Publicado em: 24/04/2012

Proposta de Tese em Bioinformática

UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL
INSTITUTO DE INFORMÁTICA
PROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO
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DEFESA DE PROPOSTA DE TESE

Aluno: Márcio Dorn
Orientador: Prof. Dr. Luís Da Cunha Lamb
Coorientadora: Profa. Dra. Luciana Salete Buriol

Título: MOIRAE: A fast computational strategy to predict approximate 3-D structures of polypeptides

Linha de Pesquisa: Bioinformática

Data: 27/04/2012
Horário: 14h
Local: Sala ANFV (Anfiteatro Vermelho). Prédio 43412 – Instituto de Informática

Banca Examinadora:
Prof. Dr. Hugo Verli (Centro de Biotecnologia – UFRGS)
Prof. Dr. Leandro Krug Wives (Instituto de Informática – UFRGS)
Prof. Dr. Roberto da Silva (Instituto de Física – UFRGS)

Presidente da Banca: Prof. Dr. Luís Da Cunha Lamb

Abstract: Currently, one of the main research problems in Structural Bioinformatics is associated to the study and prediction of the 3-D structure of proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures have not followed the same growth trend. Currently, the number of protein sequences is much higher than the number of known 3-D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. This work present a new strategy for the 3-D protein structure prediction problem. A first principle strategy which uses database information for the prediction of the 3-D structure of polypeptides was developed. The proposed technique combines Artificial Neural Networks with a Genetic Algorithm strategy to search the protein conformational space in order to find the 3-D protein structure with the lowest potential energy. The obtained results shows that the 3-D structures obtained by the proposed method were topologically close to their correspondent experimental structure.

Keywords: 3-D protein structure prediction, artificial neural networks, genetic algorithms, structural bioinformatics.