Detalhes bibliográficos
| Ano de defesa: |
2017 |
| Autor(a) principal: |
Ávila, Maurício Boff de
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| Orientador(a): |
Azevedo Junior, Walter Filgueira de
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| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
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| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Biologia Celular e Molecular
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| Departamento: |
Faculdade de Biociências
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| País: |
Brasil
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| Palavras-chave em Português: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tede2.pucrs.br/tede2/handle/tede/7395
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Resumo: |
Microorganisms, in general, are the major agents of disease in humans. Data from the Brazilian Ministry of Health show bacterial diseases as the main causes of death in the country. In the therapy of these organisms, antibiotics are considered the most successful chemotherapy methods of 21st century medicine, as they represent the first, and only, line of combat against bacterial diseases. The development of new antibiotic drugs is becoming increasingly necessary, as bacterial resistance rates become higher each year. At this point, shikimate pathway is attractive to this type of research, since it is considered an essential pathway for the maintenance of these organisms in the environment, besides being absent in animals. The pathway is responsible for the formation of chorismate, precursor of aromatic amino acids (Phe, Trp and Tyr), folic acid and ubiquinones in the groups of organisms that presents it. The third reaction of the shikimate biosynthetic pathway is performed by the enzyme DHQD. In this step the reversible dehydration of the DHQ molecule is performed aiming to transform into 3-dehydroshikimate, the focus reaction of this study. In the search for new DHQD inhibitors, docking simulations were performed against the three-dimensional structure of a target protein, since it is a process that seeks to find, among possible orientations/conformations of a ligand in the active site, the one that presents the lower binding energy and, consequently, greater affinity. In addition to the docking simulations, machine learning methods were used to formulate polynomial scoring functions, based on the MVD scoring functions, which were able to predict protein/binder affinity. At the end of all the simulations and tests carried out throughout the project, we conclude that the Polscore56 equation was the most skilled to predict the affinity between the active site of DHQD and tested compounds. For this polynomial, the results of test set (ρ = 0,900; p-value = 0,037), AUC (74,686%), EF1 (540) and EF2 (159,23) were, in most of the categories evaluated, the best, confirming the formulated hypotheses on the equation and indicating it for further studies with the enzyme. |
