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Prediction and Classification of Allergenicity within Protein Families
Monday, March 7, 2016
South Exhibit Hall H (Convention Center)
Surendra Negi, PhD, Terumi Midoro-Horiuti, MD PhD FAAAAI, Chris Kearney, PhD, Randall M. Goldblum, MD, Werner Braun, PhD
Rationale: The finding that allergens belong to a relatively limited number of protein families provides a unique opportunity to identify the allergen specific motifs (ASMs) that are not present on innocuous proteins from the same family. We derived quantitative physical-chemical descriptors of aligned amino acid sequences and three-dimensional (3D) structures of the allergens within the same family. This approach should allow more accurate predictions of the allergenic potential of proteins within our environment.

Methods: Unique clusters of protein sequences from the pectate lyase family were generated to find conserved motifs. These motifs were compared to those in Jun a 1 and other allergens in the pectate lyase family to identify ASMs. The residues in ASMs were then compared with those in the predicted conformational epitopes of Jun a 1 to find 3D motifs.

Results: We have developed a unique algorithm for identifying ASMs within allergens and have begun to test this approach by identifying potential conformational epitopes on Jun a 1. The structure-function relationships will be validated by synthesizing mutated Jun a 1 and expressing in a tobacco mosaic virus system and testing for alterations in IgE and monoclonal antibody binding.

Conclusions: Our new computational analyses will establish a quantitative platform for identifying proteins that cross-react with known allergens and potential allergenicity of proteins that are being introduced into our environment. This approach will also allow us to identify hypoallergenic derivatives that could be used for rapid and safe immunotherapy.