AllergyPred: a web server for allergen prediction

Area of Science:

• Computational biology
• Allergenicity assessment
• Bioinformatics

Background:

• Identifying allergenic proteins and chemicals is crucial for product safety, especially for sensitive populations.
• Computational tools can aid in assessing allergenic potential, minimizing risks, and guiding safer product development.
• Food allergens can interact with drugs, affecting processing, worsening allergic responses, and complicating treatment.

Purpose of the Study:

• To present AllergyPred, a novel web server for predicting both protein- and chemical-based allergens.
• To provide a free and open-access tool for researchers and developers to assess allergenicity.
• To support safer product development by identifying potential allergenic risks.

Main Methods:

• Utilized five distinct predictive models within the AllergyPred web server.
• Employed sequence analysis, structural modeling, and epitope mapping for allergen prediction.
• Accepted protein IDs, sequences, chemical IDs, and structures as input data.

Main Results:

• Developed AllergyPred, a web server capable of predicting protein and chemical allergens.
• The server is freely accessible without login requirements at https://allergypred.charite.de/AllergyPred/.
• Prediction results are presented in downloadable tables in various file formats.

Conclusions:

• AllergyPred offers a valuable computational resource for identifying potential allergens.
• The tool facilitates safer product reformulation and development by predicting allergenic risks.
• Accessible results support users in reporting and utilizing allergenicity predictions for their projects.