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Conditional Antibody Design as 3D Equivariant Graph Translation
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Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregressive manner. In this paper, we propose Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency and precision against previous autoregressive approaches. Our method significantly surpasses state-of-the-art models in sequence and structure modeling, antigen-binding CDR design, and binding affinity optimization. Specifically, the relative improvement to baselines is about 23% in antigen-binding CDR design and 34% for affinity optimization.
Forward citations
Cited by 6 Pith papers
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AbFlowNet trains a diffusion-based antibody CDR designer with a GFlowNet Trajectory Balance term so that sampled CDRs are rewarded for lower Rosetta-estimated binding energy, improving energy and reconstruction metric...
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A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer
Light-DDG is a fast, distilled Transformer for binding-energy mutation prediction that is repurposed as an antibody optimizer and explainer, but its benchmark gains may be inflated by training on teacher-generated mut...
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Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization
RAAD is a relation-aware equivariant graph network that co-generates antibody CDR sequences and structures in one shot and adds a contrastive specificity loss.
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Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
PepHAR generates peptide binders by first sampling hot-spot residues from a learned energy model, then autoregressively extending fragments via dihedral angles, then refining the full structure.
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Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding
AbMEGD, a fusion of ViS-MP and IPA inside a diffusion process, reports modest CDR-H3 gains over DiffAb on SAbDab.
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