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

SUBMITTER: Dhouib W 

PROVIDER: S-EPMC12616846 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature