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.