TCRgener

TCRgener is a computational framework for the design and exploration of T-cell receptor sequences. It uses generative machine learning methods to create sequences that may recognize defined antigens when paired with appropriate MHC molecules. The aim is to support research in adaptive immunity and immunotherapy by enabling rapid generation and screening of candidate TCRs.

Core components include a generative model trained on curated repositories of TCR sequences, including information on V(D)J gene segments and CDR3 regions, plus optional conditioning on target antigen features or HLA restriction. The system can generate new receptor sequences conditioned on specified criteria and can score them for predicted binding, structural plausibility, or expression potential.

Typical workflow involves assembling training data from public TCR datasets, training a model, generating candidate sequences for a given target, and applying in silico filters before experimental validation. TCRgener is designed to integrate with existing bioinformatics tools for TCR annotation and immunogenicity prediction.

Applications span exploratory studies of receptor diversity, prioritization of candidate TCRs for experimental testing, and hypotheses about motif usage in antigen recognition. The approach remains subject to uncertainties in binding prediction, potential off-target reactivity, and safety considerations.

As with other generative designs in biology, TCRgener reflects a research-stage approach that requires careful validation and oversight. Researchers emphasize transparent reporting, benchmarking against known receptor-antigen pairs, and consideration of ethical and regulatory implications.