{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T23:07:52Z","timestamp":1783984072342,"version":"3.55.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2014including one-hot encoding, SeqVec, ProtBert, and ESM-1B\u2014and 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.<\/jats:p>","DOI":"10.1093\/bib\/bbaf605","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T14:20:19Z","timestamp":1763130019000},"source":"Crossref","is-referenced-by-count":4,"title":["DeepPlantAllergy: deep learning for explainable prediction of allergenicity in plant proteins"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0800-130X","authenticated-orcid":false,"given":"Wala","family":"Dhouib","sequence":"first","affiliation":[{"name":"Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax , Sidi Mansour Road Km 6, P.O. Box 1177, 3018, Sfax, \u00a0","place":["Tunisia"]},{"name":"National School of Engineers of Sfax, University of Sfax , Soukra Road Km 3, 3038, Sfax, \u00a0","place":["Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fakher","family":"Frikha","sequence":"additional","affiliation":[{"name":"Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax , Sidi Mansour Road Km 6, P.O. Box 1177, 3018, Sfax, \u00a0","place":["Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Rebai","sequence":"additional","affiliation":[{"name":"Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax , Sidi Mansour Road Km 6, P.O. Box 1177, 3018, Sfax, \u00a0","place":["Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najla","family":"Kharrat","sequence":"additional","affiliation":[{"name":"Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax , Sidi Mansour Road Km 6, P.O. Box 1177, 3018, Sfax, \u00a0","place":["Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"2025111409201221900_ref1","first-page":"821","article-title":"A classification of plant food allergens","volume":"113","author":"Breiteneder","year":"2004","journal-title":"J Allergy Clin Immunol"},{"key":"2025111409201221900_ref2","first-page":"1","volume-title":"Joint FAO\/WHO Expert Consultation on Allergenicity of Foods Derived from Biotechnology","author":"FAO, WHO","year":"2001"},{"key":"2025111409201221900_ref3","doi-asserted-by":"publisher","first-page":"100813","DOI":"10.1016\/j.waojou.2023.100813","article-title":"Prevalence of self-reported food allergy in Tunisia: general trends and probabilistic modeling","volume":"16","author":"Belmabrouk","year":"2023","journal-title":"World Allergy Organ 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