Representation choice shapes the interpretation of protein conformational dynamics
Abstract
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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BibTeX
@article{axel2026representation,
title = {{Representation choice shapes the interpretation of protein conformational dynamics}},
author = {Giottonini Axel and Lemmin Thomas},
journal = {Communications Chemistry},
year = {2026},
month = oct,
doi = {10.1038/s42004-026-02209-x},
url = {https://www.nature.com/articles/s42004-026-02209-x},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Representation choice shapes the interpretation of protein conformational dynamics
AU - Giottonini Axel
AU - Lemmin Thomas
PY - 2026
DA - 2026/10/12
JO - Communications Chemistry
DO - 10.1038/s42004-026-02209-x
UR - https://www.nature.com/articles/s42004-026-02209-x
AB - 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.
SN - 2399-3669
ER -