Relation-Aware Graph-Hypergraph Networks for Antibody Sequence-Structure Co-Design
Abstract
Motivation: The design of antibody complementarity-determining regions (CDRs) is pivotal for therapeutic development, yet remains challenged by the complexity of the antibody-antigen interface. Existing methods, which largely rely on pairwise relationships, struggle to capture intrinsic multi-body interactions and global structural constraints. Results: We propose the Relation-Aware Graph-Hypergraph Network (RA-GHN), a framework that unifies graph-based pairwise modeling with biophysical hypergraphs to capture high-order cooperative effects. Its heterogeneous hypergraph uses epitope-paratope interaction patches, spatial structural clusters, and high-degree hub structures. A dual-pathway architecture integrates a Relation-Aware Equivariant Graph Network with an Equivariant Hypergraph Encoder while preserving SE(3)-equivariance. We also introduce the Structure-Sequence Decoupling Index, which balances structural reliability with sequence novelty. Experiments show that RA-GHN outperforms state-of-the-art baselines across standard benchmarks and affinity optimization tasks.
Related
- A common structure in recurrent networks supports neural sequence generation locally and in downstream neurons
- HR-FRGS: A Novel Biomarker Discovery Protocol using Dual Layer Hypergraph Learning for NGS RNA Sequence Data
- An interpretable open platform for sequence-based antibody developability prediction
- Sequence-only antigen context improves modeling of antibody hypervariable regions
- Dynamical consequences of context-dependent mutation: visualizing antibody sequence evolution during somatic hypermutation
BibTeX
@misc{wei2026relationaware,
title = {{Relation-Aware Graph-Hypergraph Networks for Antibody Sequence-Structure Co-Design}},
author = {J. Wei and S. Zhang and H. Duan and L. Wei and J. GUO},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758263},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758263v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Relation-Aware Graph-Hypergraph Networks for Antibody Sequence-Structure Co-Design
AU - J. Wei
AU - S. Zhang
AU - H. Duan
AU - L. Wei
AU - J. GUO
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758263
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758263v1
AB - Motivation: The design of antibody complementarity-determining regions (CDRs) is pivotal for therapeutic development, yet remains challenged by the complexity of the antibody-antigen interface. Existing methods, which largely rely on pairwise relationships, struggle to capture intrinsic multi-body interactions and global structural constraints. Results: We propose the Relation-Aware Graph-Hypergraph Network (RA-GHN), a framework that unifies graph-based pairwise modeling with biophysical hypergraphs to capture high-order cooperative effects. Its heterogeneous hypergraph uses epitope-paratope interaction patches, spatial structural clusters, and high-degree hub structures. A dual-pathway architecture integrates a Relation-Aware Equivariant Graph Network with an Equivariant Hypergraph Encoder while preserving SE(3)-equivariance. We also introduce the Structure-Sequence Decoupling Index, which balances structural reliability with sequence novelty. Experiments show that RA-GHN outperforms state-of-the-art baselines across standard benchmarks and affinity optimization tasks.
ER -