Evidence map›Paper›PMID 36742312›Full record

ArticleFrontiers in immunology2023

Prediction of celiac disease associated epitopes and motifs in a protein.

Ritu Tomer, Sumeet Patiyal, Anjali Dhall, Gajendra P S Raghava

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
5.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Frontiers in immunology · 2025
    Article
  5. Classification of bioactive peptides: A systematic benchmark of models and encodings.Computational and structural biotechnology journal · 2024
    Article
  6. Review
  7. The Role of Gluten in the Development of Autoimmune Thyroid Diseases: A Narrative Review.International journal of endocrinology and metabolism · 2024
    Review
  8. Review
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Ritu TomerDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Sumeet PatiyalDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Anjali DhallDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Gajendra P S RaghavaDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Indraprastha Institute of Information Technology Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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/).

Indexed as

Celiac DiseaseAmino AcidsEpitopesGlutensHumansPeptidesAmino AcidsEpitopesGlutensPeptidesceliac diseaseensemble methodgluten immunogenic peptidesHLA-DQ2/DQ8motif

Identifiers

PMID36742312
PMCPMC9893285
OpenAlexW4317423648

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.