Conditional Antibody Design as 3D Equivariant Graph Translation

TL;DR

This paper introduces MEAN, a novel deep learning model that co-designs antibody CDR sequences and structures by modeling antibody-antigen interactions as a 3D graph translation problem, improving efficiency and accuracy.

Contribution

The paper presents MEAN, a 3D equivariant graph translation model that jointly predicts antibody CDR sequences and structures, addressing limitations of previous autoregressive methods.

Findings

Outperforms state-of-the-art models in sequence and structure prediction.

Achieves 23% improvement in antigen-binding CDR design.

Realizes 34% enhancement in affinity optimization.

Abstract

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.