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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Updated: Sep 9, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Puntuación basada en el aprendizaje gráfico de estructuras complejas de ARN-proteína

Zheng Jiang1, Ye Zhang1, Guipu Yang1

  • 1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, P. R. China.

Journal of chemical theory and computation
|August 29, 2025
PubMed
Resumen
Este resumen es generado por máquina.

EGARPS + utiliza el aprendizaje profundo de gráficos para puntuar estructuras complejas de ARN-proteína, superando las CNN y los potenciales estadísticos. Este nuevo método mejora las predicciones, especialmente para complejos flexibles, y ayuda a la predicción de la estructura de novo.

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Área de la Ciencia:

  • Biología computacional
  • Bioinformática estructural
  • Aprendizaje automático en biología estructural

Sus antecedentes:

  • Las funciones de puntuación precisas son cruciales para predecir las estructuras complejas de ARN-proteína.
  • Los métodos tradicionales luchan con la flexibilidad conformacional.
  • Las redes neuronales convolucionales (CNN) son prometedoras, pero el aprendizaje profundo del gráfico ofrece un rendimiento superior para las tareas biomoleculares.

Objetivo del estudio:

  • Desarrollar una nueva función de puntuación basada en el aprendizaje de gráficos para estructuras complejas de ARN-proteína.
  • Mejorar la evaluación de las interacciones intermoleculares e intramoleculares dentro de estos complejos.
  • Mejorar la precisión y la robustez de la predicción de la estructura del ARN-proteína.

Principales métodos:

  • Propuso EGARPS+, un algoritmo de aprendizaje de gráficos que utiliza redes neuronales de gráficos equivalentes y mecanismos de atención.
  • Incorpora nuevas características de secuencia, estructura e interacción para la representación de la interfaz.
  • Se han desarrollado módulos intermoleculares e intramoleculares separados para una evaluación integral.

Principales resultados:

  • EGARPS+ superó sistemáticamente los métodos basados en CNN y los potenciales estadísticos tanto en conjuntos de datos vinculados como no vinculados.
  • El modelo demostró un rendimiento superior en complejos con cambios conformacionales significativos, interfaces pequeñas y baja similitud estructural.
  • EGARPS+ mejoró la predicción de nuevo del complejo ARN-proteína cuando se integró con RoseTTAFoldNA y AlphaFold3.

Conclusiones:

  • El aprendizaje profundo de gráficos, específicamente EGARPS+, ofrece un enfoque poderoso para la puntuación de estructuras complejas de ARN-proteína.
  • La capacidad del modelo para manejar casos complejos y mejorar las herramientas de predicción existentes destaca su importancia.
  • El análisis de interpretabilidad reveló la importancia de los motivos conservados y el enlace de hidrógeno en las interacciones ARN-proteína.