Prediction of Allergenicity by Linear and Conformational EpitopesEPA Grant Number: R834823
Title: Prediction of Allergenicity by Linear and Conformational Epitopes
Investigators: Braun, Werner , Ivanciuc, Ovidiu I , Schein, Catherine H.
Institution: The University of Texas Medical Branch - Galveston
EPA Project Officer: McOliver, Cynthia
Project Period: September 15, 2010 through September 30, 2014
Project Amount: $425,000
RFA: Approaches to Assessing Potential Food Allergy from Genetically Engineered Plants (2009) RFA Text | Recipients Lists
Research Category: Food Allergy , Health
We propose to establish and validate new criteria for the risk assessment of a pesticide protein being allergenic. Recombinant genetic tools are increasingly used by the biotechnology industry to introduce novel proteins into crops for protection against pesticides, as plant-incorporated protectants (PIP) or to increase the nutritional value. Appropriate safeguards must be in place to insure that this will not introduce new proteins that are potentially allergenic in the plants. The goal of this project is to provide quantitative bioinformatics criteria to determine the potential allergenicity of transgenic proteins in food products and PIPs from allergen specific motifs. Our specific aims are to: maintain and expand the currently available information in our Structural Data Base of Allergenic Proteins (SDAP) on the sequences, 3D structures and IgE epitopes of allergens (aim1); to establish a data base of allergen specific motifs that can be quantitatively screened to estimate the potential allergenicity of a query protein sequence (aim 2); to develop and validate novel computational methods to detect similar linear motifs, and to compare the sensitivity and specificity of this new approach to previously suggested bioinformatics guidelines (aim3).
We will expand our SDAP web server by sequences and allergen specific motifs of novel allergens, and include relevant physico-chemical data on allergens, such as pepsin digestibility, heat stability, and other factors of gastrointestinal processing in the data base. Allergen specific motifs are generated using the classification of allergens in protein families by PFAM, and restrict motif identification to known allergens within each family. We will assess the new prediction method determining the specificity and sensitivity of these predictions by test data sets with allergens and non-allergens. We will also assess the validity of the approach using sequences from a variety of peanut isoforms and irradiated cultivars that have been screened for their ability to bind IgE from patient sera. A new bioinformatics search method that locates structurally similar motifs in a data base of 3D structures will be tested with data on observed clinical cross-reactivity, and compared to previously suggested bioinformatics guidelines on linear epitopes.
Novel insights on the sequential and 3D characteristics of currently known allergens and on quantitative descriptors for allergenicity will provide an improved scientific basis for new bio-safety regulations. All new bioinformatics tools developed in this project will be available to the scientific community on our SDAP Web server. Another result of this project is the systematic, statistical analysis of the new bioinformatics rules to previously suggested guidelines.