ArticleBioinformatics (Oxford, England)2019
AllerCatPro-prediction of protein allergenicity potential from the protein sequence.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed.
- Deciphering Allergen Peptides for Dermatological and Cosmetic Applications with Explainable Artificial Intelligence.Journal of proteome research · 2026Article
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- NToxSEM: Enhancing prediction of neurotoxic peptides and neurotoxins using a stacked ensemble-based multimodal framework.Protein science : a publication of the Protein Society · 2026Article
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- iALP: Identification of Allergenic Proteins Based on Large Language Model and Gate Linear Unit.Interdisciplinary sciences, computational life sciences · 2025Article
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- The anti-aging potential of antihypertensive peptides ofFrontiers in aging · 2025Article
- A Reverse Vaccinology and Immunoinformatic Approach for the Designing of a Novel mRNA Vaccine Against Stomach Cancer Targeting the Potent Pathogenic Proteins ofBioinformatics and biology insights · 2025Article
- Molecular Analysis and Bioinformatics Assessment of Full-Length L1 Gene of Bovine Papillomavirus Type-1 as a Potential DNA Vaccine Study.Veterinary medicine international · 2025Article
- AnCurrent computer-aided drug design · 2025Article
- Chickpea Proteome Analysis Reveals Genotype-Dependent Variations Associated with Seed Traits.Journal of agricultural and food chemistry · 2024Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- De Novo Transcriptomic Analyses to Identify and Compare Allergens in Foods.Methods in molecular biology (Clifton, N.J.) · 2024Article
14 more citing papers are in PubMed but not listed here.
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
motivationDue to the risk of inducing an immediate Type I (IgE-mediated) allergic response, proteins intended for use in consumer products must be investigated for their allergenic potential before introduction into the marketplace. The FAO/WHO guidelines for computational assessment of allergenic potential of proteins based on short peptide hits and linear sequence window identity thresholds misclassify many proteins as allergens.
resultsWe developed AllerCatPro which predicts the allergenic potential of proteins based on similarity of their 3D protein structure as well as their amino acid sequence compared with a data set of known protein allergens comprising of 4180 unique allergenic protein sequences derived from the union of the major databases Food Allergy Research and Resource Program, Comprehensive Protein Allergen Resource, WHO/International Union of Immunological Societies, UniProtKB and Allergome. We extended the hexamer hit rule by removing peptides with high probability of random occurrence measured by sequence entropy as well as requiring 3 or more hexamer hits consistent with natural linear epitope patterns in known allergens. This is complemented with a Gluten-like repeat pattern detection. We also switched from a linear sequence window similarity to a B-cell epitope-like 3D surface similarity window which became possible through extensive 3D structure modeling covering the majority (74%) of allergens. In case no structure similarity is found, the decision workflow reverts to the old linear sequence window rule. The overall accuracy of AllerCatPro is 84% compared with other current methods which range from 51 to 73%. Both the FAO/WHO rules and AllerCatPro achieve highest sensitivity but AllerCatPro provides a 37-fold increase in specificity. AVAILABILITY AND IMPLEMENTATION: https://allercatpro.bii.a-star.edu.sg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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