AI 中文总结
该研究提出残基级门控量子电路框架，将氨基酸表示为两态量子比特，在Trp-cage等蛋白上重现能量分布，解析系综统计相关性，扩展量子蛋白建模至系综水平表征。
AI 中文摘要
蛋白质占据非均匀的自由能景观，其中高熵系综收敛到具有多个亚态的紧凑低能盆地。分子动力学可在原子分辨率下访问这些景观，但穷尽采样在计算上仍要求极高。同时，大多数量子方法仅针对单一最优结构，未探索完整系综的能量异质性。我们提出一种基于残基级、门控的量子电路框架，用于粗粒化蛋白质热力学。每个氨基酸基于残基溶剂化能量学表示为两态量子比特（稳定态与激发溶剂化态）。结构感知的纠缠块随后使用参数化受控门编码共价和非共价接触，嵌入残基相互作用网络中的相关性。对电路进行采样（约10^6次测量）得到二元热力学微态，用于计算蛋白质能量分布、残基级统计耦合、能量敏感性以及相对于总自由能的信息增益。我们在基准Trp-cage微型蛋白1L2Y（TC5b）和9GDL（一种二硫键稳定的Trp-cage强化exenatide嵌合体）上展示该框架。对于1L2Y，电路重现了类似结构化折叠漏斗的能量分布。与9GDL的比较分析揭示了全局能量分布和残基级稳定性谱的变化。耦合和信息论分析定位了与系综重组相关的残基，而多体耦合表明该电路可解析直接和间接统计相关性。该框架将量子蛋白质建模从单一结构优化扩展到系综水平表征，捕捉崎岖能量景观的关键特征，以指导蛋白质设计、突变作图和变构途径识别。
英文摘要
Proteins occupy heterogeneous free-energy landscapes in which high-entropy ensembles converge toward compact, low-energy basins with multiple sub-states. Molecular dynamics can access these landscapes at atomic resolution, but exhaustive sampling remains computationally demanding. Meanwhile, most quantum approaches target only single optimal structures, leaving full ensemble energetic heterogeneity unexplored. We introduce a residue-level, gate-based quantum circuit framework for coarse-graining protein thermodynamics. Each amino acid is represented as a two-state qubit (stabilised vs. excited solvation state) based on residue solvation energetics. A structure-informed entanglement block then encodes covalent and non-covalent contacts using parameterised controlled gates, embedding correlations across the residue-interaction network. Sampling the circuit ($\sim 10^6$ measurements) yields binary thermodynamic microstates used to compute protein energy distributions, residue-level statistical couplings, energetic sensitivities, and information gains relative to total free energy. We showcase the framework on the benchmark Trp-cage miniprotein 1L2Y (TC5b) and 9GDL, a disulfide-stabilised Trp-cage-fortified exenatide chimera. For 1L2Y, the circuit reproduces a structured, folding-funnel-like energy distribution. Comparative analysis with 9GDL reveals shifts in global energy distributions and residue-level stability profiles. Coupling and information-theoretic analyses localise residues associated with ensemble reorganisation, while multi-body couplings show the circuit resolves both direct and indirect statistical correlations. This framework expands quantum protein modelling beyond single-structure optimisation toward ensemble-level characterisation, capturing key features of rugged energy landscapes to guide protein design, mutation mapping, and allosteric pathway identification.
CommentsPlease see github repo https://github.com/pra-ashok/QProtSim for code and supplementary information. A Pypi package qprotsim is also available at https://pypi.org/project/qprotsim/
