QAOA 用于蛋白质侧链堆积：在 AlphaFold 骨架基础上改进构象
Quantum Approximate Optimisation Algorithm for Protein Sidechain Packing
DeepMind 系之外的团队拿 QAOA 修 AlphaFold2 的侧链短板，在 5PTI 上能量真的降了，做量子计算或蛋白质折叠的可以看看。
蛋白质侧链堆积是基于结构的药物发现的关键步骤，但 AlphaFold2 对侧链位置的预测精度低于主链。论文提出一个量子-经典混合流水线，用 QAOA 在 AlphaFold2 预测的主链上重新堆积侧链，把单体和二体能量编码为 QUBO 优化问题。作者设计了约束保持的 ansatz，结合 W 态初始化与循环 XY 环混合器，在不引入罚项的情况下保证 one-hot rotamer 有效性，且双比特门数量随 rotamer 数线性增长。在牛胰蛋白酶抑制剂 5PTI 的高置信和中等置信区域测试中，该流水线将构象能量降到 AlphaFold2 基线之下。
Quantum Approximate Optimisation Algorithm for Protein Sidechain Packing
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.
