A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
This work addresses the challenge of designing therapeutic antibodies with specific binding properties, which is crucial for drug development, though it appears incremental as it builds on existing deep generative architectures.
The paper tackles the problem of antibody sequence-structure co-design by proposing a hierarchical training paradigm (HTP) that integrates geometric graph neural networks with protein language models, achieving state-of-the-art performance in co-design and fix-backbone design tasks.
Therapeutic antibodies are an essential and rapidly expanding drug modality. The binding specificity between antibodies and antigens is decided by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality within a particular protein domain. Through carefully crafted tasks in different stages, HTP seamlessly and effectively integrates geometric graph neural networks (GNNs) with large-scale protein language models to excavate evolutionary information from not only geometric structures but also vast antibody and non-antibody sequence databases, which determines ligand binding pose and strength. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem as well as the fix-backbone design. Our research offers a hopeful path to unleash the potential of deep generative architectures and seeks to illuminate the way forward for the antibody sequence and structure co-design challenge.