Predição da estrutura de proteínas off-lattice usando evolução diferencial multiobjetivo adaptativa

• Ano de defesa: 2014
• Autor(a) principal: Venske, Sandra Mara Guse Scós
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial
• Palavras-chave em Português: Proteínas - Estrutura - Modelos matemáticos; Proteínas - Conformação; Programação (Computadores); Processo decisório por critério múltiplo; Bioinformática; Simulação (Computadores); Engenharia elétrica; Proteins - Structure - Mathematical models; Proteins - Conformation; Computer programming; Multiple criteria decision making; Bioinformatics; Computer simulation; Electric engineering
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/946

Protein Structure Prediction (PSP) can be considered one of the most challenging problems in Bioinformatics nowadays. When a protein is in its conformation state, the free energy is minimized. Evaluation of protein conformation is generally performed based on two values of the estimated free energy, i.e., those provided by intra and intermolecular interactions among atoms. Some recent experimental studies show that these interactions are in conflit, justifying the use of multiobjective optimization approaches to solve PSP. In this case, the energy optimization is performed separately, different from the mono-objective optimization which considers the sum of free energy. Differential Evolution (DE) is a technique based on Evolutionary Computation and represents an interesting alternative to solve multiobjective PSP. In this work, an optimizer based on DE is proposed to solve the PSP problem. Due to the great number of parameters, typical for evolutionary algorithms, this work also investigates adaptive parameters strategies. In experiments, a simple approach based on ED is evaluated for PSP. An evolution for this method, which incorporates concepts of the MOEA/D algorithm and parameter adaptation techniques is tested for a set of benchmarks in the multiobjective optimization context. The preliminary results for PSP (for six real proteins) are promising and those obtained for the benchmark set stands the proposed approach as a candidate to the state-of-art for multiobjective optimization.