Computational Prediction of Protein-Protein Interaction Networks Using Graph Neural Networks and Evolutionary Features
Protein-protein interactions (PPIs) are fundamental to virtually all biological processes, yet experimental determination of complete interactomes remains resource-intensive and error-prone. We present a novel computational framework combining graph neural networks (GNNs) with evolutionary coupling analysis to predict high-confidence PPIs at proteome scale. Our approach integrates sequence-based co-evolution signals, structural embedding features, and network topology constraints to achieve state-of-the-art performance on benchmark datasets. Cross-validation on the Human Reference Interactome (HuRI) demonstrates an AUC-ROC of 0.94, representing a 12% improvement over existing deep learning methods. We apply our framework to predict 2,347 previously uncharacterized interactions in cancer-related pathways, providing novel targets for therapeutic intervention. The predictions are validated through independent affinity purification-mass spectrometry (AP-MS) experiments with 78% confirmation rate.