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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.5K
4.5K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
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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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
15.4K
Protein Families02:47

Protein Families

17.3K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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Video Experimental Relacionado

Updated: Feb 28, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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Predicción de la Función de Proteínas Basada en Agentes LLM

Fernando Zhapa-Camacho1, Olga Mashkova1, Robert Hoehndorf2

  • 1Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Makkah Region, Saudi Arabia www.kaust.edu.sa.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un sistema de agentes de Modelo Lingüístico Grande (LLM) para la predicción mejorada de la función de proteínas. El novedoso enfoque sintetiza evidencia y conocimiento de múltiples fuentes para mejorar la precisión en biología computacional.

Palabras clave:
agentes LLMpredicción de función de proteínasbiología computacionalrazonamiento aumentado por conocimientosíntesis de evidencia

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Área de la Ciencia:

  • Biología computacional
  • Bioinformática
  • Inteligencia artificial en biología

Sus antecedentes:

  • La predicción de la función de proteínas es una tarea crítica pero desafiante en biología computacional.
  • Los métodos existentes a menudo tienen dificultades para integrar diversas fuentes de datos biológicos.
  • La anotación precisa de la función de proteínas es esencial para comprender los sistemas biológicos y los mecanismos de las enfermedades.

Objetivo del estudio:

  • Desarrollar y evaluar un sistema basado en agentes LLM para mejorar la predicción de la función de proteínas.
  • Aprovechar el razonamiento aumentado por conocimiento y la síntesis de evidencia multifuente para mejorar la precisión de la predicción.
  • Proporcionar predicciones transparentes y explicables documentando el proceso de razonamiento.

Principales métodos:

  • Integración de predicciones computacionales con metadatos de proteínas estructurados, literatura científica y conocimiento ontológico.
  • Un proceso de razonamiento multietapa que utiliza un agente LLM con herramientas especializadas para consultar, cotejar y verificar la plausibilidad.
  • Evaluación sistemática frente a métodos de referencia en subontologías de la Gene Ontology utilizando múltiples métricas de rendimiento.

Principales resultados:

  • El sistema de agentes LLM demostró un rendimiento superior en medidas dependientes del umbral en comparación con los métodos de referencia establecidos.
  • Se obtuvieron las puntuaciones Smin más bajas en todas las subontologías de la Gene Ontology evaluadas.
  • Se obtuvieron las mejores puntuaciones Fmax para las ontologías de Función Molecular y Componente Celular.

Conclusiones:

  • El sistema propuesto basado en agentes LLM mejora significativamente la precisión de la predicción de la función de proteínas.
  • El razonamiento aumentado por conocimiento y la síntesis de evidencia multifuente son estrategias efectivas para mejorar las predicciones biológicas.
  • El sistema ofrece un enfoque prometedor para avanzar en la investigación y las aplicaciones de la biología computacional.