Common physical-chemical properties correlate with similar structure of the IgE epitopes of peanut allergens

AllergenAI: a deep learning model predicting allergenicity based on protein sequence

Innovations in protein engineering can help redesign allergenic proteins to reduce adverse reactions in sensitive individuals. To accomplish this aim, a better knowledge of the molecular properties of allergenic proteins and the molecular features that make a protein allergenic is needed. We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a given protein. Our approach is solely based on protein sequences, differentiating it from previous tools that use some knowledge of the allergens' physicochemical and other properties in addition to sequence homology. We used the collected data on protein sequences of allergenic proteins as archived in the three well-established databases, SDAP 2.0, COMPARE, and AlgPred 2, to train a convolutional neural network and assessed its prediction performance by cross-validation. We then used Allergen AI to find novel potential proteins of the cupin family in date palm, spinach, maize, and red clover plants with a high allergenicity score that might have an adverse allergenic effect on sensitive individuals. By analyzing the feature importance scores (FIS) of vicilins, we identified a proline-alanine-rich (P-A) motif in the top 50% of FIS regions that overlapped with known IgE epitope regions of vicilin allergens. Furthermore, using∼ 1600 allergen structures in our SDAP database, we showed the potential to incorporate 3D information in a CNN model. Future, incorporating 3D information in training data should enhance the accuracy. AllergenAI is a novel foundation for identifying the critical features that distinguish allergenic proteins.

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.

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.

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.

Distinguishing allergens from non-allergenic homologues using Physical-Chemical Property (PCP) motifs

Quantitative guidelines to distinguish allergenic proteins from related, but non-allergenic ones are urgently needed for regulatory agencies, biotech companies and physicians. In a previous study, we found that allergenic proteins populate a relatively small number of protein families, as characterized by the Pfam database. However, these families also contain non-allergenic proteins, meaning that allergenic determinants must lie within more discrete regions of the sequence. Thus, new methods are needed to discriminate allergenic proteins within those families. Physical-Chemical Properties (PCP)-motifs specific for allergens within a Pfam class were determined for 17 highly populated protein domains. A novel scoring method based on PCP-motifs that characterize known allergenic proteins within these families was developed, and validated for those domains. The motif scores distinguished sequences of allergens from a large selection of 80,000 randomly selected non-allergenic sequences. The motif scores for the birch pollen allergen (Bet v 1) family, which also contains related fruit and nut allergens, correlated better than global sequence similarities with clinically observed cross-reactivities among those allergens. Further, we demonstrated that the average scores of allergen specific motifs for allergenic profilins are significantly different from the scores of non-allergenic profilins. Several of the selective motifs coincide with experimentally determined IgE epitopes of allergenic profilins. The motifs also discriminated allergenic pectate lyases, including Jun a 1 from mountain cedar pollen, from similar proteins in the human microbiome, which can be assumed to be non-allergens. The latter lacked key motifs characteristic of the known allergens, some of which correlate with known IgE binding sites.

Functional classification of protein toxins as a basis for bioinformatic screening

Proteins are fundamental to life and exhibit a wide diversity of activities, some of which are toxic. Therefore, assessing whether a specific protein is safe for consumption in foods and feeds is critical. Simple BLAST searches may reveal homology to a known toxin, when in fact the protein may pose no real danger. Another challenge to answer this question is the lack of curated databases with a representative set of experimentally validated toxins. Here we have systematically analyzed over 10,000 manually curated toxin sequences using sequence clustering, network analysis, and protein domain classification. We also developed a functional sequence signature method to distinguish toxic from non-toxic proteins. The current database, combined with motif analysis, can be used by researchers and regulators in a hazard screening capacity to assess the potential of a protein to be toxic at early stages of development. Identifying key signatures of toxicity can also aid in redesigning proteins, so as to maintain their desirable functions while reducing the risk of potential health hazards.

Heterogeneous responses and cross reactivity between the major peanut allergens Ara h 1, 2,3 and 6 in a mouse model for peanut allergy

BACKGROUND

The relative contribution and the relation between individual peanut allergens in peanut allergic responses is still matter of debate. We determined the individual contribution of peanut proteins to B, T cell and allergic effector responses in a mouse model for peanut allergy.

METHODS

Mice were immunized and challenged by oral gavage with peanut protein extract or isolated allergens Ara h 1, 2, 3 and 6 followed by assessment of food allergic manifestations. In addition, T cell responses to the individual proteins were measured by an in vitro dendritic cell-T cell assay.

