Representation choice shapes the interpretation of protein conformational dynamics
This addresses the problem of biased interpretation in protein dynamics analysis for computational biologists, though it is incremental as it builds on existing representation methods.
The study tackled the challenge of extracting interpretable insights from high-dimensional molecular dynamics simulations by showing that the choice of representation fundamentally shapes the inferred conformational organization and transitions. It introduced Orientation features as a new encoding and found that different representations emphasize complementary aspects, with no single one providing a complete picture.
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.