发表机构
Quantum Group, School of Computing, Newcastle University（纽卡斯尔大学计算学院量子小组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对蛋白质侧链包装的 NP 难问题，提出一种混合量子-经典流水线，利用 QAOA 和约束保持拟设，在 AlphaFold 主链上降低构象能量，并在 5PTI 上验证有效性。
AI 中文摘要
侧链包装是蛋白质折叠中的关键阶段，对基于结构的药物发现具有直接影响。Google DeepMind 的工具 AlphaFold2 能可靠预测蛋白质主链，但侧链定位的准确性较低。在固定主链上恢复最低能量的旋转异构体分配是 NP 难的。我们提出了一种混合量子-经典流水线，使用量子近似优化算法（QAOA）在 AlphaFold 主链上重新包装侧链，将单体和双体能量编码为二次无约束二元优化（QUBO）问题。我们引入了一种约束保持的拟设，将 W 态初始化与循环 XY 环混合器配对，在无惩罚项的情况下强制单热旋转异构体有效性，同时保持双量子比特门数量随旋转异构体数量线性扩展。我们还定义了一个渐近射击缩放度量，以实验解析的构象为基准，独立于基线固定优化器质量；其拟合增长在中等旋转异构体灵活性下低于经典穷举搜索速率。在牛胰腺胰蛋白酶抑制剂（5PTI）上，跨越高和中等 AlphaFold 置信度区域进行评估，该流水线相对于 AlphaFold 基线降低了构象能量。
英文摘要
Sidechain packing is a critical stage in protein folding, with direct implications for structure-based drug discovery. Google DeepMind's tool, AlphaFold2, predicts protein backbone reliably but sidechain positioning less accurately. Recovering the lowest-energy rotamer assignment over a fixed backbone is NP-hard. We present a hybrid quantum-classical pipeline that repacks sidechains on the AlphaFold backbone using the Quantum Approximate Optimisation Algorithm (QAOA), encoding the one- and two-body energies as a quadratic unconstrained binary optimisation (QUBO) problem. We introduce a constraint-preserving ansatz, pairing a W-state initialisation with a cyclic XY ring mixer, that enforces one-hot rotamer validity without penalty terms while keeping two-qubit gate scaling linear in the rotamer count. We also define an asymptotic shot-scaling metric, measured against the experimentally resolved conformation, that fixes optimiser quality independently of the baseline; its fitted growth stays below the classical exhaustive-search rate at moderate rotamer flexibility. Evaluated on bovine pancreatic trypsin inhibitor (5PTI) across high- and moderate AlphaFold-confidence regions, the pipeline lowers conformational energy against the AlphaFold baseline.
Comments6 pages, 3 figures