RESULTS

Sensitization with the individual peanut proteins elicited IgE responses with specificity to the allergen used as expected. However, cross reactivity among Ara h 1, 2, 3 and 6 was observed. T cell re-stimulations with peanut extract and individual peanut proteins also showed cross reactivity between Ara h 1, 2, 3 and 6. Despite the cross reactivity at the IgE level, only Ara h 2 and 6 were able to elicit mast cell degranulation after an oral challenge. However, after systemic challenge, Ara h 1, 2 and 6 and to lesser extent Ara h 3 were able to elicit anaphylactic responses.

CONCLUSIONS

Ara h 1, 2, 3 and 6 sensitize via the intra-gastric route, but differ in their capacity to cause allergic effector responses. Interestingly, extensive cross reactivity at T cell and antibody level is observed among Ara h 1, 2, 3 and 6, which may have important implications for the diagnosis and therapy of peanut allergy. Awareness about the relative contribution of individual peanut allergens and cross reactivity between these allergens is of importance for current research in diagnostics and therapeutics for and the mechanism of peanut allergy.

The molecular basis of peanut allergy

Peanut allergens can trigger a potent and sometimes dangerous immune response in an increasing number of people. The molecular structures of these allergens form the basis for understanding this response. This review describes the currently known peanut allergen structures and discusses how modifications both enzymatic and non-enzymatic affect digestion, innate immune recognition, and IgE interactions. The allergen structures help explain cross-reactivity among allergens from different sources, which is useful in improving patient diagnostics. Surprisingly, it was recently noted that similar short peptide sequences among unrelated peanut allergens could also be a source of cross-reactivity. The molecular features of peanut allergens continue to inform predictions and provide new research directions in the study of allergic disease.

Assessment of 3D models for allergen research

Allergenic proteins must crosslink specific IgE molecules, bound to the surface of mast cells and basophils, to stimulate an immune response. A structural understanding of the allergen-IgE interface is needed to predict cross-reactivities between allergens and to design hypoallergenic proteins. However, there are less than 90 experimentally determined structures available for the approximately 1500 sequences of allergens and isoallergens cataloged in the Structural Database of Allergenic Proteins. To provide reliable structural data for the remaining proteins, we previously produced more than 500 3D models using an automated procedure, with strict controls on template choice and model quality evaluation. Here, we assessed how well the fold and residue surface exposure of 10 of these models correlated with recently published experimental 3D structures determined by X-ray crystallography or NMR. We also discuss the impact of intrinsically disordered regions on the structural comparison and epitope prediction. Overall, for seven allergens with sequence identities to the original templates higher than 27%, the backbone root-mean square deviations were less than 2 Å between the models and the subsequently determined experimental structures for the ordered regions. Further, the surface exposure of the known IgE epitopes on the models of three major allergens, from peanut (Ara h 1), latex (Hev b 2), and soy (Gly m 4), was very similar to the experimentally determined structures. For the three remaining allergens with lower sequence identities to the modeling templates, the 3D folds were correctly identified. However, the accuracy of those models is not sufficient for a reliable epitope mapping.

Ara h 1 structure is retained after roasting and is important for enhanced binding to IgE

SCOPE

Ara h 1 from roasted peanut binds higher levels of serum immunoglobulin E than raw peanuts and this is likely due to the Maillard reaction. While Ara h 1 linear IgE epitopes have been mapped, the presence and importance of structural epitopes is not clear.

METHODS AND RESULTS

Mass spectrometry, immunoblot, ELISA, circular dichroism (CD), and structural analysis were used to compare structural and subsequent IgE-binding differences in Ara h 1 purified from raw (N) and roasted peanuts (R) and denatured Ara h 1 (D). Although N and R had similar CD spectra, the latter bound significantly more IgE. Decreased IgE binding was seen with the loss of secondary structure. This same IgE-binding pattern [R > N > D] was seen for the sera of ten peanut allergic patients. While the majority of linear epitopes are located on surface and structured regions of Ara h 1, our study shows that conformational epitopes of Ara h 1 bind better to IgE than linear epitopes.

CONCLUSION

Enhanced IgE binding to roasted Ara h 1 could be due to alterations such as chemical modifications to individual amino acids or increased epitope exposure. IgE binding is significantly reduced with loss of structure.

