Local-Frame 3D Rotary Position Encoding (LF3DRoPE), which expresses inter-residue displacements in backbone-defined local frames and injects them directly into rotary attention, preserves continuous directional geometry while ensuring invariance to global $\mathrm{SE}(3)$ transformations.
Abstract
Antibody-specific epitope prediction aims to identify which antigen residues are recognized by a given antibody, a task that depends on the three-dimensional complementarity between antibody CDRs and the antigen surface. Existing methods usually leverage PLM embeddings and inject structure through additional graph, surface, or point-cloud encoders, where the positional mechanism inside attention remains largely tied to one-dimensional sequence order. For proteins, the analogue of a token offset is not only sequence separation, but also the three-dimensional displacement between residues after folding. This raises a question, can folded residue geometry serve as the positional mechanism of attention itself? We propose Local-Frame 3D Rotary Position Encoding (LF3DRoPE), which expresses inter-residue displacements in backbone-defined local frames and injects them directly into rotary attention. This design preserves continuous directional geometry while ensuring invariance to global $\mathrm{SE}(3)$ transformations. On the AsEP benchmark, LF3DRoPE achieves state-of-the-art $\mathrm{MCC}$ on both ratio and epitope-group splits. Ablations and rigid transformation tests show that local three-dimensional geometry provides information beyond sequence-order attention while preserving invariance to arbitrary global coordinate systems. Mutation ranking results further indicate that LF3DRoPE captures antigen-specific structural compatibility.
Antibody complementarity-determining regions (CDRs), especially CDR-H3, are a dominant source of binding specificity but remain difficult to design due to coupled sequence-structure constraints and local geometric variability. Here we present Kukulu, a structure-aware Joint Embedding Predictive Architecture (JEPA) combined with conditional diffusion for CDR loop reconstruction in antibody-antigen complexes. Our pipeline prepares structures by chain-aware cleanup, Fv trimming, Chothia-indexed CDR identification, and in silico CDR masking, then trains on paired prepared/masked structures represented in an atom37 format. The model uses a context encoder over masked structures, a transformer predictor for latent CDR representations, and a diffusion head that reconstructs loop coordinates, atom presence, and residue identities under geometry-aware losses. During generation, Kukulu denoises only masked CDR residues while preserving frame-work context, then optionally rebuilds sidechains with local frame templates and performs post-generation structural relaxation. This manuscript provides a methods-focused overview of the model’s implementation details and an evaluation protocol based on structure quality and docking-oriented scoring for integration into existing antibody design workflows.
Seth Rabinowitz, Prbhuv Nigam, Nicholas Santolla et al.· bioRxiv· 0 citations
Mutation-induced changes in binding free energy (ΔΔG) at antibody–antigen interfaces are important for antibody optimization, mutational scanning, and viral immune escape assessment. However, computational prediction remains challenging because antibodies and antigens have distinct sequence backgrounds, mutation effects are often localized at interfaces, and related complexes may remain across training and evaluation partitions. We present AbAgMut-GNN as a task-oriented paired graph framework that coordinates established sequence and geometric learning components around explicit comparison of wild-type (WT) and mutant (MUT) antibody–antigen complexes. AntiBERTy and ESM2 provide frozen residue-level embeddings for antibody and antigen chains, respectively, while mutation-centered, interface-aware, paired-residue, and contact-delta representations capture local perturbations and interaction remodeling. We evaluate AbAgMut-GNN under four complementary internal settings, including the PDB-based split, the complex-cluster split, the antibody-family preserving validation split, and the antigen-cluster-preserving validation split. Under the complex-cluster split, AbAgMut-GNN achieves Pearson correlation coefficients of 0.5841 on AB-Bind and 0.5480 on SKEMPI v2.0. External validation on SARS-CoV-2 and influenza antibody–antigen systems further shows useful mutation-effect correlation trends, although absolute-error performance varies across target systems. Contact-masking and residue-class enrichment analyses indicate that model-derived importance patterns are associated with biologically relevant interface interactions. Overall, AbAgMut-GNN is best viewed as a task-oriented computational tool for trend-level mutation ranking and pre-experimental candidate prioritization rather than as a high-precision substitute for quantitative biophysical measurement.
Wenchi Ge, Qi-Jia Yu, Jincen Shuai et al.· Journal of Chemical Informat...· 0 citations
Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative models have demonstrated broad capabilities in biomolecular design, yet post-training strategies for downstream objectives remain limited. Standard denoising training operates on noisy states obtained by perturbing native structures, whereas recursive generation proceeds through model-generated intermediate states. For flexible antibody CDR loops such as CDR-H3, this mismatch can allow backbone deviations to accumulate along the denoising trajectory and compromise antigen-facing loop geometry. We introduce ABOPD, an antibody design framework based on on-policy distillation that leverages privileged native geometry during training to supervise states visited along the model's own denoising trajectories. With this fine-grained structural supervision, ABOPD substantially improves structural recovery on RAbD CDR-H3 generation, reducing RMSD by 0.42 {\AA} (from 2.37 {\AA} to 1.95 {\AA}) and outperforming supervised fine-tuning and offline distillation controls, offering a path to higher-fidelity protein design.
Zhuo Yang, Jiaying He, Jiaqing Xie et al.· 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
This work designs a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes in antibody screening and drug residence time analysis.
G. Luo, Junkai Wang, Sizhe Zhang et al.· Bioinformatics· 0 citations
AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context, achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering.
Xiaoliang Shi, Zichen Wang, Runze Ma et al.· 0 citations