Função de avaliação dinâmica em algoritmos genéticos aplicados na predição de estruturas tridimensionais de proteínas / Genetic Algorithms with Dynamic Fitness Functions Applied to Tridimensional Protein Structure Prediction

AUTOR(ES)

Luís Henrique Uchida Ishivatari

FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

28/09/2012

RESUMO

The protein structure prediction can be seen as an optimization problem where given an amino acid sequence, the tertiary protein structure must be found amongst many possible by obtaining energy functions minima. Many researchers have been proposing Evolutionary Computation strategies to find tridimensional structures of proteins; however results are not always satisfactory since among other factors, there are always a great number of local optima in the search space. Usually, the fitness functions used by optimization algorithms are based on force fields with different energy terms with parameters from those terms being adjusted a priori, kept static through the optimization process. Some researchers suggest that the use of dynamic functions, i.e., that can be changed during the evolutionary process, can help the population to escape from local optima in highly multimodal problems. In this work we propose that the force field parameters can be changed during the optimization process of Genetic Algorithms (GAs) in the protein structure prediction problem, being increased or decreased, for instance, according with its influence on formation of secondary structures and its fine tuning. Since the cost function will be changed during the optimization process, the protein tridimensional structure prediction becomes a dynamic optimization problem and specific Genetic Algorithms for this kind of problem, like the hypermutation GA and random immigrants GA are investigated. We also propose a new metric related to the proteins secondary structure alignment to help the analysis of obtained data. Results indicate that the dynamic function algorithms obtained better results than static algorithms since changes on the fitness function allow the population to escape local optima, as well as an increase on the population diversity.

ASSUNTO(S)

algoritmos genéticos genetic algorithms predição de estruturas de proteínas protein protein structure prediction proteínas

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