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T cells monitor the health status of cells by identifying foreign peptidesdisplayed on their surface. T-cell receptors (TCRs), which are proteincomplexes 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 immuneresponse. Optimizing TCR sequences for TCR recognition represents a fundamentalstep towards the development of personalized treatments to trigger immuneresponses killing cancerous or virus-infected cells. In this paper, weformulated the search for these optimized TCRs as a reinforcement learning (RL)problem, and presented a framework TCRPPO with a mutation policy using proximalpolicy optimization. TCRPPO mutates TCRs into effective ones that can recognizegiven peptides. TCRPPO leverages a reward function that combines thelikelihoods of mutated sequences being valid TCRs measured by a new scoringfunction based on deep autoencoders, with the probabilities of mutatedsequences recognizing peptides from a peptide-TCR interaction predictor. Wecompared TCRPPO with multiple baseline methods and demonstrated that TCRPPOsignificantly outperforms all the baseline methods to generate positive bindingand valid TCRs. These results demonstrate the potential of TCRPPO for bothprecision immunotherapy and peptide-recognizing TCR motif discovery.

T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy