Structured Entropy Analysis (SEA): A computational framework for latent biomolecular insights beyond conventional
Ezekiel Edward Nettey-Oppong1, Emmanuel Essel Mensah2, Per Peter Badasu3
1Department of Biomedical Engineering, Yonsei University, Wonju, South Korea.
Journal of Molecular Graphics & Modelling
|February 28, 2026
Summary
Structured Entropy Analysis (SEA) quantifies molecular dynamics (MD) trajectory information beyond traditional metrics. This novel entropy-based framework reveals non-random patterns, offering deeper insights into biomolecular structural dynamics.
Area of Science:
- Computational Biology and Biophysics
- Information Theory in Molecular Modeling
Background:
- Conventional molecular dynamics (MD) analyses like radius of gyration (Rg), RMSD, and RMSF offer limited insights into complex biomolecular dynamics.
- There is a need for advanced analytical methods to capture the latent informational content and structural complexity within MD simulations.
Purpose of the Study:
- To introduce Structured Entropy Analysis (SEA), a novel entropy-based framework for quantifying informational content in MD trajectories.
- To demonstrate SEA's ability to uncover non-random entropic patterns indicative of underlying physical and functional characteristics.
- To develop a user-friendly software pipeline for reproducible and accessible SEA implementation.
Main Methods:
- Applied a multi-step pipeline including feature normalization, binary encoding, and entropy evaluation (Shannon entropy, min-entropy, bit balance, runs test, chi-square, serial correlation).
- Utilized ubiquitin (1UBQ), amyloid beta (Aβ42) peptide (1IYT), and T4 lysozyme (3LZM) as representative biomolecular systems.
- Developed and implemented a dedicated Structured Entropy Analysis (SEA) software pipeline for automated analysis and data export.
Main Results:
- SEA revealed significant deviations from randomness in MD trajectories, unlike conventional methods.
- Quantified structured temporal ordering through low observed runs, high chi-square statistics, and persistent serial correlations.
- SEA consistently detected non-random entropic patterns across all tested systems, correlating with physical and functional properties.
Conclusions:
- SEA provides a data-driven, generalizable approach to enrich the interpretation of MD simulations.
- The framework offers new opportunities for biomolecular classification, system comparison, and developing entropy-informed descriptors.
- The SEA software pipeline ensures accessibility, consistency, and reproducibility in advanced MD analysis.
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