DeepPlantAllergy: deep learning for explainable prediction of allergenicity in plant proteins.

Abstract

Allergy is an immune response triggered by specific peptides recognized by immune system effectors. While several bioinformatics tools have been developed to predict protein allergenicity, most rely on hand-selected features and lack interpretability. Improved predictive and explainable models are needed, especially for under-studied plant allergens. We present DeepPlantAllergy, a deep learning model that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and Multi-Head Self-Attention (MHSA) to capture both local patterns and long-range dependencies within protein sequences. We evaluated four embedding techniques-including one-hot encoding, SeqVec, ProtBert, and ESM-1B-and employed Integrated gradients to identify residues contributing to allergenicity. Predictive performance was similar for ESM-1B and ProtBert embeddings, with no statistically significant difference, with an F1 score of 93.9% and 93.6% and AUC of 97.74% and 97.8%, respectively. Motif extraction revealed complementary strengths: ProtBert highlighted regions similar to OneHot patterns, while ESM captured distinct segments, and SeqVec identified additional regions overlapping with experimentally validated epitopes. Notably, molecular docking confirmed the biological plausibility of a predicted epitope, supporting the utility of residue-level predictions. DeepPlantAllergy thus offers both high predictive accuracy and interpretable insights, facilitating the discovery of allergenic motifs in under-characterized plant proteins. The source code, datasets used for training and evaluation, trained models, and the full pipeline for prediction and motif identification are available at the GitHub Repository: https://github.com/Lilly-dh/DeepPlantAllergy.