A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors

TL;DR

This paper introduces a quantum computing framework for predicting protein binding-site structures, outperforming classical methods and AlphaFold on real quantum hardware, demonstrating practical feasibility in structural biology.

Contribution

It develops a novel quantum algorithm for protein structure prediction using VQE on utility-level quantum processors, incorporating a specialized Hamiltonian and noise mitigation techniques.

Findings

Outperforms AlphaFold and classical methods in RMSD and docking efficacy.

Successfully executed on IBM quantum hardware with realistic noise conditions.

Provides an end-to-end pipeline for biologically relevant structure prediction.

Abstract

Accurate prediction of protein active-site structures remains a central challenge in structural biology, particularly for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework specifically developed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we formulate structure prediction as a ground-state energy minimization problem using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is encoded on a tetrahedral lattice model, and structural constraints-including steric, geometric, and chirality terms-are mapped into a problem-specific Hamiltonian represented as sparse Pauli operators. Optimization is performed with a two-stage architecture that separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the framework on 23 randomly selected protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, and execute experiments on the IBM-Cleveland Clinic quantum processor. Predictions are benchmarked against AlphaFold 3 (AF3) and classical simulation-based approaches using identical postprocessing and docking procedures. Our method outperforms both AF3 and classical baselines in RMSD (root-mean-square deviation) and docking efficacy. These results demonstrate an end-to-end, hardware-executable pipeline for biologically relevant structure prediction on real quantum processors, highlighting its engineering feasibility and practical advantages over existing classical and deep learning approaches.