  T-cell receptors (TCRs) play a crucial role in the immune system by
recognizing and binding to specific antigens presented by infected or cancerous
cells. Understanding the sequence patterns of TCRs is essential for developing
targeted immune therapies and designing effective vaccines. Language models,
such as auto-regressive transformers, offer a powerful solution to this problem
by learning the probability distributions of TCR repertoires, enabling the
generation of new TCR sequences that inherit the underlying patterns of the
repertoire. We introduce TCR-GPT, a probabilistic model built on a decoder-only
transformer architecture, designed to uncover and replicate sequence patterns
in TCR repertoires. TCR-GPT demonstrates an accuracy of 0.953 in inferring
sequence probability distributions measured by Pearson correlation coefficient.
Furthermore, by leveraging Reinforcement Learning(RL), we adapted the
distribution of TCR sequences to generate TCRs capable of recognizing specific
peptides, offering significant potential for advancing targeted immune
therapies and vaccine development. With the efficacy of RL, fine-tuned
pretrained TCR-GPT models demonstrated the ability to produce TCR repertoires
likely to bind specific peptides, illustrating RL's efficiency in enhancing the
model's adaptability to the probability distributions of biologically relevant
TCR sequences.
