1. Computational and Systems Biology

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

  1. Barbara Bravi  Is a corresponding author
  2. Andrea Di Gioacchino
  3. Jorge Fernandez-de-Cossio-Diaz
  4. Aleksandra M Walczak
  5. Thierry Mora
  6. Simona Cocco
  7. Rémi Monasson
  1. Imperial College London, United Kingdom
  2. École Normale Supérieure - PSL, France
https://doi.org/10.7554/eLife.85126
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Cite this article
  1. Barbara Bravi
  2. Andrea Di Gioacchino
  3. Jorge Fernandez-de-Cossio-Diaz
  4. Aleksandra M Walczak
  5. Thierry Mora
  6. Simona Cocco
  7. Rémi Monasson
(2023)
A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity
eLife 12:e85126.
https://doi.org/10.7554/eLife.85126
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Abstract

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. The data used are downloaded from public databases. The pre-processed data, the results of the analysis, the codes to train and evaluate the models as well as the trained models are all available at the github page https://github.com/bravib/diffRBM_immunogenicity_TCRspecificity.

Article and author information

Author details

  1. Barbara Bravi

    Department of Mathematics, Imperial College London, London, United Kingdom
    For correspondence
    b.bravi21@imperial.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4860-7584

  2. Andrea Di Gioacchino

    Laboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6085-7589

  3. Jorge Fernandez-de-Cossio-Diaz

    Laboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4476-805X

  4. Aleksandra M Walczak

    Laboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    Aleksandra M Walczak, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2686-5702

  5. Thierry Mora

    Laboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5456-9361

  6. Simona Cocco

    Laboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1852-7789

  7. Rémi Monasson

    Laboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4459-0204

Funding

Agence Nationale de la Recherche (RBMPro CE30-0021-01)

  • Andrea Di Gioacchino
  • Jorge Fernandez-de-Cossio-Diaz

European Research Council (COG 724208)

  • Jorge Fernandez-de-Cossio-Diaz
  • Aleksandra M Walczak

HORIZON EUROPE Marie Sklodowska-Curie Actions (101026293)

  • Andrea Di Gioacchino

Agence Nationale de la Recherche (RBMPro CE30-0021-01)

  • Simona Cocco
  • Rémi Monasson

Agence Nationale de la Recherche (Prodigen)

  • Andrea Di Gioacchino
  • Jorge Fernandez-de-Cossio-Diaz

Agence Nationale de la Recherche (Prodigen)

  • Simona Cocco
  • Rémi Monasson

Agence Nationale de la Recherche (Decrypted CE30-0021-01)

  • Andrea Di Gioacchino
  • Jorge Fernandez-de-Cossio-Diaz

Agence Nationale de la Recherche (Decrypted CE30-0021-01)

  • Simona Cocco
  • Rémi Monasson

Agence Nationale de la Recherche (RESP-REP CE45-0018)

  • Aleksandra M Walczak
  • Thierry Mora

Agence Nationale de la Recherche (RESP-REP CE45-0018)

  • Barbara Bravi

European Research Council (COG 724208)

  • Barbara Bravi

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, Bravi et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Barbara Bravi
  2. Andrea Di Gioacchino
  3. Jorge Fernandez-de-Cossio-Diaz
  4. Aleksandra M Walczak
  5. Thierry Mora
  6. Simona Cocco
  7. Rémi Monasson
(2023)
A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity
eLife 12:e85126.
https://doi.org/10.7554/eLife.85126

Share this article

https://doi.org/10.7554/eLife.85126

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