Representation choice shapes the interpretation of protein conformational dynamics

2026-04-01

AI Summary

Extracting interpretable and robust descriptions of protein conformational dynamics from high-dimensional molecular dynamics trajectories remains challenging, as existing approaches often rely on a single conformational representation while overlooking its fundamental influence on outcomes. This work systematically compares multiple conformational representations and introduces a geometry-driven, rotation-aware backbone orientation feature. To facilitate efficient computation and analysis across diverse representations, the authors develop the ManiProt library. Validation on fast-folding proteins, large-scale domain motions, and protein–protein binding systems demonstrates that different representations are complementary, with no single one fully capturing the complete dynamic landscape. The study reveals that the choice of representation significantly affects inferred conformational organization, similarity metrics, and transition pathways, advocating for a representation-aware comparative framework to establish a new methodological foundation for analyzing protein dynamics.

Technology Category

• Machine Learning: Learning with Manifolds
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• protein conformational dynamics
• molecular dynamics simulations
• representation choice
• high-dimensional data
• interpretable insights

Innovation

• Orientation features
• representation choice
• conformational dynamics
• ManiProt
• molecular dynamics simulations