Abstract Long-range interactions between residues in proteins underlie diverse biological processes such as signal transmission, conformational regulation, and catalysis. Identifying residues that mediate such communication remains a major challenge. Herein, we introduce a framework based on quantum walks and entropy, and employ it on protein structures to predict important residues participating in long-range interactions. The selected proteins have been well-studied experimentally in literature. Our approach models information flow through protein contact networks using discrete-time quantum walk dynamics, where quantum superposition and interference govern transition amplitudes at the residue level. Normalized time-averaged transition probabilities quantify interaction strength between residues, while Shannon entropy calculations identify residues exhibiting delocalized interaction behavior. We find that the residues identified by us are known to be structurally and functionally important in literature, thereby confirming the predictive capacity of this framework. We experimentally validate our formalism on As LOV2 (PDB: 2V0U) using site-directed mutagenesis, protein purification, and dark-recovery kinetics. The predicted residues are important for understanding the mechanism of photosensing in As LOV2. Significance This study bridges quantum transport theory and structural biology by introducing discrete-time quantum walks as a mechanism to model communication between residues. We provide a scalable and interpretable method to predict key residues, without requiring molecular dynamics or prior functional annotation. The present method, inspired by quantum physics and quantum information, and validated by experiments, offers a broad and robust framework to study communication in biomolecular systems. It further demonstrates how quantum principles such as coherence and interference can enhance our understanding of information flow in biological macromolecules.
Abstract Long-range interactions between residues in proteins underlie diverse biological processes such as signal transmission, conformational regulation, and catalysis. Identifying residues that mediate such communication remains a major challenge. Herein, we introduce a framework based on quantum walks and entropy, and employ it on protein structures to predict important residues participating in long-range interactions. The selected proteins have been well-studied experimentally in literature. Our approach models information flow through protein contact networks using discrete-time quantum walk dynamics, where quantum superposition and interference govern transition amplitudes at the residue level. Normalized time-averaged transition probabilities quantify interaction strength between residues, while Shannon entropy calculations identify residues exhibiting delocalized interaction behavior. We find that the residues identified by us are known to be structurally and functionally important in literature, thereby confirming the predictive capacity of this framework. We experimentally validate our formalism on As LOV2 (PDB: 2V0U) using site-directed mutagenesis, protein purification, and dark-recovery kinetics. The predicted residues are important for understanding the mechanism of photosensing in As LOV2. Significance This study bridges quantum transport theory and structural biology by introducing discrete-time quantum walks as a mechanism to model communication between residues. We provide a scalable and interpretable method to predict key residues, without requiring molecular dynamics or prior functional annotation. The present method, inspired by quantum physics and quantum information, and validated by experiments, offers a broad and robust framework to study communication in biomolecular systems. It further demonstrates how quantum principles such as coherence and interference can enhance our understanding of information flow in biological macromolecules.
We present a quantum-dynamical framework for identifying structurally and functionally important residues in proteins based on continuous-time quantum walks (CTQWs) on weighted residue-interaction networks constructed from experimentally resolved structures. By mapping the weighted adjacency matrix to a Hamiltonian, residue importance emerges from the long-time averaged occupation probability, confirmed analytically through its spectral decomposition. Across a data set of 150 proteins spanning diverse structural and functional classes, CTQW centrality exhibits consistently strong agreement with classical eigenvector centrality in identifying central residues, while extending beyond it through incorporating signatures of quantum interference. Analyzing the time-averaged quantum transition matrix reveals consistently larger spectral gaps than the classical random-walk operator. Furthermore, biological relevance is confirmed through recovery of experimentally established functional residues in proteins kinase A and oxytocin. CTQW-derived centrality rankings are accessible on near-term intermediate-scale quantum hardware, as we demonstrate through a proof-of-principle implementation on IBM superconducting quantum hardware. These results establish continuous-time quantum walks as a computationally tractable framework for protein network analysis, that connects network theoretical treatments of protein structural biology to continuous-time quantum walk dynamics.
We present a quantum-dynamical framework for identifying structurally important residues in proteins based on continuous time quantum walks (CTQWs) on weighted residue interaction networks constructed from experimentally resolved structures. By mapping the weighted adjacency matrix to a Hamiltonian, residue importance emerges from the long-time averaged occupation probability, confirmed analytically through its spectral decomposition. Across a dataset of approximately 150 proteins spanning diverse structural and functional classes, CTQW centrality exhibits consistently strong agreement with classical eigenvector centrality in identifying central residues, while extending beyond it through incorporating signatures of quantum interference. Analyzing the time-averaged quantum transition matrix reveals consistently larger spectral gaps than the classical random-walk operator. Furthermore, biological relevance is confirmed through recovery of experimentally established functional residues in proteins kinase A and oxytocin. CTQW-derived centrality rankings are accessible on near-term intermediate-scale quantum hardware, as we demonstrate through a proof-of-principle implementation on IBM superconducting quantum hardware. These results establish continuous-time quantum walks as a computationally tractable framework for protein network analysis, that connects network theoretical treatments of protein structural biology to continuous-time quantum walk dynamics.
