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AllergenAI

A deep learning model predicting allergenicity based on protein sequence

SDAP All Allergens SDAP Food Allergens Submit New Allergen Mirror Site @ UT Houston

Alphabetical listing of allergens A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

AllergenAI Overview

Innovations in protein engineering can help redesign allergenic proteins to reduce adverse reactions in sensitive individuals. To accomplish this aim, a better knowledge of the molecular properties of allergenic proteins and the molecular features that make a protein allergenic is needed.

We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a given protein. Our approach is solely based on protein sequences, differentiating it from previous tools that use some knowledge of the allergens’ physicochemical and other properties in addition to sequence homology.

We used the collected data on protein sequences of allergenic proteins as 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.

Furthermore, using ~1600 allergen structures in our SDAP database, we showed the potential to incorporate 3D information in a CNN model. AllergenAI is a novel foundation for identifying the critical features that distinguish allergenic proteins.

AllergenAI Prediction

⚠️ AllergenAI is trained on allergenic proteins having fewer than 1000 amino acids. Results for longer sequences may be unreliable.

Training and Validation Data

Training data

Cupin proteins in SDAP 2.0

Non-allergen proteins in the cupin Pfam

Modeling with SDAP allergens (with protein sequence and 3D structure information)

Modeling with SDAP allergens (protein sequence only, without 3D structure)

Available Models and Code

AllergenAI Model Files

Step 1 — Pre-process input protein sequence

AllergenAI_preprocess.py — make one-hot encoded protein matrix from FASTA input

# Example command
python AllergenAI_preprocess.py input.fasta

# Example with provided test file
python AllergenAI_preprocess.py Cupin.fasta

Example FASTA file: Cupin.fasta

Step 2 — Run AllergenAI prediction

Run_AllergenAI.py — predict the allergenicity of your protein

# Example command
python Run_AllergenAI.py input.txt

# Example with cupin test file
python Run_AllergenAI.py Cupin.txt

Example one-hot encoded input (output of pre-processing step): Cupin.txt

Requirements

Software and packages required to train and run AllergenAI

Python 3
tensorflow
keras 2.11
numpy
pandas

# Create a dedicated conda environment
conda update conda
conda create -n allergenai python=3
conda activate allergenai

# Install required packages
conda install numpy
conda install pandas
conda install tensorflow
pip install --upgrade tensorflow
conda install keras=2.11

Mirror Website

Visit https://compbio.uth.edu/AllergenAI/ (UT Houston mirror)

Related Citation

Liu J, Negi SS, Yang C, Zhou X, Schein CH, Braun W, Kim P. AllergenAI: a deep learning model predicting allergenicity based on protein sequence. BMC Bioinformatics. 2025 Nov 18;26(1):279. [Abstract] [Full Paper]
Negi SS, Schein CH, Braun W. The updated Structural Database of Allergenic Proteins (SDAP 2.0) provides 3D models for allergens and incorporated Bioinformatics Tools. J Allergy Clin Immunol Glob. 2023;2(4):100162. [Abstract] [Full Paper]