Antigen-antibody binding is a critical process in the immune response. Although recent progress has advanced antibody design, current methods lack a generative framework for end-to-end modeling of full-atom antibody structures and struggle to fully exploit antigen-specific geometric information for optimizing local binding interfaces and global structures. To overcome these limitations, we introduce AbFlow, a paratope-restricted one-step flow-matching framework for designing full-atom antibodies end-to-end. AbFlow incorporates an extended velocity field network featuring an equivariant Surface Multi-channel Encoder, which uses surface-level antigen interaction data to refine the antibody structure, particularly the CDR-H3 region. Extensive experiments in paratope-centric antibody design, multi-CDRs and full-atom antibody design, binding affinity optimization, and complex structure prediction show that AbFlow produces superior antigen-antibody complexes, especially at the contact interface, and markedly improves the binding affinity of generated antibodies.

Residue-residue interactions between individual subunits of protein complexes are critical for predicting complex structures and can serve as distance constraints to guide complex structure modeling. Some recent studies have made some progress in predicting protein inter-chain contact maps based on multiple sequence alignments and deep learning models. Here we develop a new model based on graph attention network and triangular multiplication update to predict interchain contact maps for homologous protein complexes, named PGT (P is Protein, G is Graph attention network and T is Triangular multiplication update). Different from other methods which need to perform multiple sequence alignment processes and extract complicated manual features, PGT extracts embeddings of residues through the protein language model. Besides, we introduce structural information through the graph attention network to learn the spatial information of subunits from the complex structure and utilize the triangular multiplication module to capture triangular constraints between residues. To demonstrate the effectiveness of our method, we compare PGT with previous works such as DeepHomo, DRCon and Glinter on two independent test sets. The results show that PGT substantially outperforms these methods. Furthermore, we also perform two ablation experiments to demonstrate the necessity of introducing graph attention network and triangular multiplication update. In all, our framework presents new modules to accurately predict inter-chain contact maps in homologous protein complexes and it’s also useful to analyze interactions in other type of protein complexes.