T-cell receptor optimization with reinforcement learning and mutation policies for precision immunotherapy

A non-transitory computer-readable storage medium comprising a computer-readable program for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:

• extracting peptides to identify a virus or tumor cells;
• collecting a library of TCRs from target patients;
• predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients;
• developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, wherein the TCRs and the extracted peptides are encoded in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies by mutating one amino acid of the TCRs at a step;
• defining reward functions based on a reconstruction-based score and a density estimation-based score;
• randomly sampling batches of TCRs and following a policy network to mutate the TCRs;
• outputting mutated TCRs;
• and
• ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.

A system for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy, the system comprising:

• a memory;
• and
• one or more processors in communication with the memory configured to:
• extract peptides to identify a virus or tumor cells;
• collect a library of TCRs from target patients;
• predict, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients;
• develop a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, wherein the TCRs and the extracted peptides are encoded in a distributed embedding space, and a mapping is learnt between the embedding space and the TCR mutation policies by mutating one amino acid of the TCRs at a step;
• define reward functions based on a reconstruction-based score and a density estimation-based score;
• randomly sample batches of TCRs and following a policy network to mutate the TCRs;
• output mutated TCRs;
• and
• rank the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.