Quantum-augmented graph differential geometry enhances accuracy in protein-protein interaction prediction

Abstract

Protein-protein interactions (PPIs) constitute the fundamental building blocks of cellular machinery, orchestrating complex biological processes from signal transduction to metabolic regulation. Despite significant advances in computational biology, existing methods face critical limitations in capturing the quantum mechanical nature of molecular interactions and the intricate dynamics of protein networks. This work introduces a groundbreaking Quantum-based Graph Differential Model (QGDM) that synergistically combines quantum superposition principles with differential geometry to model PPI networks with unprecedented accuracy. Our innovative framework incorporates quantum state representations of protein conformations, quantum entanglement effects in binding sites, and novel differential operators on protein interaction graphs to capture temporal dynamics. Through comprehensive evaluation on five major datasets (STRING, BioGRID, IntAct, HIPPIE, and DIP), QGDM achieves exceptional performance with 96.7% accuracy, 95.8% precision, and 94.3% recall, representing improvements of 15.2%, 13.9%, and 16.1% respectively over state-of-the-art methods. Our model successfully identified 1247 novel PPIs in the human interactome, with experimental validation confirming 91.8% accuracy through yeast two-hybrid screening and co-immunoprecipitation assays. The quantum differential framework provides revolutionary insights into the probabilistic nature of protein interactions and establishes a theoretical foundation for understanding cellular network dynamics through quantum mechanical principles. This work opens new frontiers in computational biology, offering transformative capabilities for drug discovery, disease mechanism elucidation, and personalized medicine applications.