Predictor de interacciones entre estructuras secundarias de proteínas

• Julio César Quintana-Zaez ^[2] ; Nicolás Quintana-Bernabé ^[2] ; Raúl Giráldez Rojo ^[1] ; Reinaldo Molina-Ruiz ^[1] ; Cosme E. Santiesteban-Toca ^[3]
  1. [1] Universidad Central de Las Villas

     Universidad Central de Las Villas

     Cuba

     
  2. [2] Universidad Máximo Gómez Báez de Ciego de Ávila Facultad de Informática
  3. [3] Centro de Bioplantas Departamento de Informática

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• Localización: Revista Cubana de Ciencias Informáticas, ISSN-e 2227-1899, Vol. 11, Nº. 3, 2017
• Idioma: español
• Títulos paralelos:
  • Predictor of interactions between secondary protein structures
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• Dialnet Métricas: 2 Citas
• Resumen
  • English

    Los métodos de predicción de mapas de contacto son un paso intermedio para la predicción de estructuras de proteínas. A pesar de los avances logrados la precisión de las predicciones continúa por debajo del umbral deseado. Una vía mediante la cual el desempeño de estos métodos puede ser elevado es realizando la predicción de las interacciones entre estructuras secundarias. En este artículo se realiza un estudio de la influencia de las interacciones en el plegamiento de las proteínas. Donde, se propone un novedoso meta multiclasificador basado en árboles de decisión para predecir dichas interacciones. El método consiste en un esquema que combina el resultado de diferentes multiclasificadores especializados en las interacciones en el mapa de contacto final. El conjunto de proteínas empleado para validar el modelo contó con 2020 elementos y fue dividido en cuatro particiones, con respecto a su tamaño. La capacidad de generalización promedio alcanzada para los cuatro grupos de proteínas es de 51% de precisión, con una sensibilidad de 74%. El mejor desempeño del algoritmo se logró en proteínas de tamaño medio donde se alcanzó un 55% de precisión.

  • English

    The methods for the prediction of contact maps are an intermediate step for the prediction of protein structures. Despite the progress made, the accuracy of the predictions continues below the desired threshold. One way in which the performance of these methods can be heightened is by predicting the interactions between secondary structures. In this paper, we study the influence of interactions in the folding of proteins where we propose a novel multi-class goal based on decision trees to predict such interactions. The method consists of a scheme that combines the result of different specialized multiclassifiers into the interactions in the final contact map. The set of proteins used to validate the model counted on 2020 elements and were divided into four partitions, with respect to their size. The average generalization capacity achieved for all four protein groups is 51% accuracy, with a sensitivity of 74%. The best performance of the algorithm was attained in medium-sized proteins where 55% accuracy was achieved.

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