Continuous-Time Quantum-Walk Centrality Identifies Key Protein Residues with Quantum Interference Signatures.

Identifying critical residues in proteins is fundamental for understanding protein function, designing novel therapeutics, and engineering enzymes. Traditional computational methods, such as classical eigenvector centrality, often rely on simplified network models that may overlook subtle, yet significant, quantum mechanical interactions governing protein dynamics. This gap limits the precision of identifying structurally and functionally important residues, hindering advancements in drug discovery and protein engineering where precise target identification is paramount.

Researchers developed a novel computational framework using continuous-time quantum walks (CTQWs) on weighted residue-interaction networks derived from experimentally resolved protein structures. They mapped the weighted adjacency matrix of these networks to a Hamiltonian, allowing residue importance to be determined by the long-time averaged occupation probability. The framework was validated across a dataset of 150 proteins spanning diverse structural and functional classes. Comparisons were made against classical eigenvector centrality, and the method's biological relevance was assessed by recovering known functional residues in protein kinase A and oxytocin. A proof-of-principle implementation was demonstrated on IBM superconducting quantum hardware.

The CTQW centrality framework demonstrated consistently strong agreement with classical eigenvector centrality in identifying central protein residues across a dataset of 150 proteins. Crucially, it extended beyond classical methods by incorporating unique signatures of quantum interference, providing a more nuanced understanding of residue importance. Analysis of the time-averaged quantum transition matrix revealed consistently larger spectral gaps compared to the classical random-walk operator, indicating enhanced information propagation. Furthermore, the biological relevance of the CTQW method was confirmed through the successful recovery of experimentally established functional residues in both protein kinase A and oxytocin. This suggests the method can accurately pinpoint residues critical for biological activity. > The CTQW-derived centrality rankings are accessible on near-term intermediate-scale quantum hardware, with a successful proof-of-principle implementation on IBM superconducting quantum hardware, highlighting its practical applicability.