specific artificial ankyrin repeat proteinproduced in E. coli for immunochromatographic assay as a surrogate for antibody

Immunochromatographic strip test is a unique type of rapid test that has been developed for use as part of a diagnostic kit for the rapid detection of antibodies and/or other proteins of interest. For the detection of target proteins, most of the commercial tests are assembled based on the conjugation of colloidal gold particles to monoclonal antibodies embedded within the conjugate pad of a strip test. In this study, we tested the novel concept of using an artificial non-antibody structure for generating a colloidal gold conjugate (CGC). We exploited the property of an ankyrin repeat protein that specifically binds to the HIV-1 capsid protein termed Ank^GAG1D4. This construct was applied as a model structure to create Ank1D4-CGC and used as a new type of visible detector system and termed it ankyrin-based immunochromatographic strip (ABIS) test. The ABIS test was shown to be highly sensitive with a lower limit of detection of the target protein at 0.1 μg/ml. Moreover, the ABIS test was not only highly sensitive but also shared a level of specificity within the same range of the commercial test kit. The results of the studies presented herein therefore demonstrate the novel application of an artificial non-immunoglobulin structure (ankyrin repeat protein) as the new line of a visible detector using a rapid diagnostic test with characteristics that have the potential to be superior to those that utilize antibody-based tests.     

Improved prediction of allergenicity by combination of multiple sequence motifs

The identification and validation of protein allergens have become more important nowadays as more and more transgenic proteins are introduced into our food chains. Current allergen prediction algorithms focus on the identification of single motif or single allergen peptide for allergen detection. However, an analysis of the 575 allergen dataset shows that most allergens contain multiple motifs. Here, we present a novel algorithm that detects allergen by making use of combinations of motifs. Sensitivity of 0.772 and specificity of 0.904 were achieved by the proposed algorithm to predict allergen. The specificity of the proposed approach is found to be significantly higher than traditional single motif approaches. The high specificity of the proposed algorithm is useful in filtering out false positives, especially when laboratory resources are limited.