Physicochemical property consensus sequences for functional analysis, design of multivalent antigens and targeted antivirals

Background

Analysis of large sets of biological sequence data from related strains or organisms is complicated by superficial redundancy in the set, which may contain many members that are identical except at one or two positions. Thus a new method, based on deriving physicochemical property (PCP)-consensus sequences, was tested for its ability to generate reference sequences and distinguish functionally significant changes from background variability.

Methods

The PCP consensus program was used to automatically derive consensus sequences starting from sequence alignments of proteins from Flaviviruses (from the Flavitrack database) and human enteroviruses, using a five dimensional set of Eigenvectors that summarize over 200 different scalar values for the PCPs of the amino acids. A PCP-consensus protein of a Dengue virus envelope protein was produced recombinantly and tested for its ability to bind antibodies to strains using ELISA.

Results

PCP-consensus sequences of the flavivirus family could be used to classify them into five discrete groups and distinguish areas of the envelope proteins that correlate with host specificity and disease type. A multivalent Dengue virus antigen was designed and shown to bind antibodies against all four DENV types. A consensus enteroviral VPg protein had the same distinctive high pKa as wild type proteins and was recognized by two different polymerases.

Conclusions

The process for deriving PCP-consensus sequences for any group of aligned similar sequences, has been validated for sequences with up to 50% diversity. Ongoing projects have shown that the method identifies residues that significantly alter PCPs at a given position, and might thus cause changes in function or immunogenicity. Other potential applications include deriving target proteins for drug design and diagnostic kits.

Modeling of allergen proteins found in sea food products

Shellfish are a source of food allergens, and their consumption is the cause of severe allergic reactions in humans. Tropomyosins, a family of muscle proteins, have been identified as the major allergens in shellfish and mollusks species. Nevertheless, few experimentally determined three-dimensional structures are available in the Protein Data Base (PDB). In this study, 3D models of several homologous of tropomyosins present in marine shellfish and mollusk species (Chaf 1, Met e1, Hom a1, Per v1, and Pen a1) were constructed, validated, and their immunoglobulin E binding epitopes were identified using bioinformatics tools. All protein models for these allergens consisted of long alpha-helices. Chaf 1, Met e1, and Hom a1 had six conserved regions with sequence similarities to known epitopes, whereas Per v1 and Pen a1 contained only one. Lipophilic potentials of identified epitopes revealed a high propensity of hydrophobic amino acids in the immunoglobulin E binding site. This information could be useful to design tropomyosin-specific immunotherapy for sea food allergies.

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.

TILLING for allergen reduction and improvement of quality traits in peanut (Arachis hypogaea L.)

BACKGROUND

Allergic reactions to peanuts (Arachis hypogaea L.) can cause severe symptoms and in some cases can be fatal, but avoidance is difficult due to the prevalence of peanut-derived products in processed foods. One strategy of reducing the allergenicity of peanuts is to alter or eliminate the allergenic proteins through mutagenesis. Other seed quality traits could be improved by altering biosynthetic enzyme activities. Targeting Induced Local Lesions in Genomes (TILLING), a reverse-genetics approach, was used to identify mutations affecting seed traits in peanut.

RESULTS

Two similar copies of a major allergen gene, Ara h 1, have been identified in tetraploid peanut, one in each subgenome. The same situation has been shown for major allergen Ara h 2. Due to the challenge of discriminating between homeologous genes in allotetraploid peanut, nested PCR was employed, in which both gene copies were amplified using unlabeled primers. This was followed by a second PCR using gene-specific labeled primers, heteroduplex formation, CEL1 nuclease digestion, and electrophoretic detection of labeled fragments. Using ethyl methanesulfonate (EMS) as a mutagen, a mutation frequency of 1 SNP/967 kb (3,420 M2 individuals screened) was observed. The most significant mutations identified were a disrupted start codon in Ara h 2.02 and a premature stop codon in Ara h 1.02. Homozygous individuals were recovered in succeeding generations for each of these mutations, and elimination of Ara h 2.02 protein was confirmed. Several Ara h 1 protein isoforms were eliminated or reduced according to 2D gel analyses. TILLING also was used to identify mutations in fatty acid desaturase AhFAD2 (also present in two copies), a gene which controls the ratio of oleic to linoleic acid in the seed. A frameshift mutation was identified, resulting in truncation and inactivation of AhFAD2B protein. A mutation in AhFAD2A was predicted to restore function to the normally inactive enzyme.

