Centrality-Based Approach for Identifying Essential Cancer Proteins in PPI Networks

Protein–protein interaction (PPI) networks serve as invaluable repositories, shedding light on the intricate web of protein interactions within living organisms. Traditional methods for identifying essential proteins fail to capture the complex dynamics of these networks. This research aims to introduce a novel quantum-inspired centrality-based approach for identifying essential proteins in PPI networks (Quantum-EPI), leveraging quantum walk principles and a sequential pattern algorithm. A composite score for each protein is calculated by leveraging centrality measures, including degree, betweenness, closeness, and clustering coefficient. The algorithm, inspired by quantum interference and superposition, employs composite scores as a sorting mechanism. A quantum walk-inspired thresholding strategy distinguishes essential proteins, introducing a quantum-inspired probabilistic selection. The approach is exemplified using a set of centrality values and validated through permutation and enrichment tests. The Quantum-EPI approach prioritizes candidate cancer proteins and identifies nine proteins as the most significant disease-causing proteins through local and global topological analysis, correlation analysis, and ranking strategies. The simulation results, along with the validation through permutation and enrichment tests, demonstrate the superiority of the Quantum-EPI approach over existing state-of-the-art methods, providing a strong foundation for its efficacy in pinpointing essential proteins crucial to PPI network connectivity. The Quantum-EPI approach not only contributes to the field of network medicine but also sets the stage for innovative methodologies in protein identification and pathway analysis. The findings of this research hold significant promise for cancer research, drug design, and disease prevention, offering a new paradigm for essential protein discovery in biological systems.