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

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Bravi, Barbara | Di Gioacchino, Andrea | Fernandez-De-Cossio-Diaz, Jorge | Walczak, Aleksandra | Mora, Thierry | Cocco, Simona | Monasson, Rémi

Edité par CCSD ; eLife Sciences Publication -

International audience. 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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