CONCLUSIONS

This work represents the first steps toward the goal of creating a peanut cultivar with reduced allergenicity. TILLING in peanut can be extended to virtually any gene, and could be used to modify other traits such as nutritional properties of the seed, as shown in this study.

Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.

Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories

We introduce here a new class of invariants for MD trajectories based on the spectral moments pi(k)(L) of the Markov matrix associated to lattice network-like (LN) graph representations of Molecular Dynamics (MD) trajectories. The procedure embeds the MD energy profiles on a 2D Cartesian coordinates system using simple heuristic rules. At the same time, we associate the LN with a Markov matrix that describes the probabilities of passing from one state to other in the new 2D space. We construct this type of LNs for 422 MD trajectories obtained in DNA-drug docking experiments of 57 furocoumarins. The combined use of psoralens+ultraviolet light (UVA) radiation is known as PUVA therapy. PUVA is effective in the treatment of skin diseases such as psoriasis and mycosis fungoides. PUVA is also useful to treat human platelet (PTL) concentrates in order to eliminate Leishmania spp. and Trypanosoma cruzi. Both are parasites that cause Leishmaniosis (a dangerous skin and visceral disease) and Chagas disease, respectively; and may circulate in blood products collected from infected donors. We included in this study both lineal (psoralens) and angular (angelicins) furocoumarins. In the study, we grouped the LNs on two sets; set1: DNA-drug complex MD trajectories for active compounds and set2: MD trajectories of non-active compounds or no-optimal MD trajectories of active compounds. We calculated the respective pi(k)(L) values for all these LNs and used them as inputs to train a new classifier that discriminate set1 from set2 cases. In training series the model correctly classifies 79 out of 80 (specificity=98.75%) set1 and 226 out of 238 (Sensitivity=94.96%) set2 trajectories. In independent validation series the model correctly classifies 26 out of 26 (specificity=100%) set1 and 75 out of 78 (sensitivity=96.15%) set2 trajectories. We propose this new model as a scoring function to guide DNA-docking studies in the drug design of new coumarins for anticancer or antiparasitic PUVA therapy.

Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected drug-receptor pairs (DRPs) of affinity/nonaffinity drugs to similar/dissimilar receptors and we represented them as a large network, which may be used to identify drugs that can act on a receptor. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) substantially increases the potentialities of this kind of networks avoiding time- and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multitarget QSAR (mt-QSAR) classification model. Overall model classification accuracy was 72.25% (1390/1924 compounds) in training, 72.28% (459/635) in cross-validation. Outputs of this mt-QSAR model were used as inputs to construct a network. The observed network has 1735 nodes (DRPs), 1754 edges or pairs of DRPs with similar drug-target affinity (sPDRPs), and low coverage density d = 0.12%. The predicted network has 1735 DRPs, 1857 sPDRPs, and also low coverage density d = 0.12%. After an edge-to-edge comparison (chi-square = 9420.3; p < 0.005), we have demonstrated that the predicted network is significantly similar to the one observed and both have a distribution closer to exponential than to normal.

Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values ξ_k (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The ξ_k (L) values are invariants of a Markov matrix (¹ Π), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA-Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective ξ_k (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy = 85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA-Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.

Resolution and identification of major peanut allergens using a combination of fluorescence two-dimensional differential gel electrophoresis, Western blotting and Q-TOF mass spectrometry

Peanut allergy is triggered by several proteins known as allergens. In this study, the complexity the peanut allergome is investigated with proteomic tools. The strength of this investigation resides in combining the high-resolving power and reproducibility of fluorescence two-dimensional differential gel electrophoresis with specific immunological detection as well as polypeptide sequencing by high-resolution mass spectrometry. Matching of the peanut proteins in 2D gels was achieved by differential labelling whereby peanut proteins and purified allergens (Ara h 1, Ara h 2 or Ara h 3/4) were run on the same gel. Ten protein spots on a mass line of ca. 63-68 kDa were likely to correspond to Ara h 1. Two doublets on two different mass lines at ca. 16 and 18 kDa matched with purified allergen Ara h 2. The basic and acidic sub-units of Ara h 3/4 were observed at masses of ca. 25 kDa and 40-45 kDa, respectively. Subsequently the antibody-binding capacity of spots corresponding to peanut allergens was investigated by Western blotting of 2D gels using antibodies (IgY) raised against Ara h 1, Ara h 2 and the recombinant 40 kDa sub-unit of Ara h 3/4. Final confirmation of the identity of the protein spots matched after 2D electrophoresis and identified by Western blotting was obtained by in-gel digestion of protein spots and analysis by quadrupole time-of-flight mass spectrometry. By using the method developed in our work, the location and identification of two different isoforms of the allergen Ara h 1, the allergen Ara h 2 and six isoforms of the allergen Ara h 3/4 in 2D peanut protein maps was established.

