T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning ( $$\mathop {\texttt{RL}}\limits $$ ) problem, and presented a framework $$\mathop {\texttt{TCRPPO}}\limits $$ with a mutation policy using proximal policy optimization. $$\mathop {\texttt{TCRPPO}}\limits $$ mutates TCRs into effective ones that can recognize given peptides. $$\mathop {\texttt{TCRPPO}}\limits $$ leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared $$\mathop {\texttt{TCRPPO}}\limits $$ with multiple baseline methods and demonstrated that $$\mathop {\texttt{TCRPPO}}\limits $$ significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of $$\mathop {\texttt{TCRPPO}}\limits $$ for both precision immunotherapy and peptide-recognizing TCR motif discovery.