Bioinformatics approaches to classifying allergens and predicting cross-reactivity

Utility of animal models for predicting human allergenicity

The biochemical characterization of protein structures has led to a better understanding of allergens, their structure/function relationship, and can be very powerful in identifying protein sequences with significant structural similarity to known allergens. However, for scientists, regulators and food manufacturers there exists a need for acquiring additional data on potential allergenicity of proteins, particularly, biotechnology derived molecules in food products for which minimal or no prior human exposure information is available. Since human exposure testing, while direct, is unacceptable, understanding allergy in animals has been used to investigate the allergic response on a molecular level as well as test the potential in vivo allergenicity of food proteins. Rodents seem to be the most likely candidate for assessing allergenicity. For development of an animal test system for allergenicity characterization and testing, a number of criteria are required for qualification for a model of human allergy including acceptable immunization protocols, allergic response measurements, and for standardization and validation of materials and procedures. If an animal test system can minimally provide a basis for measuring the relative physiological response to known allergens, this should be enough to establish a model that produces a relative measure of potential allergenicity. Our article will consider development of an adequate animal model for allergenicity determination that can be validated as a tool in safety assessments.

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.

Allergen structures and biologic functions: the cutting edge of allergy research

Studies of structure and function of allergens using state-of-the-art technologies have led to a better understanding of allergenicity, including aspects related to cross-reactivity, allergen nomenclature, and the identification of antigenic determinants. This information is being applied to the design and production of allergy vaccines, some of which already have proven efficacy and safety in clinical trials.

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.

When does a protein become an allergen? Searching for a dynamic definition based on most advanced technology tools

Since the early beginning of allergology as a science considerable efforts have been made by clinicians and researchers to identify and characterize allergic triggers as raw allergenic materials, allergenic sources and tissues, and more recently basic allergenic structures defined as molecules. The last 15–20 years have witnessed many centres focusing on the identification and characterization of allergenic molecules leading to an expanding wealth of knowledge. The need to organize this information leads to the most important question ‘when does a protein become an allergen?’ In this article, I try to address this question by reviewing a few basic concepts of the immunology of IgE-mediated diseases, reporting on the current diagnostic and epidemiological tools used for allergic disease studies and discussing the usefulness of novel biotechnology tools (i.e. proteomics and molecular biology approaches), information technology tools (i.e. Internet-based resources) and microtechnology tools (i.e. proteomic microarray for IgE testing on molecular allergens). A step-wise staging of the identification and characterization process, including bench, clinical and epidemiological aspects, is proposed, in order to classify allergenic molecules dynamically. This proposal reflects the application and use of all the new tools available from current technologies.

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.

Crystal structure analysis of NP24-I: a thaumatin-like protein

The crystal structure of NP24-I, an isoform of the thaumatin-like protein (TLP) NP24 from tomato, has been reported. A prominent acidic cleft is observed between domains I and II of the three-domain structure of this antifungal protein, a feature common to other antifungal TLPs. The defensive role of the TLPs has also been attributed to their beta-1,3-glucanase activity and here too the acidic cleft is reported to play a vital role. NP24 is known to bind beta-glucans and so a linear beta-1,3-glucan molecule has been docked in the interdomain cleft of NP24-I. From the docked complex it is observed that the beta-glucan chain is so positioned in the cleft that a Glu and Asp residue on either side of it may form a catalytic pair to cause the cleavage of a glycosidic bond. NP24 has been reported to be an allergenic protein and an allergenic motif could be identified on the surface of the helical domain II of NP24-I. In addition, some allergenic motifs bearing high similarity/identity with some predicted Ig-E binding motifs of closely related allergenic TLPs like Jun a 3 (Juniperus ashei, from mountain cedar pollen) and banana-TLP have been identified on the molecular surface of NP24-I.

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.

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.