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.

Characteristic motifs for families of allergenic proteins

The identification of potential allergenic proteins is usually done by scanning a database of allergenic proteins and locating known allergens with a high sequence similarity. However, there is no universally accepted cut-off value for sequence similarity to indicate potential IgE cross-reactivity. Further, overall sequence similarity may be less important than discrete areas of similarity in proteins with homologous structure. To identify such areas, we first classified all allergens and their subdomains in the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/) to their closest protein families as defined in Pfam, and identified conserved physicochemical property motifs characteristic of each group of sequences. Allergens populate only a small subset of all known Pfam families, as all allergenic proteins in SDAP could be grouped to only 130 (of 9318 total) Pfams, and 31 families contain more than four allergens. Conserved physicochemical property motifs for the aligned sequences of the most populated Pfam families were identified with the PCPMer program suite and catalogued in the webserver MotifMate (http://born.utmb.edu/motifmate/summary.php). We also determined specific motifs for allergenic members of a family that could distinguish them from non-allergenic ones. These allergen specific motifs should be most useful in database searches for potential allergens. We found that sequence motifs unique to the allergens in three families (seed storage proteins, Bet v 1, and tropomyosin) overlap with known IgE epitopes, thus providing evidence that our motif based approach can be used to assess the potential allergenicity of novel proteins.

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

Similarities in the sequence and structure of allergens can explain clinically observed cross-reactivities. Distinguishing sequences that bind IgE in patient sera can be used to identify potentially allergenic protein sequences and aid in the design of hypo-allergenic proteins. The property distance index PD, incorporated in our Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/), may identify potentially cross-reactive segments of proteins, based on their similarity to known IgE epitopes. We sought 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 three linear IgE epitopes of Jun a 1, the dominant allergen from mountain cedar pollen. For each of the three epitopes, 60 peptides were designed with increasing PD values (decreasing physicochemical similarity) to the starting sequence. The peptides synthesized on a derivatized cellulose membrane were probed with sera from patients who were allergic to Jun a 1, and the experimental data were interpreted with a PD classification method. Peptides with low PD values relative to a given epitope were more likely to bind IgE from the sera than were those with PD values larger than 6. Control sequences, with PD values between 18 and 20 to all the three epitopes, did not bind patient IgE, thus validating our procedure for identifying negative control peptides. The PD index is a statistically validated method to detect discrete regions of proteins that have a high probability of cross-reacting with IgE from allergic patients.

Comprehensive 3D-modeling of allergenic proteins and amino acid composition of potential conformational IgE epitopes

Similarities in sequences and 3D structures of allergenic proteins provide vital clues to identify clinically relevant immunoglobulin E (IgE) cross-reactivities. However, experimental 3D structures are available in the Protein Data Bank for only 5% (45/829) of all allergens catalogued in the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP). Here, an automated procedure was used to prepare 3D-models of all allergens where there was no experimentally determined 3D structure or high identity (95%) to another protein of known 3D structure. After a final selection by quality criteria, 433 reliable 3D models were retained and are available from our SDAP Website. The new 3D models extensively enhance our knowledge of allergen structures. As an example of their use, experimentally derived "continuous IgE epitopes" were mapped on 3 experimentally determined structures and 13 of our 3D-models of allergenic proteins. Large portions of these continuous sequences are not entirely on the surface and therefore cannot interact with IgE or other proteins. Only the surface exposed residues are constituents of "conformational IgE epitopes" which are not in all cases continuous in sequence. The surface exposed parts of the experimental determined continuous IgE epitopes showed a distinct statistical distribution as compared to their presence in typical protein-protein interfaces. The amino acids Ala, Ser, Asn, Gly and particularly Lys have a high propensity to occur in IgE binding sites. The 3D-models will facilitate further analysis of the common properties of IgE binding sites of allergenic proteins.

