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Prediction of celiac disease associated epitopes and motifs in a protein

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/ant...

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Autores principales: Tomer, Ritu, Patiyal, Sumeet, Dhall, Anjali, Raghava, Gajendra P. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893285/
https://www.ncbi.nlm.nih.gov/pubmed/36742312
http://dx.doi.org/10.3389/fimmu.2023.1056101
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author Tomer, Ritu
Patiyal, Sumeet
Dhall, Anjali
Raghava, Gajendra P. S.
author_facet Tomer, Ritu
Patiyal, Sumeet
Dhall, Anjali
Raghava, Gajendra P. S.
author_sort Tomer, Ritu
collection PubMed
description 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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spelling pubmed-98932852023-02-03 Prediction of celiac disease associated epitopes and motifs in a protein Tomer, Ritu Patiyal, Sumeet Dhall, Anjali Raghava, Gajendra P. S. Front Immunol Immunology 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/). Frontiers Media S.A. 2023-01-19 /pmc/articles/PMC9893285/ /pubmed/36742312 http://dx.doi.org/10.3389/fimmu.2023.1056101 Text en Copyright © 2023 Tomer, Patiyal, Dhall and Raghava https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Tomer, Ritu
Patiyal, Sumeet
Dhall, Anjali
Raghava, Gajendra P. S.
Prediction of celiac disease associated epitopes and motifs in a protein
title Prediction of celiac disease associated epitopes and motifs in a protein
title_full Prediction of celiac disease associated epitopes and motifs in a protein
title_fullStr Prediction of celiac disease associated epitopes and motifs in a protein
title_full_unstemmed Prediction of celiac disease associated epitopes and motifs in a protein
title_short Prediction of celiac disease associated epitopes and motifs in a protein
title_sort prediction of celiac disease associated epitopes and motifs in a protein
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893285/
https://www.ncbi.nlm.nih.gov/pubmed/36742312
http://dx.doi.org/10.3389/fimmu.2023.1056101
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