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Análisis de Entropía Estructurada (SEA): Un marco computacional para obtener información biomolecular latente más

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

Resumen

El Análisis de Entropía Estructurada (SEA) cuantifica la información de la trayectoria de dinámica molecular (MD) más allá de las métricas tradicionales. Este marco novedoso basado en la entropía revela patrones no aleatorios, ofreciendo una visión más profunda de la dinámica estructural biomolecular.

Área de la Ciencia:

Biología Computacional y Biofísica; Teoría de la Información en Modelado Molecular

Sus antecedentes:

Los análisis convencionales de dinámica molecular (MD) como el radio de giro (Rg), RMSD y RMSF ofrecen información limitada sobre la dinámica biomolecular compleja.
Existe la necesidad de métodos analíticos avanzados para capturar el contenido informativo latente y la complejidad estructural dentro de las simulaciones de MD.

Objetivo del estudio:

Introducir el Análisis de Entropía Estructurada (SEA), un marco novedoso basado en la entropía para cuantificar el contenido informativo en trayectorias de MD.
Demostrar la capacidad de SEA para descubrir patrones entrópicos no aleatorios que indican características físicas y funcionales subyacentes.
Desarrollar un pipeline de software fácil de usar para la implementación reproducible y accesible de SEA.

Principales métodos:

Se aplicó un pipeline de varios pasos que incluye normalización de características, codificación binaria y evaluación de entropía (entropía de Shannon, min-entropía, balance de bits, prueba de series, chi-cuadrado, correlación serial).
Se utilizaron ubiquitina (1UBQ), péptido beta amiloide (Aβ42) (1IYT) y T4 lisozima (3LZM) como sistemas biomoleculares representativos.
Se desarrolló e implementó un pipeline de software dedicado de Análisis de Entropía Estructurada (SEA) para análisis automatizado y exportación de datos.

Principales resultados:

SEA reveló desviaciones significativas de la aleatoriedad en las trayectorias de MD, a diferencia de los métodos convencionales.
Cuantificó el orden temporal estructurado a través de pocas series observadas, altas estadísticas de chi-cuadrado y correlaciones seriales persistentes.
SEA detectó consistentemente patrones entrópicos no aleatorios en todos los sistemas probados, correlacionándose con propiedades físicas y funcionales.

Conclusiones:

SEA proporciona un enfoque generalizable y basado en datos para enriquecer la interpretación de las simulaciones de MD.
El marco ofrece nuevas oportunidades para la clasificación biomolecular, la comparación de sistemas y el desarrollo de descriptores informados por la entropía.
El pipeline de software SEA garantiza la accesibilidad, la coherencia y la reproducibilidad en el análisis avanzado de MD.

Palabras clave:

modelado biomolecular métricas de entropía teoría de la información simulación de dinámica molecular análisis de entropía estructurada (SEA)

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