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Tianxu Lv

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Book Open access Aug 2026

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

This work introduces 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.

Yang Xiao, Yu Zhao, Fandi Wu et al. · 0 citations
Book Open access Aug 2026

PRIME: A Pretrained Representation-Induced Model for 3D Molecules in De Novo Binder Design

Biomolecular binder design for peptides and antibodies requires generating diverse candidates that satisfy stringent three-dimensional geometric constraints while enabling affinity-oriented exploration under strong structural priors. In current generative models, the effective search space for structurally feasible binders is severely constrained, as the complexity of biochemical interactions is not explicitly encoded into a semantically grounded representation of viable molecular manifolds. To address this challenge, we propose Pretrained Representation Induced Molecular gEneration (PRIME), a unified generative framework for three-dimensional binder design across peptides and antibodies. PRIME grounds stochastic generation on frozen large-scale pretrained structural representations, inheriting robust physical priors to ensure structural feasibility without training a manifold from scratch. However, defining a feasible space alone is insufficient for effective exploration. Under commonly used isotropic perturbations, chain topology is ignored, allowing local noise to propagate into global structural distortions. To enable controlled exploration within the feasible space, we introduce Semantics-Preserving Exploratory Sampling (SPES), which integrates Graph Laplacian Spectral Noise to respect chain connectivity and Conditional Freedom Modulation to dynamically balance exploration with fidelity. By aligning stochastic exploration with structural semantics, PRIME enables diversity-enhanced generation without sacrificing geometric validity under the reported structural metrics, improving the empirical exploration--fidelity trade-off. PRIME achieves state-of-the-art performance on unified peptide and antibody benchmarks, effectively reconciling geometric validity with functional optimization under computational proxy metrics. The source code is available at https://github.com/simplaj/PRIME.

Zhihua Tian, Jiale Zhou, Rubo Wang et al. · 0 citations
Book Open access Aug 2026

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.

Yang Xiao, Yu Zhao, Fandi Wu et al. · 0 citations
Preprint Jul 2026

Branch-JEPA: Finite-Support Predictive Distributions for JEPA World Models

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
Book Open access Aug 2026

PRIME: A Pretrained Representation-Induced Model for 3D Molecules in De Novo Binder Design

Semantics-Preserving Exploratory Sampling (SPES), which integrates Graph Laplacian Spectral Noise to respect chain connectivity and Conditional Freedom Modulation to dynamically balance exploration with fidelity, enables diversity-enhanced generation without sacrificing geometric validity under the reported structural metrics, improving the empirical exploration--fidelity trade-off.

Zhihua Tian, Jiale Zhou, Rubo Wang et al. · 0 citations

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