Quantum walks provide a versatile framework for probing the structural and dynamical properties of complex systems ranging from biological networks to synthetic materials. However, their realization on current noisy pre-fault-tolerant quantum computers is fundamentally limited by decoherence. Conventional dense encodings of graph structures require prohibitively deep circuits, making them incompatible with existing hardware. Here we introduce an algorithm that leverages symmetry-sector encoding and trades circuit depth for the number of qubits, while integrating symmetry-respecting postselection as an effective noise-mitigation strategy. This combination enables us to execute practical quantum-walk circuits for biological network motifs on actual quantum hardware. We benchmark the proposed methodology against known state-of-the-art circuit architectures, highlighting significant reduction of circuit depth in our approach at the cost of moderate qubit overhead. Utilizing 40 qubits, we implement quantum walks on complex graphs containing up to 17 nodes and 20 edges -- the largest experiment on superconducting hardware to date, with the Hellinger fidelity exceeding 87% throughout 7 steps. We present a proof-of-principle case study that illustrates how experimentally obtained quantum-walk dynamics on a protein-protein-interaction network can be applied to prioritizing disease-associated genes. We discuss the framework scalability in the pre-fault-tolerant era and its potential for studying larger biological networks.
Abstract Proteins occupy heterogeneous free-energy landscapes, where broad, high-entropy ensembles converge toward compact, low-energy basins containing multiple sub-states. Molecular dynamics can capture these landscapes at atomic resolution, but exhaustively sampling rare transitions remains computationally demanding. Furthermore, most quantum approaches to protein modelling target only a single optimal structure, leaving the energetic heterogeneity of the full ensemble largely unexplored. We introduce a residue-level, gate-based quantum circuit framework for coarse-graining protein thermodynamics. Each amino acid is represented as a two-state qubit with stabilised and excited solvation states, initialised from solvation energetics. Following this, a structure-informed entanglement block of parametrised controlled gates encodes covalent bonds and non-covalent contacts across the residue network. The circuit is then measured approx 10 6 times, yielding an ensemble of binary thermodynamic microstates, from which we compute energy distributions, residue-level statistical couplings, energetic sensitivities, and information gain relative to total free energy. We showcase the comparative results for Trp-cage miniprotein (PDB:1L2Y) and the disulfide-stabilised chimera (PDB:9GDL), which comprises a structured, folding-funnel-like energy distribution for 1L2Y. Moreover, comparison with 9GDL reveals clear shifts in global energy and residue-level profiles, localising the residues driving stabilisation and ensemble reorganisation. Additionally, dimer- and trimer-level couplings resolve both direct and interaction-mediated dependencies, showing the circuit captures network-level, not just isolated, correlations. The framework moves quantum protein modelling beyond single-structure optimisation toward interpretable, ensemble-level characterisation.
We introduce RinQ, a hybrid quantum-classical framework for identifying functionally critical residues in proteins, utilizing techniques in quantum optimization. To that end, protein structures are modeled as residue interaction networks (RINs), and the centrality detection task is cast as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Solved using D-Wave’s simulated annealing, this approach is applied to a diverse set of proteins, including small peptides and biologically significant regulatory proteins. RinQ consistently finds residues that align with classical centrality benchmarks, underscoring the accuracy and reliability of the approach. This work highlights the promise of near-term quantum and quantum-inspired methods for advancing protein network analysis and lays the groundwork for future extensions to larger systems using real quantum hardware.
This paper presents a novel approach to protein structure prediction leveraging the principles of quantum entanglement. Traditional protein structure prediction methods are often limited by the computational complexity of simulating large biomolecular systems. We propose a framework that utilizes quantum entanglement to model the complex correlations inherent in protein folding, potentially overcoming these limitations. The core idea involves translating the amino acid sequence of a protein into a quantum state and employing quantum computation, specifically entanglement-based algorithms, to predict the protein's three-dimensional structure. The theoretical framework outlines the transformation process, the quantum algorithm design, and the methods for interpreting the results. We explore the potential advantages of this approach, focusing on its ability to capture long-range interactions and conformational flexibility that are difficult to model accurately with classical methods. The ultimate goal is to establish a new paradigm for protein structure prediction, offering improved accuracy and efficiency.