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Protein Networks02:26

Protein Networks

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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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein Networks02:26

Protein Networks

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Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
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MsipNet: un marco de aprendizaje de representación multiescala para predecir la interacción proteína-ARN

Nan Song1, Zhijin Li2, Yang Deng3

  • 1College of Artificial Intelligence, Nanjing Agricultural University, No. 666 Binjiang Avenue, Nanjing, Jiangsu 211800, China; Center for Data Science and Intelligent Computing, Nanjing Agricultural University, No. 666 Binjiang Avenue, Nanjing, Jiangsu 211800, China.

International journal of biological macromolecules
|December 25, 2025
PubMed
Resumen
Este resumen es generado por máquina.

MsipNet, un nuevo marco, predice con precisión las interacciones proteína-ARN (PRI) integrando datos de secuencia y estructurales. Esta herramienta mejora la comprensión de la regulación génica y los mecanismos de las enfermedades.

Palabras clave:
ConvoluciónMotivoInteracción proteína-ARNEstructura del ARN

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Área de la Ciencia:

  • Biología Molecular
  • Bioinformática
  • Biología Computacional

Sus antecedentes:

  • Las interacciones proteína-ARN (PRI) son cruciales para la regulación génica postranscripcional, afectando el empalme, la estabilidad y la traducción del ARN.
  • Comprender las PRI es vital para dilucidar las redes de regulación génica y los mecanismos de las enfermedades relacionados con mutaciones.
  • La identificación precisa de PRI une la investigación básica y las aplicaciones biomédicas.

Objetivo del estudio:

  • Desarrollar un marco computacional avanzado para predecir interacciones proteína-ARN.
  • Mejorar la precisión y eficiencia de la predicción de PRI utilizando una estrategia de aprendizaje multimodal.
  • Proporcionar una herramienta robusta para priorizar mutaciones funcionales y avanzar en estudios mecanicistas.

Principales métodos:

  • Introdujo MsipNet, un marco de aprendizaje de representación multiescala.
  • Integró características de secuencia de ARN globales y locales con información estructural.
  • Empleó una arquitectura híbrida que combina redes de memoria a corto plazo (LSTM) y módulos de convolución en forma de U con convolución dilatada (UCDC).

Principales resultados:

  • MsipNet superó a ocho métodos de vanguardia en 42 proteínas de unión a ARN (PBR) de seis líneas celulares.
  • Demostró un rendimiento superior en la predicción de preferencias de unión y la identificación de motivos de unión biológicamente validados.
  • Mostró una fuerte generalización en datos no vistos, manteniendo una alta eficiencia computacional.

Conclusiones:

  • MsipNet es una herramienta robusta e interpretable para la predicción de PRI.
  • El marco tiene un amplio potencial para estudios mecanicistas y aplicaciones biomédicas, incluida la priorización de mutaciones funcionales.
  • MsipNet avanza el campo de la biología computacional para comprender la regulación génica y la enfermedad.