The cases of allergic diseases and asthma have steadily increased in the US in the last two decades. The best medical advice to patients is to avoid allergenic sources, which in severe cases can lead to fatal anaphylactic reactions. Hypersensitive patients react in general not only to a specific allergenic protein, but also to proteins in other sources that share some structural similarity to a known allergen. Here we will develop better computational tools to quantify the degree to which this similarity could indicate the potential for significant cross reactivity. We will define linear and 3D motifs, based on common physicochemical properties, for all major allergens using the amino acid sequences and 3D structures of allergens catalogued in our Structural Database of Allergenic Proteins (SDAP). We will then test the hypothesis that the allergen specific motifs we derive can distinguish allergenic proteins from their homologues of commensal bacteria in the human microbiome and of the human genome. It has already been shown that some major allergenic food proteins have low sequence similarity to their human homologues. We will examine the structural relation of one particular class of allergens, the pectate lyase family, to their homologues in the human microbiome, and test our computational predictions experimentally using a dominant allergen from this family, the mountain cedar pollen, Jun a 1.
Our specific aims are (1) to develop and assess a new computational method to define surface 3D motifs in families of structurally related allergens, and use these to predict potential cross-reacting allergens;(2) to identify conformational IgE epitopes of the mountain cedar allergen Jun a 1 based on linear and 3D motifs and confirm their identity using monoclonal antibodies (mAbs) that have similar reactivities to those of serum IgE Abs from Jun a 1-sensitive patients;(3) to search the human microbiome sequence data base for protein homologues of airborne allergens in SDAP, generate 3D models for those homologues and compare the surface characteristics of those proteins and their allergenic homologues. We will make all results of the motif analysis publicly available on the SDAP web server. This will provide clinicians with the tools to alert hypersensitive patients to the presence of similar proteins in other sources. Defining characteristic features of allergens is also needed by biotechnology companies or regulators to avoid introducing novel allergenic foods or drugs in the market place by genetic engineering.

Public Health Relevance

The overall goal of this project is to develop novel three-dimensional motifs that quantitatively characterize allergenic proteins, and distinguish those from their non-allergenic counterparts. Defining characteristic features of allergenicity is needed by biotechnology companies or regulators to avoid introducing novel allergenic foods or drugs in the market place and for allergen researchers to design new hypoallergenic proteins for use in foods and immunotherapy.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Exploratory/Developmental Grants (R21)
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Macromolecular Structure and Function D Study Section (MSFD)
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Minnicozzi, Michael
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University of Texas Medical Br Galveston
Schools of Medicine
United States
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Lu, Wenzhe; Negi, Surendra S; Schein, Catherine H et al. (2018) Distinguishing allergens from non-allergenic homologues using Physical-Chemical Property (PCP) motifs. Mol Immunol 99:1-8
Negi, Surendra S; Schein, Catherine H; Ladics, Gregory S et al. (2017) Functional classification of protein toxins as a basis for bioinformatic screening. Sci Rep 7:13940
Negi, Surendra S; Braun, Werner (2017) Cross-React: a new structural bioinformatics method for predicting allergen cross-reactivity. Bioinformatics 33:1014-1020
Chen, Xueni; Negi, Surendra S; Liao, Sumei et al. (2016) Conformational IgE epitopes of peanut allergens Ara h 2 and Ara h 6. Clin Exp Allergy 46:1120-1128