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

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 proper...

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Vydáno v:eLife Ročník 12
Hlavní autoři: Bravi, Barbara, Di Gioacchino, Andrea, Fernandez-de-Cossio-Diaz, Jorge, Walczak, Aleksandra M, Mora, Thierry, Cocco, Simona, Monasson, Rémi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cambridge eLife Sciences Publications Ltd 08.09.2023
eLife Sciences Publication
eLife Sciences Publications, Ltd
Témata:
Antigens
Cancer
Computational and Systems Biology
Datasets
Immune response
Immunogenicity
Life Sciences
Lymphocytes T
machine learning
Pathogens
Peptides
Prediction models
Receptor mechanisms
T cell receptors
Transfer learning
ISSN:2050-084X, 2050-084X
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Shrnutí: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.
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These authors contributed equally to this work.
These authors also contributed equally to this work.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.85126