Multi-Scale Feature Fusion Network for the Prediction of Protein-Protein Binding Affinity Changes upon Mutations

Accurate prediction of changes in protein-protein binding affinity influenced by mutations (i.e., ΔΔ G) is essential for understanding the structure and function of proteins and elucidating the underlying mechanisms of complex diseases. We introduce a novel multi-scale feature fusion network for protein-protein ΔΔ G prediction, aiming to reduce the dependency of previous methods on intricate biological features and expert-driven knowledge, and demonstrate its effectiveness in the challenging and important domain of protein data. Specifically, we employ multi-scale modeling of protein complexes, and introduce self-attention mechanisms and various extraction modules to comprehensively capture the features. The proposed method effectively learns the complete biological regulation of complexes and incorporates interactions between amino acids. Extensive experiments on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art methods for ΔΔ G prediction while also providing excellent performance in the domain of membrane protein design.