PHASE：用局部哈密顿量和全原子逆映射编码全局蛋白质构象集合
PHASE: encoding global protein ensembles with local Hamiltonians and all-atom backmapping
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中文总结 AI 辅助
本文提出PHASE框架，将蛋白质构象集合转换为显式统计模型，可重现残基统计、定义激活景观坐标，完成粗粒度-采样-逆映射循环，支持QUBO编码，用于构建蛋白质构象集合的紧凑可解释统计模型。
中文摘要 AI 辅助
蛋白质功能由构象集合决定，这些集合可视为分子构象上的高维概率分布。然而，这些分布的统计结构通常仅以隐式方式表示，要么通过模拟轨迹集合，要么通过高容量生成模型。本文中，我们引入PHASE（Protein Hamiltonians for Sampling of Ensembles，用于采样集合的蛋白质哈密顿量），这是一种特定于系统的框架，可将原子级构象集合转换为显式且可解释的统计模型。将其应用于从腺苷A2A受体的约37微秒原子级模拟得到的10个构象集合，仅包含6埃以内局部残基耦合的哈密顿量可重现残基级和成对微态统计，包括模型中未直接耦合残基间的相关性。此外，独立拟合的非活性和活性参考哈密顿量定义了一个终点偏好坐标，该坐标可沿A2A激活景观组织新采样的配体、效应物和构象依赖性集合，而无需将这些生化标签作为模型输入。最后，一个簇条件全原子重建模型保留了新采样构象的规定残基微态模式，完成了粗粒度-采样-逆映射循环。所得离散表示还支持直接QUBO编码，可实现经典退火并为未来量子退火实现提供途径。因此，PHASE提供了一种通用蛋白质方法，用于构建紧凑、可解释且原子级可实现的蛋白质构象集合统计模型。
英文摘要
Protein function is governed by conformational ensembles, which can be viewed as high-dimensional probability distributions over molecular conformations. Yet the statistical organization of these distributions is often represented only implicitly, either through collections of simulation trajectories or within high-capacity generative models. Here, we introduce PHASE (Protein Hamiltonians for Sampling of Ensembles), a system-specific framework that converts atomistic conformational ensembles into an explicit and interpretable statistical model. Applied to ten conformational ensembles derived from approximately 37$μ$s of atomistic simulations of the adenosine A2A receptor, Hamiltonians containing only local residue couplings within 6$\mathring{A}$ reproduce residue-wise and pairwise microstate statistics, including correlations between residues that are not directly coupled in the model. Moreover, independently fitted inactive and active reference Hamiltonians define an endpoint preference coordinate that organizes newly sampled ligand-, effector- and conformation-dependent ensembles along the A2A activation landscape without receiving these biochemical labels as model inputs. Finally, a cluster-conditioned all-atom reconstruction model preserves the prescribed residue microstate patterns of newly sampled configurations, closing the coarse-graining-sampling-backmapping cycle. The resulting discrete representation additionally admits direct QUBO encoding, enabling classical annealing and providing a route toward future quantum-annealing implementations. PHASE therefore provides a protein-general procedure for constructing compact, interpretable and atomistically realizable statistical models of protein conformational ensembles.
