Desenvolvimento de metodologias para predição de estruturas de proteínas independente de moldes

• Autor(a) principal: Rocha, Gregório Kappaun
• Data de Publicação: 2015
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do LNCC
• Texto Completo: https://tede.lncc.br/handle/tede/221
• topic: Biologia computacional; Predição de estruturas de proteinas; Modelagem molecular; Computational biology; Prediction of protein structure; Molecular modeling; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO::COMPUTABILIDADE E MODELOS DE COMPUTACAO

The protein structure prediciton problem (PSP) consists of discovering the native three-dimensional arrangement of a protein molecule using the information stored in its amino acid sequence. Unveiling the 3D structure of a protein is a way to obtain crucial information about its functions, given that the function of a protein is intrinsically related to its native three-dimensional structure. The experimental determination of the protein structure presents some technical difficulties and is also costly in workload and time. Thus, the investment in computational methods for PSP becomes imminent. This thesis has as main objective to increase the predictive ability of the GAPF protein structure prediction program and contribute to the advancement of theories and methodologies in the free-modeling prediction area. Efforts are directed on two fronts: (i) Improve the modeling of the energy function by the development and Implementing new potential for modeling the problem. (ii) To Increase the conformational search through the development and implementation of a multi-objective genetic algorithm. For the modeling of the problem, they were inserted in the function cost new ad hoc potentials that deal with hydrophobic compactation and with hydrogen bonds, key components in protein folding. For conformational search, a multiobjective steady-state genetic algorithm with phenotypic crowding was proposed. The new methodology was evaluated in a test set of 46 proteins, of all classes, and compared to consolidated methods in the literature, such as quark. The contributions of this thesis provided a major advance in the GAPF's predictive power, increasing the quality of the models and allowing investments in longer sequences. Advances have been notable in beta-sheets predictions, mainly due to the inclusion of hydrogen bonding potentials. Were made available also interesting tools for the future development of the program and GAPF was put as a good candidate for free-modeling predictions against prominent methodologies in the area.