Artículos

 

Mutating HIV Protease Protein Using Ant Colony Optimization and Fuzzy Cognitive Maps: Drug Susceptibility Analysis

 

Mutación de la proteína proteasa del VIH utilizando optimización basada en colonia de hormigas y mapas cognitivos difusos: análisis de susceptibilidad a fármacos

 

Isel Grau and Gonzalo Nápoles

 

Centro de Estudios de Informática, Universidad Central "Marta Abreu" de Las Villas, Cuba. igrau@uclv.edu.cu, gnapoles@uclv.edu.cu

 

Abstract

Understanding the dynamics of the resistance mechanisms in HIV proteins mutations is a key for optimizing the use of existing antiviral drugs and developing new ones. Several statistical and machine learning techniques have been proposed for predicting the resistance of a mutation to a certain drug using its genotype information. However, the knowledge publicly available for this kind of processing is majorly about resistant sequences, leading to highly imbalanced knowledge bases, which is a serious problem in classification tasks. In previous works, the authors proposed a methodology for modeling an HIV protein as a dynamic system through Fuzzy Cognitive Maps. The adjusted maps obtained not just allow discovering relevant knowledge in the causality among the protein positions and the resistant, but also achieved very competitive performance in terms classification accuracy. Based on these works, in this paper we propose an Ant Colony Optimization based method for generating possible susceptible mutations using the adjusted maps and biological heuristic knowledge. As a result, the mutations obtained allow drug experts to have more information of the behavior of the protease protein whenever a susceptible mutation takes place.

Keywords: HIV, drug resistance, mutations, fuzzy cognitive maps, modeling, ant colony optimization.

 

Resumen

El conocimiento de los mecanismos de resistencia en las mutaciones de las proteínas del VIH es fundamental para optimizar el uso de los fármacos existentes, así como diseñar nuevos medicamentos. Varias técnicas de estadística y aprendizaje automatizado han sido propuestas en la literatura para intentar predecir la resistencia de una mutación a un fármaco determinado usando su información genotípica. Sin embargo el conocimiento disponible públicamente para este tipo de procesamientos está enfocado mayormente a las mutaciones resistentes, lo que provoca bases de conocimiento altamente desbalanceadas que constituyen un serio problema en las tareas de clasificación. En trabajos previos, los autores proponen una metodología para modelar una proteína del VIH como un sistema dinámico a través de Mapas Cognitivos Difusos. Los mapas ajustados obtenidos no solo permiten descubrir conocimiento en la causalidad entre las posiciones de la proteína y la resistencia, sino que alcanza un desempeño competitivo en términos de exactitud de la clasificación. Basado en estos trabajos, en este artículo proponemos un método basado en la técnica de Optimización de Colonias de Hormigas para generar nuevas mutaciones susceptibles utilizando los mapas ajustados y conocimiento biológico heurístico. Como resultado, las mutaciones obtenidas permitirían a los expertos en fármacos contar con mayor información sobre el comportamiento de la proteasa cuando aparece una mutación susceptible.

Palabras clave: VIH, resistencia a fármacos, mutaciones, mapas cognitivos difusos, modelación, optimización basada en colonia de hormigas.

 

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