TDLM: A Diffusion Language Model for TCR Sequence Exploration and Generation
Haoyan Wang, Yuhang Yang, Yifei Huang, Tianyi Zang, Yadong Liu · 2024
The adaptive immune response relies on the ability of T-cell receptors (TCRs) to recognize specific antigens. The vast diversity of TCRs allows T-cells to recognize a broad spectrum of antigens, but this complexity also poses challenges for understanding and predicting TCR-antigen binding specificity. Despite the development of various machine learning and deep learning methods for prediction and clustering, there remains a need for a versatile and effective TCR language framework that can be flexibly applied to various downstream tasks, including sequence generation. Here we present TDLM, a T-cell Receptor (TCR) diffusion language model, designed to decode complex patterns within TCR sequences and apply them across various downstream tasks. Firstly, TDLM can be trained on unlabeled TCR sequence data, enabling it to utilize vast datasets to generate comprehensive embeddings. When compared to other embedding methods, TDLM embeddings enhance TCR-antigen binding prediction accuracy and enable effective TCR sequence clustering and similarity analysis, helping identify TCRs with shared antigen specificity. Furthermore, as a diffusion-based generative model, TDLM can generate highly diverse and specific TCR sequences. This ability is invaluable for the rapid screening and optimization of TCRs with target antigen specificities, offering significant potential in disease diagnosis, personalized immunotherapy, and vaccine research. The code is available at: https://github.com/skybluewhy/TDLM