Title: Prediction of Protein Three-dimensional Structures via a Hardware-Executable Quantum Computing Framework Show Chinese title

Title: 通过硬件可执行的量子计算框架预测蛋白质三维结构

Abstract: Accurate prediction of protein active site structures remains a central challenge in structural biology, particularly for short and flexible peptide fragments where conventional 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 the structure prediction task 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 expressed as sparse Pauli operators. The optimization is executed via a two-stage architecture separating energy estimation and measurement decoding, allowing noise mitigation under realistic quantum device conditions. We evaluate the framework on 23 randomly selected real protein fragments from the PDBbind dataset, as well as 7 real fragments from proteins with therapeutic potential, and run the experiments on the IBM-Cleveland Clinic quantum processor. Structural predictions are benchmarked against AlphaFold3 (AF3) using identical postprocessing and docking procedures. Our quantum method outperformed AF3 in both RMSD (Root-Mean-Square Deviation) and docking efficacy. This work demonstrates, for the first time, a complete end-to-end pipeline for biologically relevant structure prediction on real quantum hardware, highlighting its engineering feasibility and practical advantage over existing classical and deep learning approaches. Show Chinese abstract

Abstract: 蛋白质活性位点结构的准确预测仍然是结构生物学中的一个核心挑战，尤其是在短而灵活的肽片段中，传统方法常常失效。 在此，我们提出了一种专为实用级量子处理器开发的量子计算框架，以解决这个问题。 从氨基酸序列出发，我们将结构预测任务表述为使用变分量子本征值求解器（VQE）的基态能量最小化问题。 氨基酸连接性被编码在四面体晶格模型上，结构约束——包括立体、几何和手性项——被映射到一个特定于问题的哈密顿量中，该哈密顿量表示为稀疏泡利算子。 优化通过一个两阶段架构执行，将能量估计和测量解码分开，从而在现实量子设备条件下实现噪声缓解。 我们在PDBbind数据集上随机选取的23个真实蛋白质片段以及具有治疗潜力的蛋白质的7个真实片段上评估了该框架，并在IBM-Cleveland Clinic量子处理器上进行了实验。 结构预测通过相同的后处理和对接程序与AlphaFold3（AF3）进行基准测试。 我们的量子方法在RMSD（均方根偏差）和对接效果方面都优于AF3。 这项工作首次展示了在真实量子硬件上进行生物相关结构预测的完整端到端流程，突显了其工程可行性以及相对于现有经典方法和深度学习方法的实际优势。