Publicado em: 26/07/2012

Tese de Doutorado em Bioinformática

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

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

Título: MOIRAE: A Computational Strategy to Predict 3-D Structures of Polypeptides
Linha de Pesquisa: Bioinformática

Data: 02/08/2012
Hora: 8h45min
Local: Sala 220 (conselhos). Prédio 43412 – Instituto de Informática

Banca Examinadora:
Prof. Dr. Hugo Verli – Centro de Biotecnologia UFRGS
Prof. Dr. Leandro Krug Wives – UFRGS
Prof. Dr. Mario Inostroza Ponta – Universidad de Santiago de Chile – USACH

Presidente da Banca: Prof. Dr. Luis 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 presents a new computational 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 manipulates structural information from the PDB in order to generate torsion angles intervals. Torsion angles intervals are used as input to a genetic algorithm with a local-search operator in order to search the protein conformational space and predict its 3-D structure. Obtained results show that the 3-D structures obtained by the proposed method were topologically close to their correspondent experimental structure.

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