Data from: Deep peptide recognition profiling decodes TCR specificity and enables disease-associated antigen discovery

Predicting T cell receptor (TCR) specificity based on sequence is
challenging because TCRs of similar sequence can recognize entirely
different antigens, whereas TCRs of different sequence can recognize the
same antigens. Here, we present a system that integrates high-throughput
yeast display with fine-tuned protein language models (pLMs) to generate
deep Peptide Recognition Profiles (PRPs) for individual TCRs, each
detailing binding against millions of peptides. We provide detailed PRPs
for a panel of HLA-B*27:05-restricted TCRs from patients with ankylosing
spondylitis and acute anterior uveitis that almost exclusively recognize
peptides through CDR3β. pLMs trained on these PRPs outperform AlphaFold3
and tFold-TCR in predicting T cell activation. We discover and validate
novel candidate autoantigens, demonstrate that model generalization to new
TCRs correlates with functional distance (PRP divergence) rather than
sequence similarity, and introduce a model-intrinsic uncertainty metric to
quantify prediction confidence. This system and its associated PRP
datasets offer a scalable approach to mapping TCR recognition,
accelerating antigen discovery, and guiding TCR engineering.