Structured Entropy Analysis (SEA): A computational framework for latent biomolecular insights beyond conventional descriptors in molecular dynamics simulations.

Molecular dynamics (MD) simulations are widely employed to study biomolecular systems, yet conventional feature-based analyses such as the radius of gyration (Rg), root-mean-square deviation (RMSD), and root-mean-square fluctuation (RMSF) often provide only descriptive trends without fully capturing the underlying complexity of structural dynamics. In this study, we introduce Structured Entropy Analysis (SEA), a novel entropy-based framework designed to quantify the latent informational content of MD trajectories. Using ubiquitin (1UBQ), the amyloid beta (Aβ42) peptide fragment (1IYT), and T4 lysozyme (3LZM) as representative systems, we demonstrate the application of SEA through a multi-step pipeline encompassing feature normalization, binary encoding, entropy evaluation, and statistical testing. Entropy metrics, including Shannon entropy, min-entropy, bit balance, runs test, chi-square analysis, and serial correlation, were applied to characterize structured randomness within the feature-derived sequences. Our results reveal that while conventional analyses highlight global stability and flexibility trends, SEA uncovers non-random entropic patterns, correlations, and constrained fluctuations that are strongly indicative of underlying physical and functional characteristics. Quantitatively, SEA consistently detected pronounced deviations from randomness across all systems, with observed runs substantially lower than expected (Rg: 6519 vs 10677 in 1UBQ; RMSD: 4517 vs 10677 across systems; RMSF: 2823 vs 6687 in 1IYT and 11921 vs 28192 in 3LZM), large chi-square statistics (7.98 × 104 - 3.37 × 105), and persistent non-zero serial correlations (0.051 - 0.264), confirming structured temporal ordering in MD-derived descriptors that is not captured by standard feature plots alone. To facilitate reproducibility and broader application, we developed a dedicated Structured Entropy Analysis (SEA) software pipeline. This tool automates the entire workflow while simultaneously exporting data and figures. The software enables streamlined integration of SEA into molecular dynamics workflows, offering both accessibility and consistency across analyses. By bridging information theory and biomolecular modeling, this framework offers a lightweight, data-driven, and generalizable approach for enriching the interpretation of MD simulations, providing new opportunities for biomolecular classification, system comparison, and the development of entropy-informed descriptors.