Detalhes bibliográficos
| Ano de defesa: |
2020 |
| Autor(a) principal: |
Sousa, Bruna Katiele de Paula
 |
| Orientador(a): |
Andrade, Carolina Horta
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| Banca de defesa: |
Andrade, Carolina Horta,
Neves, Bruno Junior,
Rodrigues, Daniel Alencar |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de Goiás
|
| Programa de Pós-Graduação: |
Programa de Pós-graduação em Ciências Farmacêuticas (FF)
|
| Departamento: |
Faculdade de Farmácia - FF (RMG)
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://repositorio.bc.ufg.br/tede/handle/tede/12639
|
Resumo: |
Introduction: Dengue (DENV) and Zika (ZIKV) viruses are flaviviruses that infect millions of people each year causing epidemics, which can cause mild to severe consequences in infected patients and can even lead to death. However, to date there are no effective ways to prevent or treat infections caused by these viruses. Thus, the development of antiviral drugs for DENV and ZIKV is urgent. Objective: The aims is identify drug candidates against DENV and ZIKV NS3 helicase and NS2B-NS3 protease proteins, which are essential in the viral life cycle, using an integration of computational and experimental strategies. Methods: This dissertation was divided in two projects. In the first project a virtual screening (VS) based on molecular docking and machine learning models (ML) was used to prioritize compounds that inhibit DENV helicase and protease proteins. To do this, binary ML models were developed and validated to select potential compounds with activity against DENV proteins. In the virtual screening of compounds against DENV protease, in addition to docking and ML models, models based on the molecular shape and volume (shape-based) generated and validated from DENV NS2B-NS3 protease inhibitors identified in the literature were also used. In the second project, we used a molecular docking approach and ML models to prioritize compounds against ZIKV NS2B-NS3 protease and NS3 helicase. Results: At the virtual screening performed in project 1, it was possible to prioritize 19 compounds, 9 compounds for NS3 helicase and 10 for NS2B-NS3 protease. These compounds have already been purchased and are in the experimental evaluation phase. In project 2, 22 compounds were selected, 15 for NS3 helicase and 7 for NS2B-NS3 protease. Phenotypic assays in ZIKV infected cells, showed that 6 compounds demonstrated inhibitory activity against the virus and are being validated in enzymatic assays. Conclusion: the present work demonstrated the potential of integration of computational techniques and experimental assays to accelerate the identification of potential candidates for Dengue and Zika virus antiviral candidates. |
