Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | 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. |
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Andrade, Carolina Hortahttp://lattes.cnpq.br/2018317447324228Mottin, Melinahttp://lattes.cnpq.br/4062617848341219Andrade, Carolina HortaNeves, Bruno JuniorRodrigues, Daniel Alencarhttp://lattes.cnpq.br/5200741925289561Sousa, Bruna Katiele de Paula2023-02-16T11:23:04Z2023-02-16T11:23:04Z2020-03-17SOUSA, B. K. P. Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika. 2020. 77 f. Dissertação (Mestrado em Ciências Farmacêuticas) - Universidade Federal de Goiás, Goiânia, 2020.http://repositorio.bc.ufg.br/tede/handle/tede/12639Introduction: 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.Introdução: Os vírus Dengue (DENV) e Zika (ZIKV) são flavivírus que infectam milhões de pessoas todos os anos causando surtos e epidemias, com consequências brandas a graves nos pacientes infectados, podendo levar inclusive a morte. No entanto, não existem formas efetivas de prevenção ou tratamento das infecções causadas por estes vírus. Face ao exposto, é urgente a necessidade de se desenvolver fármacos antivirais para DENV e ZIKV. Objetivo: Este trabalho teve como objetivo identificar candidatos a fármacos antivirais inibidores das proteínas NS3 helicase e NS2B-NS3 protease de DENV e ZIKV, que são proteínas essenciais no ciclo de vida desses flavivírus, utilizando uma integração de estratégias computacionais e experimentais. Métodos: A presente dissertação está dividida em dois trabalhos. No primeiro trabalho, utilizou-se uma triagem virtual (VS) baseada em docking molecular e de modelos de aprendizado de máquina (AM) para priorizar compostos inibidores das proteínas helicase e protease de DENV. Foram desenvolvidos e validados modelos de AM binários para selecionar compostos potenciais com atividade contra essas proteínas de DENV. Na triagem virtual de compostos contra a protease de DENV, além de docking e dos modelos de AM, utilizou-se também modelos baseados na forma e volume moleculares (shape-based) gerados e validados a partir inibidores da NS2B-NS3 protease de DENV identificados na literatura. No segundo trabalho, utilizou-se a abordagem de docking molecular e modelos de AM para priorizar compostos contra NS2B-NS3 protease e NS3 helicase de ZIKV. Resultados: Ao final da triagem virtual realizada no trabalho 1, foi possível priorizar 19 compostos, sendo 9 compostos para NS3 helicase e 10 para NS2B-NS3 protease. Esses compostos foram adquiridos e estão em fase de avaliação experimental. No trabalho 2, foram selecionados 22 compostos no total, sendo 15 para NS3 helicase e 7 para NS2B-NS3 protease de ZIKV. Ensaios fenotípicos em células infectadas com ZIKV demostraram que 6 compostos apresentaram atividade inibitória contra o vírus e estão sendo validados em ensaios enzimáticos. Conclusão: Em conclusão, o presente trabalho demonstrou o potencial da integração de diversas técnicas computacionais e experimentais para acelerar a identificação de potenciais candidatos a fármacos antivirais contra os vírus DENV e ZIKV.Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2023-02-15T18:41:42Z No. of bitstreams: 2 Dissertação - Bruna Katiele de Paula Sousa - 2020.pdf: 3574154 bytes, checksum: f6f452290649418d2bf6f5bf09d9695e (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2023-02-16T11:23:04Z (GMT) No. of bitstreams: 2 Dissertação - Bruna Katiele de Paula Sousa - 2020.pdf: 3574154 bytes, checksum: f6f452290649418d2bf6f5bf09d9695e (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5)Made available in DSpace on 2023-02-16T11:23:04Z (GMT). No. of bitstreams: 2 Dissertação - Bruna Katiele de Paula Sousa - 2020.pdf: 3574154 bytes, checksum: f6f452290649418d2bf6f5bf09d9695e (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Previous issue date: 2020-03-17Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de GoiásPrograma de Pós-graduação em Ciências Farmacêuticas (FF)UFGBrasilFaculdade de Farmácia - FF (RMG)Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessDengueZikaDescoberta de fármacosQuimioinformáticaTriagem virtualDrug discoveryCheminformaticsVirtual screeningCIENCIAS BIOLOGICAS::FARMACOLOGIA::FARMACOLOGIA GERALEstratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e ZikaComputational and experimental approaches for identification of inhibitors of the helicase and protease proteins of Dengue and Zika virusesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis27500500500500225241reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.bc.ufg.br/tede/bitstreams/72ff74a0-b9c9-4b76-adea-c73b6f4aeaf5/download4460e5956bc1d1639be9ae6146a50347MD52ORIGINALDissertação - Bruna Katiele de