蛋白构象动态分析中，不同表示方法会改变结果解读，需多视角比较。
Representation choice shapes the interpretation of protein conformational dynamics
- 提出基于几何的旋转感知表示法（Orientation features）
- 不同表示法揭示互补的构象特征，无单一方法能全覆盖
- 适合分子动力学研究者、生物物理学家使用
分子动力学模拟提供原子级详细轨迹，但从高维数据中提取可解释且稳健的见解仍具挑战。实践中通常依赖单一表示方式。本文表明，表示选择并非中立：它从根本上影响从相同模拟数据中推断出的构象组织、相似关系和表观转变。为此，我们引入了方向特征（Orientation features），一种几何基础、旋转感知的蛋白质主链编码方法。在快速折叠蛋白、大尺度域运动及蛋白-蛋白结合三种动态情形下，与常见描述方法进行对比。结果显示，不同表示强调构象空间的不同方面，且没有单一表示能完整呈现底层动态。为促进系统性比较，我们开发了ManiProt，一个用于高效计算和分析多种蛋白质表示的工具库。结果支持建立一种比较性的、表示意识的分子动力学模拟解读框架。
原文摘要 · Abstract (English)
Molecular dynamics simulations provide detailed trajectories at the atomic level, but extracting interpretable and robust insights from these high-dimensional data remains challenging. In practice, analyses typically rely on a single representation. Here, we show that representation choice is not neutral: it fundamentally shapes the conformational organization, similarity relationships, and apparent transitions inferred from identical simulation data. To complement existing representations, we introduce Orientation features, a geometrically grounded, rotation-aware encoding of protein backbone. We compare it against common descriptions across three dynamical regimes: fast-folding proteins, large-scale domain motions, and protein-protein association. Across these systems, we find that different representations emphasize complementary aspects of conformational space, and that no single representation provides a complete picture of the underlying dynamics. To facilitate systematic comparison, we developed ManiProt, a library for efficient computation and analysis of multiple protein representations. Our results motivate a comparative, representation-aware framework for the interpretation of molecular dynamics simulations.
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