Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions

View ORCID ProfileCeder Dens, View ORCID ProfileWout Bittremieux, View ORCID ProfileFabio Affaticati, View ORCID ProfileKris Laukens, View ORCID ProfilePieter Meysman
doi: https://doi.org/10.1101/2022.05.02.490264
Ceder Dens
1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ceder Dens
Wout Bittremieux
2Dorrestein Laboratory, University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences, 9500 Gilman Drive, La Jolla, CA 92093, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Wout Bittremieux
Fabio Affaticati
1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Fabio Affaticati
Kris Laukens
1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kris Laukens
Pieter Meysman
1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pieter Meysman
  • For correspondence: pieter.meysman@uantwerpen.be
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR–epitope pair is still a challenging task, especially for novel epitopes, because the underlying patterns are largely unknown to domain experts and machine learning models. To achieve a deeper understanding of TCR–epitope interactions, we have used interpretable deep learning techniques to gain insights into the performance of TCR–epitope binding machine learning models. We demonstrate how interpretable AI techniques can be linked to the three-dimensional structure of molecules to offer novel insights into the factors that determine TCR affinity on a molecular level. Additionally, our results show the importance of using interpretability techniques to verify the predictions of machine learning models for challenging molecular biology problems where small hard-to-detect problems can accumulate to inaccurate results.

Competing Interest Statement

KL and PM hold shares in ImmuneWatch BV, an immunoinformatics company.

Footnotes

  • ceder.dens{at}uantwerpen.be, wbittremieux{at}health.ucsd.edu, fabio.affaticati{at}uantwerpen.be, kris.laukens{at}uantwerpen.be,

  • Comparison of the feature attributions and residue proximity is now done with the Pearson Correlation Coefficient. Al plots are updated accordingly.

  • https://doi.org/10.5281/zenodo.6500495

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted October 04, 2022.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions
Ceder Dens, Wout Bittremieux, Fabio Affaticati, Kris Laukens, Pieter Meysman
bioRxiv 2022.05.02.490264; doi: https://doi.org/10.1101/2022.05.02.490264
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions
Ceder Dens, Wout Bittremieux, Fabio Affaticati, Kris Laukens, Pieter Meysman
bioRxiv 2022.05.02.490264; doi: https://doi.org/10.1101/2022.05.02.490264

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4235)
  • Biochemistry (9136)
  • Bioengineering (6784)
  • Bioinformatics (24001)
  • Biophysics (12129)
  • Cancer Biology (9534)
  • Cell Biology (13778)
  • Clinical Trials (138)
  • Developmental Biology (7636)
  • Ecology (11702)
  • Epidemiology (2066)
  • Evolutionary Biology (15513)
  • Genetics (10644)
  • Genomics (14326)
  • Immunology (9483)
  • Microbiology (22839)
  • Molecular Biology (9090)
  • Neuroscience (48995)
  • Paleontology (355)
  • Pathology (1482)
  • Pharmacology and Toxicology (2570)
  • Physiology (3846)
  • Plant Biology (8331)
  • Scientific Communication and Education (1471)
  • Synthetic Biology (2296)
  • Systems Biology (6192)
  • Zoology (1301)