A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

Detalhes bibliográficos
Autor(a) principal: Gaudêncio, Susana Pereira
Data de Publicação: 2020
Outros Autores: Pereira, Florbela
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/109143
Resumo: UIDB/50006/2020 UIDB/04378/2020 Norma transitória DL 57/2016
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spelling A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibitionactinomycetesdrug discoverymachine learning (ML) techniquesmain protease enzyme (Mpro)marine natural products (MNPs)molecular dockingquantitative structure–activity relationship (QSAR)severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)virtual screeningUIDB/50006/2020 UIDB/04378/2020 Norma transitória DL 57/2016The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure–activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.DQ - Departamento de QuímicaLAQV@REQUIMTEUCIBIO - Applied Molecular Biosciences UnitRUNGaudêncio, Susana PereiraPereira, Florbela2020-12-22T05:17:40Z2020-12-102020-12-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/109143eng1660-3397PURE: 26950082https://doi.org/10.3390/md18120633info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:49:31Zoai:run.unl.pt:10362/109143Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:49:31Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
title A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
spellingShingle A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
Gaudêncio, Susana Pereira
actinomycetes
drug discovery
machine learning (ML) techniques
main protease enzyme (Mpro)
marine natural products (MNPs)
molecular docking
quantitative structure–activity relationship (QSAR)
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
virtual screening
title_short A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
title_full A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
title_fullStr A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
title_full_unstemmed A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
title_sort A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition
author Gaudêncio, Susana Pereira
author_facet Gaudêncio, Susana Pereira
Pereira, Florbela
author_role author
author2 Pereira, Florbela
author2_role author
dc.contributor.none.fl_str_mv DQ - Departamento de Química
LAQV@REQUIMTE
UCIBIO - Applied Molecular Biosciences Unit
RUN
dc.contributor.author.fl_str_mv Gaudêncio, Susana Pereira
Pereira, Florbela
dc.subject.por.fl_str_mv actinomycetes
drug discovery
machine learning (ML) techniques
main protease enzyme (Mpro)
marine natural products (MNPs)
molecular docking
quantitative structure–activity relationship (QSAR)
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
virtual screening
topic actinomycetes
drug discovery
machine learning (ML) techniques
main protease enzyme (Mpro)
marine natural products (MNPs)
molecular docking
quantitative structure–activity relationship (QSAR)
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
virtual screening
description UIDB/50006/2020 UIDB/04378/2020 Norma transitória DL 57/2016
publishDate 2020
dc.date.none.fl_str_mv 2020-12-22T05:17:40Z
2020-12-10
2020-12-10T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/109143
url http://hdl.handle.net/10362/109143
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1660-3397
PURE: 26950082
https://doi.org/10.3390/md18120633
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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