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MOIRAE : a computational strategy to predict 3-D structures of polypeptides

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MOIRAE : a computational strategy to predict 3-D structures of polypeptides

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Título MOIRAE : a computational strategy to predict 3-D structures of polypeptides
Autor Dorn, Márcio
Orientador Lamb, Luis da Cunha
Co-orientador Buriol, Luciana Salete
Data 2012
Nível Doutorado
Instituição Universidade Federal do Rio Grande do Sul. Instituto de Informática. Programa de Pós-Graduação em Computação.
Assunto 3D
Algoritmos
Algoritmos geneticos
Bioinformática
Biologia molecular
[en] 3-D protein structure prediction
[en] Artificial neural networks
[en] GA local-search operator
[en] Genetic algorithms
[en] Structural bioinformatics
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. 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. Results show that the 3-D structures obtained by the proposed method were topologically comparable to their correspondent experimental structure.
Tipo Tese
URI http://hdl.handle.net/10183/142870
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