Paula Sousa - 2020.pdfDissertação - Bruna Katiele de Paula Sousa - 2020.pdfapplication/pdf3574154http://repositorio.bc.ufg.br/tede/bitstreams/a97cece0-2f35-41d3-987a-82c698129c68/downloadf6f452290649418d2bf6f5bf09d9695eMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.bc.ufg.br/tede/bitstreams/821aa157-a3e2-4a40-b10b-b84a3f21e165/download8a4605be74aa9ea9d79846c1fba20a33MD51tede/126392023-02-16 08:23:04.253http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accessoai:repositorio.bc.ufg.br:tede/12639http://repositorio.bc.ufg.br/tedeRepositório InstitucionalPUBhttp://repositorio.bc.ufg.br/oai/requesttasesdissertacoes.bc@ufg.bropendoar:2023-02-16T11:23:04Repositório Institucional da UFG - Universidade Federal de Goiás (UFG)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 |
dc.title.pt_BR.fl_str_mv |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika |
dc.title.alternative.eng.fl_str_mv |
Computational and experimental approaches for identification of inhibitors of the helicase and protease proteins of Dengue and Zika viruses |
title |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika |
spellingShingle |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika Sousa, Bruna Katiele de Paula Dengue Zika Descoberta de fármacos Quimioinformática Triagem virtual Drug discovery Cheminformatics Virtual screening CIENCIAS BIOLOGICAS::FARMACOLOGIA::FARMACOLOGIA GERAL |
title_short |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika |
title_full |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika |
title_fullStr |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika |
title_full_unstemmed |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika |
title_sort |
Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika |
author |
Sousa, Bruna Katiele de Paula |
author_facet |
Sousa, Bruna Katiele de Paula |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Andrade, Carolina Horta |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2018317447324228 |
dc.contributor.advisor-co1.fl_str_mv |
Mottin, Melina |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/4062617848341219 |
dc.contributor.referee1.fl_str_mv |
Andrade, Carolina Horta |
dc.contributor.referee2.fl_str_mv |
Neves, Bruno Junior |
dc.contributor.referee3.fl_str_mv |
Rodrigues, Daniel Alencar |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/5200741925289561 |
dc.contributor.author.fl_str_mv |
Sousa, Bruna Katiele de Paula |
contributor_str_mv |
Andrade, Carolina Horta Mottin, Melina Andrade, Carolina Horta Neves, Bruno Junior Rodrigues, Daniel Alencar |
dc.subject.por.fl_str_mv |
Dengue Zika Descoberta de fármacos Quimioinformática Triagem virtual |
topic |
Dengue Zika Descoberta de fármacos Quimioinformática Triagem virtual Drug discovery Cheminformatics Virtual screening CIENCIAS BIOLOGICAS::FARMACOLOGIA::FARMACOLOGIA GERAL |
dc.subject.eng.fl_str_mv |
Drug discovery Cheminformatics Virtual screening |
dc.subject.cnpq.fl_str_mv |
CIENCIAS BIOLOGICAS::FARMACOLOGIA::FARMACOLOGIA GERAL |
description |
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. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-03-17 |
dc.date.accessioned.fl_str_mv |
2023-02-16T11:23:04Z |
dc.date.available.fl_str_mv |
2023-02-16T11:23:04Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
SOUSA, B. K. P. Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika. 2020. 77 f. Dissertação (Mestrado em Ciências Farmacêuticas) - Universidade Federal de Goiás, Goiânia, 2020. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/12639 |
identifier_str_mv |
SOUSA, B. K. P. Estratégias computacionais e experimentais para identificação de inibidores das proteínas helicase e protease dos vírus Dengue e Zika. 2020. 77 f. Dissertação (Mestrado em Ciências Farmacêuticas) - Universidade Federal de Goiás, Goiânia, 2020. |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/12639 |
dc.language.iso.fl_str_mv |
por |
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por |
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27 |
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500 500 500 500 |
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524 |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Universidade Federal de Goiás |
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Programa de Pós-graduação em Ciências Farmacêuticas (FF) |
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UFG |
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Brasil |
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Faculdade de Farmácia - FF (RMG) |
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Universidade Federal de Goiás |
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