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STAG-LLM: Predicción de la unión TCR-pHLA con modelos de lenguaje de proteínas y estructuras 3D generadas

Jared K Slone1, Minying Zhang2, Peixin Jiang2

  • 1Computer Science, Rice University, Houston, 77005, TX, USA.

Computational and structural biotechnology journal
|January 16, 2026
PubMed
Resumen

La predicción de la unión del receptor de células T (TCR) y el péptido-HLA (pHLA) es crucial para la inmunoterapia. STAG-LLM, un nuevo modelo multimodal, utiliza estructuras 3D y secuencias para mejorar las predicciones de especificidad de unión, superando a los métodos existentes.

Palabras clave:
aprendizaje profundo geométricoInmunologíamodelo de lenguaje de proteínasProteómicabioinformática estructuralTCRHLA

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Área de la Ciencia:

  • Inmunología
  • Biología Computacional
  • Aprendizaje Automático

Sus antecedentes:

  • La unión del receptor de células T (TCR) y el péptido-HLA (pHLA) es vital para la inmunidad adaptativa.
  • La predicción precisa de la especificidad de unión ayuda al diseño de inmunoterapia personalizada.
  • Los métodos actuales utilizan principalmente secuencias de aminoácidos, descuidando la información estructural.

Objetivo del estudio:

  • Desarrollar un modelo multimodal de aprendizaje automático (ML) para la predicción de la especificidad de unión TCR-pHLA.
  • Integrar datos estructurales 3D con datos de secuencia para mejorar la precisión de la predicción.
  • Abordar los desafíos asociados con el uso de estructuras 3D generadas computacionalmente en canalizaciones de ML.

Principales métodos:

  • Desarrolló STAG-LLM, un modelo multimodal de ML que combina un modelo de lenguaje de proteínas y aprendizaje profundo geométrico.
  • Utilizó estructuras de proteínas 3D generadas computacionalmente junto con secuencias de aminoácidos.
  • Incorporó estrategias para gestionar los costos de inferencia, los datos de entrenamiento limitados y el ruido estructural.

Principales resultados:

  • STAG-LLM demostró un rendimiento superior en la predicción de la especificidad de unión TCR-pHLA en comparación con los métodos existentes.
  • El modelo logró una alta precisión incluso con conjuntos de datos de entrenamiento más pequeños.
  • Los experimentos de escaneo con alanina in vitro mostraron correlación con los pesos de atención del modelo, validando las predicciones.

Conclusiones:

  • STAG-LLM muestra un potencial significativo para la predicción de la unión TCR-pHLA basada en estructuras.
  • El modelo proporciona una base para avanzar en estudios inmunológicos y proteómicos utilizando estructuras 3D modeladas.
  • Se espera que la utilidad de STAG-LLM crezca con los avances en los modelos de lenguaje y estructuras de proteínas.