A hybrid CNN-GNN-XGB ensemble framework for prediction of mutation-induced protein-protein binding free-energy changes (ΔΔG) in protein engineering.

Abstract excerpt

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...

Topics

• Boosting Machine Learning Algorithms
• Proteins
• Mutation
• Convolutional Neural Networks
• Graph Neural Networks
• Protein Engineering