A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity
- Imperial College London, United Kingdom
- École Normale Supérieure - PSL, France
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
Andrea Di Gioacchino
Jorge Fernandez-de-Cossio-Diaz
Aleksandra M Walczak
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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A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity
eLife 12:e85126.
https://doi.org/10.7554/eLife.85126
