Accurately Predicting Protein Mutational Effects via a Hierarchical Many-Body Attention Network

Dahao Xu, Jiahua Rao, Mingming Zhu, Jixian Zhang, Wei Lu, Shuangjia Zheng, Yuedong Yang

Keywords: Protein Mutational Effects, Protein-Protein Interactions, Binding Free Energy, Hierarchical Many-Body Attention

TL;DR: We present H3-DDG, a model that predicts binding free energy changes by modeling higher-order many-body interactions, achieving state-of-the-art performance in protein mutational effect prediction.

Abstract:

Predicting changes in binding free energy ($\Delta\Delta G$) is essential for understanding protein-protein interactions, which are critical in drug design and protein engineering. However, existing methods often rely on pre-trained knowledge and heuristic features, limiting their ability to accurately model complex mutation effects, particularly higher-order and many-body interactions.
To address these challenges, we propose H3-DDG, a Hypergraph-driven Hierarchical network to capture Higher-order many-body interactions across multiple scales. By introducing a hierarchical communication mechanism, H3-DDG effectively models both local and global mutational effects.
Experimental results demonstrate state-of-the-art performance on multiple benchmarks. On the SKEMPI v2 dataset, H3-DDG achieves a Pearson correlation of 0.75, improving multi-point mutations prediction by 12.10%. On the challenging BindingGYM dataset, it outperforms Prompt-DDG and BA-DDG by 62.61% and 34.26%, respectively.
Ablation and efficiency analyses demonstrate its robustness and scalability, while a case study on SARS-CoV-2 antibodies highlights its practical value in improving binding affinity for therapeutic design.

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 3112