BACKGROUND: Innovations in protein engineering offer promising solutions for redesigning allergenic proteins to minimize adverse reactions in sensitive individuals. Earlier models for predicting allergenicity have relied on the knowledge of physicochemical properties and sequence homology to assess the potential risk. However, to better understand the allergenic proteins’ sequence features, we need a novel sequence-based deep learning model for predicting allergenicity. RESULTS: We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a protein’s sequence without using any other known features. Our study utilized allergenic protein sequence data 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 AllergenAI 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. We then used the approximately 1600 allergen structures in our SDAP database, in a pilot study to show the potential to incorporate 3D information in a CNN model. The prediction quality was slightly increased. CONCLUSION: Our allergenicity prediction study through the development of AllergenAI provides a foundation for identifying the critical features that distinguish allergenic proteins.

The phenomenon of cross-reactivity between allergenic proteins plays an important role to understand how the immune system recognizes different antigen proteins. Allergen proteins are known to cross-react if their sequence comparison shows a high sequence identity which also implies that the proteins have a similar 3D fold. In such cases, linear sequence alignment methods are frequently used to predict cross-reactivity between allergenic proteins. However, the prediction of cross-reactivity between distantly related allergens continues to be a challenging task. To overcome this problem, we developed a new structure-based computational method, Cross-React, to predict cross-reactivity between allergenic proteins available in the Structural Database of Allergens (SDAP). Our method is based on the hypothesis that we can find surface patches on 3D structures of potential allergens with amino acid compositions similar to an epitope in a known allergen. We applied the Cross-React method to a diverse set of seven allergens, and successfully identified several cross-reactive allergens with high to moderate sequence identity which have also been experimentally shown to cross-react. Based on these findings, we suggest that Cross-React can be used as a predictive tool to assess protein allergenicity and cross-reactivity. Availability and Implementation: : Cross-React is available at: http://curie.utmb.edu/Cross-React.html. Contact: [email protected].

UNLABELLED: A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. AVAILABILITY: The InterProSurf web server is available at http://curie.utmb.edu/