Epitope-conditioned generation of T-cell receptor β-chain CDR3 candidates using a pre-trained transformer model

Abstract

Prioritizing T-cell receptor (TCR) candidates for defined peptide-HLA targets is an important step in TCR-based immunotherapy development, but it still relies heavily on laborious and expensive experimental screening. Recent advancements in generative artificial intelligence have demonstrated promising power in protein design and engineering. In this regard, we propose a pre-trained transformer model, termed Epitope-Receptor-Transformer (ERTransformer), for the epitope-conditioned generation of candidate TCR β-chain CDR3 sequences. ERTransformer is built on EpitopeBERT and ReceptorBERT, which are trained using 1.9 million epitope sequences and 33.1 million TCR sequences, respectively. To demonstrate the model capability, we generate 1,000 candidate TCR β-chain CDR3 sequences for each of the five epitopes with known natural TCRs. The generated candidates show low sequence similarity to natural TCR β-chains while retaining plausible CDR3 length, amino-acid composition, and conservative substitution patterns. We further conduct wet-lab experiments using flow cytometry in defined TCR/pMHC contexts and find that the level of T cell activation induced by selected artificial TCRs is either comparable to or even surpasses that of natural ones. Our work suggests that ERTransformer can expand and prioritize candidate TCR β-chain CDR3 sequences for downstream experimental screening in defined peptide-HLA and TCR-chain contexts.