A hybrid CNN-GNN-XGB ensemble framework for prediction of mutation-induced protein-protein binding free-energy changes (ΔΔG) in protein engineering.
Predicting mutation-induced changes in protein-protein binding free energy (ΔΔG) remains a central challenge in protein engineering and variant interpretation. In this study, we present a hybrid ensemble framework integrating XGBoost, convolutional neural networks (CNN), and graph neural networks (GNN) trained on a curated SKEMPI v2.0 dataset. CNN captures local structural patterns from residue-residue contact maps, GNN models higher-order topological relationships through graph-based representations, and XGBoost incorporates physicochemical and positional sequence descriptors. The proposed ensemble achieved a mean absolute error (MAE) of 0.954 kcal/mol, a root mean square error (RMSE) of 1.336 kcal/mol, an R² of 0.637, and a Pearson correlation of 0.798 under a strict protein-held-out evaluation strategy. The ensemble achieved robust predictive performance comparable to the strongest individual models (XGBoost: R² = 0.634; CNN: 0.027; GNN: -0.36), demonstrating the complementary strengths of the proposed integrated approach. In addition, a paired Wilcoxon signed-rank test showed that the ensemble produced significantly lower absolute prediction errors than the standalone XGBoost baseline (p < 0.001). However, the magnitude of the improvement was modest, with the ensemble reducing the mean absolute error by approximately 0.007 kcal mol⁻¹ . Benchmark analysis of 1ACB, 1CSE, and 1BRS showed high directional agreement between predicted and experimental mutation effects, although the magnitude of the predictions varied across the three complexes. The framework supports both sequence-based prediction using XGBoost and structure-aware prediction using the full ensemble, enabling its application across proteins with or without available structural information. Overall, this framework provides a robust and practical tool for ΔΔG prediction with potential applications in protein engineering and rational mutation design.