Authors
Wang, S., Zhuang, J., Cui, X., Lv, Z., Hou, D., Zhang, G.
Abstract
Accurate modeling of antibody-antigen complexes is crucial for advancing therapeutics and diagnostics, yet predicting their binding interface remains a formidable challenge. To address this, we introduce DeepAAAssembly, a protocol that enhances static structural information from AlphaFold3 by integrating dynamic interaction patterns to guide complex assembly. Our approach leverages predicted inter-chain residue distances to construct a flexibility-aware energy function, which drives a two-stage conformational sampling process for global exploration and local exploitation. On a benchmark set of 67 representative antibody-antigen complexes, by incorporating a built-in confidence selection mechanism, DeepAAAssembly outperforms AlphaFold3, achieving not only a 12.9% higher average DockQ score and more medium- and high-quality models, but also reliably elevating the most challenging cases from incorrect to acceptable accuracy. These results demonstrate that DeepAAAssembly effectively captures conserved interaction motifs and conformational flexibility, offering a robust framework for high-accuracy antibody-antigen modeling.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 05 Nov 2025.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 51
- Comments 0