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RinQ: Predicting central sites in proteins on current quantum computers

Shah Ishmam Mohtashim

Published Jul 6, 2025• 1 citation
Publisher

Abstract

We present RinQ, a hybrid quantum-classical approach for identifying functionally critical residues in proteins by leveraging quantum computing techniques for optimization. Proteins, modeled as residue interaction networks, alongside the task of selecting top central residues, is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is then solved using D-Wave’s simulated annealing. Applied across a diverse set of proteins, the presented approach consistently identifies central residues aligned with classically calculated central sites. RinQ thus exemplifies the potential of near-term quantum and quantum-inspired methods to accelerate protein analysis and inform drug discovery. Our findings match classical benchmarks for small peptides and sets the stage for extending QUBO-based centrality detection to larger proteins using real quantum hardware.

DOI
10.26434/chemrxiv-2025-zmd2s

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We present RinQ, a hybrid quantum-classical approach for identifying functionally critical residues in proteins by leveraging quantum computing techniques for optimization. Proteins, modeled as residue interaction networks, alongside the task of selecting top central residues, is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is then solved using D-Wave’s simulated annealing. Applied across a diverse set of proteins, the presented approach consistently identifies central residues aligned with classically calculated central sites. RinQ thus exemplifies the potential of near-term quantum and quantum-inspired methods to accelerate protein analysis and inform drug discovery. Our findings match classical benchmarks for small peptides and sets the stage for extending QUBO-based centrality detection to larger proteins using real quantum hardware.
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.
We introduce RinQ, a hybrid quantum-classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave’s simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach.
Demonstrating the practical utility of Noisy Intermediate-Scale Quantum (NISQ) hardware for recurrent tasks in Computer-Aided Drug Discovery is of paramount importance. We tackle this challenge by performing three-dimensional protein pockets hydration-site prediction on a quantum computer. Formulating the water placement problem as a Quadratic Unconstrained Binary Optimization (QUBO), we use a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) to an efficient quantum optimization solver, to run various hardware experiments up to 123 qubits. Matching the precision of classical approaches, our results reproduced experimental predictions on real-life protein-ligand complexes. Furthermore, through a detailed resource estimation analysis, we show that accuracy can be systematically improved with increasing number of qubits, indicating that full quantum utility is in reach. Finally, we provide evidence that advantageous situations could be found for systems where classical optimization struggles to provide optimal solutions. The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.
Modelling and predicting protein configurations is crucial for advancing drug discovery, enabling the design of treatments for life-threatening diseases. A critical aspect of this challenge is rotamer optimisation - the determination of optimal side-chain conformations given a fixed protein backbone. This problem, involving the internal degrees of freedom of amino acid side-chains, significantly influences the protein's overall structure and function. In this work, we develop a resource-efficient optimisation algorithm to compute the ground state energy of protein structures, with a focus on side-chain configuration. We formulate the rotamer optimisation problem as a Quadratic Unconstrained Binary Optimisation problem and map it to an Ising model, enabling efficient quantum encoding. Building on this formulation, we propose a quantum algorithm based on the Quantum Approximate Optimisation Algorithm to explore the conformational space and identify low-energy configurations. To benchmark our approach, we conduct a classical study using custom-built libraries tailored for structural characterisation and energy optimisation. Our quantum method demonstrates a reduction in computational cost compared to classical simulated annealing techniques, offering a scalable and promising framework for protein structure optimisation in the quantum era.
Protein structure prediction remains central to computational biology and drug discovery. While deep learning models such as AlphaFold have achieved remarkable accuracy for known fold families, they struggle with novel folds, intrinsically disordered regions, and conformational dynamics, and they do not explicitly optimize physics-based energy functions. We present a quantum-assisted approach to protein conformation optimization that uses parameterized quantum circuits within a variational framework to explore the energy landscape of small proteins and fragments from first-principles or coarse-grained physics. Our method encodes backbone torsion angles (ϕ, ψ) and selected side-chain degrees of freedom into qubit registers and employs a variational quantum eigensolver (VQE)-inspired optimization to minimize an approximate Hamiltonian incorporating bonded interactions, sterics, hydrogen bonding, and hydrophobic contacts. We introduce problem-inspired ansätze that respect chain connectivity and secondary-structure motifs (helices, beta-sheets) and propose hierarchical circuit growth strategies for scaling from peptides to larger domains. Through classical simulations of small peptides and secondary-structure motifs, we demonstrate that quantum variational optimization can identify low-energy conformations consistent with experimentally determined structures. We provide explicit resource estimates for NISQ and early fault-tolerant quantum computers and discuss hybrid classical-quantum strategies for refining quantum solutions with classical molecular dynamics and machinelearning priors. While full protein-folding quantum advantage remains distant, we identify concrete subproblems where nearterm quantum devices offer complementary physics-based insight beyond data-driven approaches, opening pathways toward practical quantum-classical protein structure and drug discovery pipelines.
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.