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Updated: Jan 13, 2026

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Un enfoque guiado por la estructura para la evaluación de variantes no codificantes para la unión de factores de

Lukas Gerasimavicius1, Simon C Biddie1,2, Joseph A Marsh1

  • 1MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom.

Nucleic acids research
|January 7, 2026
PubMed
Resumen

El modelado estructural utilizando AlphaFold 3 y FoldX ofrece información sobre variantes no codificantes que afectan la unión de factores de transcripción. Este enfoque, que evalúa las puntuaciones de modelado de plantillas predichas de interfaz (ipTM), complementa los métodos basados en secuencias para el análisis de variantes de enfermedades.

Palabras clave:
modelado estructuralvariantes no codificantesunión de factores de transcripciónAlphaFold 3FoldXgenómicabiología estructuralbioinformática

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Área de la Ciencia:

  • Genómica
  • Biología Estructural
  • Bioinformática

Sus antecedentes:

  • Las variantes de un solo nucleótido (SNV) no codificantes pueden alterar la expresión génica al afectar la unión del factor de transcripción (TF), contribuyendo a enfermedades.
  • Los métodos de predicción actuales basados en secuencias para la unión de TF tienen limitaciones, incluida la dependencia de los datos de entrenamiento y los sesgos específicos de TF.

Objetivo del estudio:

  • Desarrollar y evaluar un enfoque guiado por la estructura para predecir el impacto de las SNV no codificantes en la unión de TF.
  • Evaluar la utilidad de AlphaFold 3 (AF3) y FoldX en el modelado de complejos TF-ADN y la evaluación de efectos de variantes.

Principales métodos:

  • Se utilizó AlphaFold 3 (AF3) para modelar complejos de factores de transcripción-ADN.
  • Se empleó FoldX para la evaluación basada en la física de los efectos de variantes en la afinidad de unión de TF.
  • Se compararon las predicciones con datos experimentales de SNP-SELEX para seis factores de transcripción.

Principales resultados:

  • La estrategia basada en FoldX mostró una buena concordancia con las preferencias experimentales de alelos.
  • Las puntuaciones de modelado de plantillas predichas de interfaz (ipTM) de AlphaFold 3 se alinearon estrechamente con los datos experimentales, superando a menudo las métricas basadas en energía.
  • El análisis combinado de ΔipTM y las energías de FoldX mejoró la fiabilidad de las variantes asociadas a enfermedades.

Conclusiones:

  • El modelado estructural proporciona información interpretable sobre cómo las variantes no codificantes influyen en la unión de TF.
  • El enfoque propuesto guiado por la estructura ofrece un método de evaluación complementario para variantes regulatorias.
  • Destaca el potencial y las limitaciones de AF3 para analizar variantes no codificantes que impactan la unión de TF.