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Article Dans Une Revue eLife Année : 2023

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

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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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hal-04252230 , version 1 (04-04-2024)

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Barbara Bravi, Andrea Di Gioacchino, Jorge Fernandez-De-Cossio-Diaz, Aleksandra Walczak, Thierry Mora, et al.. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife, 2023, 12, pp.e.85126. ⟨10.7554/eLife.85126⟩. ⟨hal-04252230⟩
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