Interpretable multilevel interaction modeling for robust protein–protein affinity

Abstract

Quantifying protein–protein binding affinity is essential for understanding molecular recognition and guiding antibody and inhibitor design. However, binding affinity is governed by tightly coupled sequence, structural, and chemical determinants. Existing models often encode these factors in isolation, limiting their ability to capture the multi-level dependencies underlying binding affinity  $$\left(\Delta\text{G}\right)$$ . We propose MIRAGE, a graph-based framework for direct $$\Delta \text{G}$$ prediction that explicitly models interactions across multidimensional (1D sequences, 2D contact maps, 3D structures) and multi-scale (residue-level, atom-level) features. MIRAGE integrates two complementary modules to capture cross-dimensional and cross-scale dependencies, enabling unified residue–atom representation learning. Across public benchmarks, MIRAGE demonstrated strong generalization, achieving Pearson correlations of 0.70 and 0.69 on two independent external test sets and retaining predictive effectiveness under structure-separated cross-validation designed to reduce structural information sharing. In a supplementary analysis with AlphaFold3-predicted complex structures, MIRAGE also preserved significant predictive correlations when experimentally resolved structures were unavailable. Ablation studies confirm the contributions of each module. Interpretability analyses further show that the model focuses on biophysically meaningful interface regions. The source code of MIRAGE is available from https://github.com/ShiweiWu-545/MIRAGE . These results indicate that explicitly modeling multi-level interactions is important for accurately capturing the determinants of binding affinity. MIRAGE provides an interpretable and robust framework for structure-aware $$\Delta \text{G}$$ prediction, with potential applications in protein engineering and drug design.