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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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EvoZymePro-Cat: Un marco de aprendizaje profundo consciente de proteínas y ligandos para predecir efectos de

Ran Xu1, Xinkang Li1, Jianan Sui1

  • 1Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

ACS synthetic biology
|December 21, 2025
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Resumen
Este resumen es generado por máquina.

EvoZymePro-Cat (EZPro-Cat) es una plataforma de aprendizaje profundo que examina mutantes de enzimas. Predice con precisión la actividad enzimática relativa, mejorando el descubrimiento de enzimas y la evolución dirigida.

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BANLayerenzimarepresentación de fusióningeniería de proteínas

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Área de la Ciencia:

  • Biocatálisis e ingeniería de enzimas
  • Biología computacional y bioinformática
  • Aprendizaje automático en química

Sus antecedentes:

  • El diseño de enzimas es complejo debido al vasto espacio de secuencias y las interdependencias.
  • La predicción de la actividad de mutantes de enzimas es un desafío con los métodos convencionales.
  • El cribado preciso de mutantes de enzimas es crucial para la biocatálisis.

Objetivo del estudio:

  • Desarrollar una plataforma de aprendizaje profundo, EvoZymePro-Cat (EZPro-Cat), para el cribado eficiente de mutantes de enzimas.
  • Superar las limitaciones de la predicción de actividad absoluta con un marco de comparación por pares.
  • Mejorar el descubrimiento de enzimas y la evolución dirigida a través de un perfilado funcional mejorado.

Principales métodos:

  • Se desarrolló EvoZymePro-Cat (EZPro-Cat), una plataforma de aprendizaje profundo que integra datos de secuencia, estructura y ligando.
  • Se utilizó un marco de comparación por pares para predecir la superioridad de la actividad relativa de los mutantes.
  • Se empleó ESM1b para la codificación de secuencias de proteínas y MolT5/MACCS para la representación de ligandos.
  • Se integraron características estructurales y características evolutivas con mecanismos de atención bilineal para interacciones proteína-ligando.

Principales resultados:

  • El marco de comparación por pares demostró un rendimiento superior en la identificación de mutantes de enzimas mejorados.
  • Se logró una alta precisión de predicción (AUC 0.908) en conjuntos de datos de escaneo de mutaciones profundas utilizando una estrategia de aprendizaje de pocos ejemplos.
  • El modelo captura eficazmente las interacciones intermoleculares a largo plazo durante la catálisis.
  • Demostró un rendimiento superior en la predicción de efectos de mutaciones en la cinética enzimática (Km, kcat).

Conclusiones:

  • EvoZymePro-Cat (EZPro-Cat) proporciona una solución mecanicista y práctica para el perfilado funcional de enzimas.
  • La plataforma facilita el descubrimiento de enzimas y la evolución dirigida de alta eficiencia.
  • El enfoque de comparación por pares supera los errores sistemáticos en la predicción de actividad absoluta.
  • Las representaciones multimodales integradas mejoran la comprensión de las funciones de variantes intraproteínas.