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Protein Networks

Protein Networks

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,...

Protein-protein Interfaces

Protein-protein Interfaces

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...

Protein-Protein Interfaces

Protein-Protein Interfaces

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

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.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

Conserved Binding Sites

Conserved Binding Sites

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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A Protocol for Computer-Based Protein Structure and Function Prediction

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PF-AGCN: una red convolucional de gráficos adaptativos para la predicción de la función basada en la interacción

Shumin Yang¹, Yuhan Su¹, Yuchen Lin¹

¹School of Electronic Science and Engineering, Xiamen University, 361005, Fujian, China.

Bioinformatics (Oxford, England)

|August 26, 2025

Resumen

Este resumen es generado por máquina.

La predicción de las interacciones proteína-proteína (IPP) es vital para comprender los procesos biológicos. Nuestro nuevo método PF-AGCN captura con precisión IPP complejos mediante la integración de funciones jerárquicas y gráficos de interacción de proteínas con técnicas avanzadas de aprendizaje profundo.

Palabras clave:

Gráfico de la red convolucional Predicción de la función de las proteínas Modelo de lenguaje de proteínas

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Área de la Ciencia:

La bioinformática
Biología computacional
Biología de sistemas

Sus antecedentes:

Las interacciones proteína-proteína (IPP) son fundamentales para los procesos biológicos.
La predicción precisa del IPP es esencial para comprender la función de las proteínas.
Los métodos existentes luchan por capturar la naturaleza compleja y jerárquica de los IPP.

Objetivo del estudio:

Desarrollar un marco avanzado de aprendizaje profundo para la predicción precisa de la interacción proteína-proteína.
Superar las limitaciones de los métodos existentes para capturar IPP jerárquicos y complejos.

Principales métodos:

Se propuso PF-AGCN, una red convolucional de gráficos adaptativos.
Se utilizaron dos estructuras de gráficos: un gráfico de funciones (términos de la ontología genética) y un gráfico de proteínas (interacciones directas).
Integró un modelo de lenguaje de proteínas con redes neuronales convolucionales causales dilatadas apiladas para el análisis de secuencia y estructura.

Principales resultados:

PF-AGCN demostró una precisión de predicción superior en experimentos extensos.
El método preserva efectivamente las estructuras biológicas originales mientras aprende nuevas relaciones.
Se logró una fusión sinérgica de la semántica de la secuencia global y los patrones estructurales locales.

Conclusiones:

PF-AGCN ofrece un avance significativo en la predicción de la interacción proteína-proteína.
La capacidad del marco para integrar diversos datos biológicos mejora el rendimiento de la predicción.
El modelo desarrollado proporciona una herramienta poderosa para la investigación biológica.