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bioRxiv · 10.1101/2022.12.06.519259

Learning the differences: a transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Bravi, B.·Di Gioacchino, A.·Fernandez-de-Cossio-Diaz, J.·Walczak, A. M.·Mora, T.·Cocco, S.·Monasson, R.
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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, one the one hand, underlie the antigens probability of triggering a response, and on the other hand the T-cell receptors 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. More broadly, diffRBM provides a general framework to detect, interpret and leverage selected features in biological data.

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Bravi, B., Di Gioacchino, A., Fernandez-de-Cossio-Diaz, J., Walczak, A. M., Mora, T., Cocco, S., Monasson, R.. 2022-12-09. Learning the differences: a transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. https://doi.org/10.1101/2022.12.06.519259

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