ArticleFrontiers in immunology2023
Prediction of celiac disease associated epitopes and motifs in a protein.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 18 citations in OpenAlex.
- A novel clustered-based binary grey wolf optimizer to solve the feature selection problem for uncovering the genetic links between non-Hodgkin lymphomas and rheumatologic diseases.Health information science and systems · 2025Article
- Living Gluten-Free in Romania: A National Cross-Sectional Study of Dietary Adherence in Clinically Diagnosed and Self-Reported Cases.Nutrients · 2025Article
- Integrated fecal macrogenomic and metabolomic analyses reveal celiac disease flora and metabolic profiles associated with Chinese populations.Journal of translational medicine · 2025Article
- Article
- Classification of bioactive peptides: A systematic benchmark of models and encodings.Computational and structural biotechnology journal · 2024Article
- Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review.Biomimetics (Basel, Switzerland) · 2024Review
- The Role of Gluten in the Development of Autoimmune Thyroid Diseases: A Narrative Review.International journal of endocrinology and metabolism · 2024Review
- Advances in Understanding the Human Gut Microbiota and Its Implication in Pediatric Celiac Disease-A Narrative Review.Nutrients · 2023Review
- Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.Frontiers in microbiology · 2023Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Celiac disease (CD) is an autoimmune gastrointestinal disorder causes immune-mediated enteropathy against gluten. Gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2/DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease. There is a need to identify CD associated epitopes in protein-based foods and therapeutics. Methods: In this study, computational tools have been developed to predict CD associated epitopes and motifs. Dataset used for training, testing and evaluation contain experimentally validated CD associated and non-CD associate peptides. We perform positional analysis to identify the most significant position of an amino acid residue in the peptide and checked the frequency of HLA alleles. We also compute amino acid composition to develop machine learning based models. We also developed ensemble method that combines motif-based approach and machine learning based models. Results and Discussion: Our analysis support existing hypothesis that proline (P) and glutamine (Q) are highly abundant in CD associated peptides. A model based on density of P&Q in peptides has been developed for predicting CD associated peptides which achieve maximum AUROC 0.98 on independent data. We discovered motifs (e.g., QPF, QPQ, PYP) which occurs specifically in CD associated peptides. We also developed machine learning based models using peptide composition and achieved maximum AUROC 0.99. Finally, we developed ensemble method that combines motif-based approach and machine learning based models. The ensemble model-predict CD associated motifs with 100% accuracy on an independent dataset, not used for training. Finally, the best models and motifs has been integrated in a web server and standalone software package "CDpred". We hope this server anticipate the scientific community for the prediction, designing and scanning of CD associated peptides as well as CD associated motifs in a protein/peptide sequence (https://webs.iiitd.edu.in/raghava/cdpred/).
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