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.
Yang Xiao, Yu Zhao, Fandi Wu et al.· Proceedings of the 32nd ACM...· 0 citations
Accurate immune receptor design requires modeling the coupled variation of aminoacid sequence, full-atom conformation, and target-binding geometry across antibodies, nanobodies, and T-cell receptors (TCRs). Existing methods often address only part of this problem, either by separating structure generation from sequence design, relying on fixed-backbone inverse folding, or focusing on a single receptor class. We introduce IgGM2, a unified all-atom generative framework for immune receptor structure prediction and CDR sequence–structure co-design. IgGM2 follows a structure-to-design strategy: it first learns how immune receptors are positioned around fixed target structures, and then transfers this target-conditioned structural prior to CDR design. Unlike modular design pipelines, IgGM2 jointly generates CDR residue identities and full-atom receptor structures, allowing frame-work geometry to adapt to designed CDRs without separate inverse folding or external sidechain packing. Unlike continuous residue encodings based on virtualatom geometry, IgGM2 keeps sequence prediction explicit while using atom14 placeholders only for full-atom representation. On structure prediction benchmarks, IgGM2 better captures receptor–target spatial relationships than AlphaFold3 on FoldBench and achieves strong performance on TCR–pMHC modeling. On sequence design benchmarks, IgGM2 achieves competitive amino-acid recovery and improves Rosetta-based interface preference metrics, suggesting more favorable generated binding interfaces. These results support IgGM2 as a unified all-atom framework for adaptive immune receptor structure prediction and design.
Jian Ma, Fandi Wu, Lin Yao et al.· bioRxiv· 0 citations
The AIntibody challenge shows that AI can optimize antibodies in defined, biologically grounded regimes, in addition to highlighting critical gaps including affinity prediction and library-inspired antibody design and cross-task generalization.
M. Erasmus, Daniel Bedinger, Elizabeth Hopkins et al.· Nature Biotechnology· 0 citations
Branch-JEPA is introduced, which replaces this point-valued transition with a context-weighted finite set of latent successors, and preserves more distinct futures, while full-set scoring improves the quality of the resulting predictive distribution.
Zhi Song, Ximing Xing, Zhenchao Tang et al.· 0 citations