A structural basis for food allergy: the role of cross-reactivity

PURPOSE OF REVIEW

Immunologic cross-reactivity, which is important in many aspects of host defense and immune-mediated diseases, is a prominent feature of allergic disorders. The goal of this article is to define allergenic cross-reactivity and its role in food allergy, review current understanding of mechanisms of cross-reactivity, and consider how advances in our ability to predict cross-reactivity can impact diagnosis and treatment of food allergy.

RECENT FINDINGS

Recent evidence suggests that specific T cells, in addition to IgE, developed in response to inhaled allergens can cross-react with related food allergens, leading to distinct clinical reactions. Several new cross-reactivities have been identified, including food-food, pollen-food, and latex-venom associations. Debate continues regarding prediction of allergenicity based on protein structure, and clinical relevance of in-vitro testing. Cross-reactivity is also being used to develop specific immunotherapy for treatment of food allergy.

SUMMARY

A thorough understanding of immunologic cross-reactivity is essential to advancing our knowledge about food allergy. This knowledge will help elucidate the pathogenesis of the disorder and prevent exposures to allergenic, genetically engineered foods. New insight will allow for better utilization of current diagnostic tools and the development of more accurate tests and therapies for food allergy.

Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors

Major histocompatibility complex (MHC) molecules bind short peptides resulting from intracellular processing of foreign and self proteins, and present them on the cell surface for recognition by T-cell receptors. We propose a new robust approach to quantitatively model the binding affinities of MHC molecules by quantitative structure-activity relationships (QSAR) that use the physical-chemical amino acid descriptors E1-E5. These QSAR models are robust, sequence-based, and can be used as a fast and reliable filter to predict the MHC binding affinity for large protein databases.

EVALLER: a web server for in silico assessment of potential protein allergenicity

Bioinformatics testing approaches for protein allergenicity, involving amino acid sequence comparisons, have evolved appreciably over the last several years to increased sophistication and performance. EVALLER, the web server presented in this article is based on our recently published 'Detection based on Filtered Length-adjusted Allergen Peptides' (DFLAP) algorithm, which affords in silico determination of potential protein allergenicity of high sensitivity and excellent specificity. To strengthen bioinformatics risk assessment in allergology EVALLER provides a comprehensive outline of its judgment on a query protein's potential allergenicity. Each such textual output incorporates a scoring figure, a confidence numeral of the assignment and information on high- or low-scoring matches to identified allergen-related motifs, including their respective location in accordingly derived allergens. The interface, built on a modified Perl Open Source package, enables dynamic and color-coded graphic representation of key parts of the output. Moreover, pertinent details can be examined in great detail through zoomed views. The server can be accessed at http://bioinformatics.bmc.uu.se/evaller.html.

Homology modelling and conformational analysis of IgE-binding epitopes of Ara h 3 and other legumin allergens with a cupin fold from tree nuts

Linear IgE-binding epitopes identified in legumin allergens of peanut (Ara h 3) and other allergenic tree nuts (Jug r 4 of walnut, Cor a 9 of hazelnut, Ana o 2 cashew nut) were mapped on three-dimensional models of the proteins built up by homology modelling. A conformational analysis revealed that consensual surface-exposed IgE-binding epitopes exhibited some structural homology susceptible to account for the IgE-binding cross-reactivity observed among peanut and tree nut allergens. This structurally related cross-reactivity seems irrespective of the botanical origin of the allergens and thus demands that persons allergic to peanut avoid other three nuts to prevent possible allergic reactions. IgE-binding epitopes similar to those found in 11S globulin allergens do not apparently occur in other vicilin allergens with the cupin fold from peanut (Ara h 1) or tree nuts (Jug r 2 of walnut, Cor a 1 of hazel nut, Ana o 3 of cashew nut).

Bioinformatics approaches to classifying allergens and predicting cross-reactivity

Allergenic proteins from very different environmental sources have similar sequences and structures. This fact may account for multiple allergen syndromes, whereby a myriad of diverse plants and foods may induce a similar IgE-based reaction in certain patients. Identifying the common triggering protein in these sources, in silico, can aid designing individualized therapy for allergen sufferers. This article provides an overview of databases on allergenic proteins, and ways to identify common proteins that may be the cause of multiple allergy syndromes. The major emphasis is on the relational Structural Database of Allergenic Proteins (SDAP []), which includes cross-referenced data on the sequence, structure, and IgE epitopes of over 800 allergenic proteins, coupled with specially developed bioinformatics tools to group all allergens and identify discrete areas that may account for cross-reactivity. SDAP is freely available on the Web to clinicians and patients.