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The authors demonstrate that the choice of representation significantly impacts the interpretation of protein conformational dynamics derived from molecular dynamics simulations. They introduce "Orientation features," a novel rotation-aware encoding of protein backbone geometry, and compare it against existing representations across diverse dynamical regimes. Their findings reveal that different representations highlight complementary aspects of conformational space, advocating for a comparative, representation-aware approach to analyzing molecular dynamics data.
Seemingly identical molecular dynamics simulations can lead to drastically different interpretations of protein behavior, depending solely on the chosen representation.
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.