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Multi-AOP: un marco de aprendizaje profundo multivisual ligero para el descubrimiento de péptidos antioxidantes

Jianxiu Cai1,2, Xinpo Lou1,3, Chak Fong Chong1,2

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macau SAR, China.

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Resumen

El descubrimiento de péptidos antioxidantes (AOP) es crucial para la salud y la conservación de alimentos. Un nuevo marco de aprendizaje profundo, Multi-AOP, identifica AOP de manera eficiente utilizando datos de secuencias y gráficos, superando los métodos existentes.

Palabras clave:
péptidos antioxidantesaprendizaje profundodescubrimiento de fármacosbioinformáticaquímica computacional

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Área de la Ciencia:

  • Bioquímica y Bioinformática
  • Química Computacional
  • Inteligencia Artificial en el Descubrimiento de Fármacos

Sus antecedentes:

  • Los péptidos antioxidantes (AOP) muestran potencial para la prevención de enfermedades y la conservación de alimentos debido a sus propiedades de eliminación de radicales libres.
  • Los métodos experimentales tradicionales para el descubrimiento de AOP son ineficientes y requieren muchos recursos.

Objetivo del estudio:

  • Desarrollar un marco computacional eficiente para el descubrimiento mejorado de péptidos antioxidantes.
  • Integrar información de secuencia y estructural para mejorar la precisión de la predicción de AOP.

Principales métodos:

  • Se desarrolló Multi-AOP, un marco de aprendizaje profundo multivisual ligero que utiliza Extended Long Short-Term Memory (xLSTM) para incrustaciones de secuencias.
  • Se empleó la Red Neuronal de Paso de Mensajes (MPNN) en representaciones SMILES para extraer características de gráficos moleculares, capturando propiedades fisicoquímicas.
  • Se implementó la fusión jerárquica de características de secuencia y gráficos para un análisis integral de péptidos.

Principales resultados:

  • Multi-AOP logró altas precisiones de predicción: 0.8043 (AnOxPePred), 0.9684 (AnOxPP) y 0.9043 (AOPP).
  • El marco superó consistentemente a los enfoques convencionales de aprendizaje automático y de aprendizaje profundo de última generación.
  • Se creó un conjunto de datos unificado de AOP para fomentar el desarrollo de modelos de predicción de AOP generalizables.

Conclusiones:

  • El marco Multi-AOP ofrece un avance significativo en el descubrimiento eficiente y preciso de péptidos antioxidantes.
  • La integración del aprendizaje de secuencias y gráficos proporciona un enfoque poderoso para predecir las funcionalidades de los péptidos.
  • Los conjuntos de datos y modelos de acceso público acelerarán la investigación futura en el desarrollo de AOP.