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 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. © 2023, Bravi et al.

Citation

Barbara Bravi, Andrea Di Gioacchino, Jorge Fernandez-de-Cossio-Diaz, Aleksandra M Walczak, Thierry Mora, Simona Cocco, Rémi Monasson. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife. 2023 Sep 08;12

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PMID: 37681658

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