In this talk I will present diffRBM, an approach
based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of protein-protein interactions underlying effective immune responses. In particular, the protein-protein interaction we focus on is the binding between protein fragments of viral origin (antigens) and the surface receptors of immune cells (T-cell receptors), which mediates the recognition by the immune system of ongoing infections. 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 diffRBM reaches performances that compare favorably to existing sequence-­based predictors of antigen-receptor binding specificity, and that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-­receptor structural complex.