AI 中文总结
该研究针对蛋白质结构预测仅关注单一构象的不足，提出将其扩展为构象状态空间推断问题，综述相关策略并勾勒出该方向的发展路线图。
AI 中文摘要
近期AI的进展已能以接近实验的精度实现蛋白质结构预测，很大程度上解决了从序列中识别主导构象的问题。然而，许多蛋白质作为动态系统，会占据多个构象状态，其功能源于相对占比的变化——若仅用单一结构来描述，便会缺失这一关键信息。本文提出，结构预测应被重新表述为状态空间推断问题：不仅要恢复单个构象的坐标，还要获取可及状态、其能量与动力学关系、环境依赖性及对扰动的响应。我们综述了新兴策略，包括深度学习集成生成器、基于物理的模拟及实验约束，并勾勒出迈向状态空间预测的路线图。
英文摘要
Recent AI advances have enabled protein structure prediction at near-experimental accuracy, largely solving the problem of identifying a dominant conformation from sequence. Many proteins, however, function as dynamic systems populating multiple conformational states with activity emerging from shifts in relative occupancy--an incomplete picture when reduced to one structure. Here, we argue that structure prediction should be reformulated as a state-space inference problem: recovering not one conformation's coordinates but accessible states, their energetic and kinetic relationships, context dependence, and responses to perturbations. We review emerging strategies--deep learning ensemble generators, physics-based simulations, and experimental constraints--and outline a roadmap toward state-space prediction.
