Yang Xiao

A closed-loop reinforcement learning framework for rapid compound directed optimization

TCRTSdesign: End-to-End Co-Design of Antigen-Specific TCR Sequences and Structures

Designing functional T cell receptors (TCRs) for a given peptide presented by MHC (pMHC) is an emerging yet highly challenging problem in computational immunology. While recent approaches have achieved initial progress, they face two major limitations: (1) the lack of structural information from TCR–pMHC complexes in the design process, and (2) the restricted generalization ability of current sequence–structure co-design models, which rely only on paired sequence–structure data and fail to leverage the vast amount of available sequence-pairing information. To address these challenges, we introduce TCRTSdesign, a framework that concurrently generates novel TCR sequences with specific binding capabilities to target pMHC molecules and predicts the full-atom structures of the TCR-pMHC complex, while optimizing their binding affinity. Our method integrates large-scale paired sequence data for pretraining a sequence generation model, and further refines the design through a structure-aware student model guided by the teacher via knowledge distillation. Extensive experiments demonstrate that TCRTSdesign significantly outperforms existing baselines in both sequence recovery and structural fidelity, offering a promising computational method for TCR engineering.