A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †

Detalhes bibliográficos
Autor(a) principal: Dias, Tiago
Data de Publicação: 2019
Outros Autores: Gaudêncio, Susana P., Pereira, Florbela
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.3390/md17010016
Resumo: Financial support from Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under Project PTDC/QUIQUI/119116/2010 and grants SFRH/BPD/108237/2015 (F.P.) and IF/00700/2014 (S.P.G.) are greatly appreciated. This work was supported by the LAQV, which is financed by national funds from FCT/MEC (UID/QUI/50006/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007265). This work was also supported by the UCIBIO, which is financed by national funds from FCT/MEC (UID/Multi/04378/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-010145-FEDER-007728). The NMR spectrometers are part of The National NMR Facility, supported by FCT (RECI/BBB-BQB/0230/2012).
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spelling A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †Antibacterial activityDrug discoveryMachine learning (ML) techniquesMarine natural products (MNPs)Marine-derived actinobacteriaMethicillin-resistant Staphylococcus aureus (MRSA)Molecular descriptorsNMR descriptorsQuantitative structure–activity relationship (QSAR)Drug DiscoveryFinancial support from Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under Project PTDC/QUIQUI/119116/2010 and grants SFRH/BPD/108237/2015 (F.P.) and IF/00700/2014 (S.P.G.) are greatly appreciated. This work was supported by the LAQV, which is financed by national funds from FCT/MEC (UID/QUI/50006/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007265). This work was also supported by the UCIBIO, which is financed by national funds from FCT/MEC (UID/Multi/04378/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-010145-FEDER-007728). The NMR spectrometers are part of The National NMR Facility, supported by FCT (RECI/BBB-BQB/0230/2012).The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R 2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data ( 1 H and 13 C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.UCIBIO - Applied Molecular Biosciences UnitLAQV@REQUIMTEDQ - Departamento de QuímicaRUNDias, TiagoGaudêncio, Susana P.Pereira, Florbela2019-09-18T22:51:27Z2019-01-012019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/md17010016eng1660-3397PURE: 14715929http://www.scopus.com/inward/record.url?scp=85059277666&partnerID=8YFLogxKhttps://doi.org/10.3390/md17010016info: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:RCAAP2024-03-11T04:36:24Zoai:run.unl.pt:10362/81695Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:36:07.920190Repositório 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-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
title A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
spellingShingle A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
Dias, Tiago
Antibacterial activity
Drug discovery
Machine learning (ML) techniques
Marine natural products (MNPs)
Marine-derived actinobacteria
Methicillin-resistant Staphylococcus aureus (MRSA)
Molecular descriptors
NMR descriptors
Quantitative structure–activity relationship (QSAR)
Drug Discovery
title_short A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
title_full A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
title_fullStr A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
title_full_unstemmed A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
title_sort A computer-driven approach to discover natural product leads for methicillin-resistant staphylococcus aureus infection therapy †
author Dias, Tiago
author_facet Dias, Tiago
Gaudêncio, Susana P.
Pereira, Florbela
author_role author
author2 Gaudêncio, Susana P.
Pereira, Florbela
author2_role author
author
dc.contributor.none.fl_str_mv UCIBIO - Applied Molecular Biosciences Unit
LAQV@REQUIMTE
DQ - Departamento de Química
RUN
dc.contributor.author.fl_str_mv Dias, Tiago
Gaudêncio, Susana P.
Pereira, Florbela
dc.subject.por.fl_str_mv Antibacterial activity
Drug discovery
Machine learning (ML) techniques
Marine natural products (MNPs)
Marine-derived actinobacteria
Methicillin-resistant Staphylococcus aureus (MRSA)
Molecular descriptors
NMR descriptors
Quantitative structure–activity relationship (QSAR)
Drug Discovery
topic Antibacterial activity
Drug discovery
Machine learning (ML) techniques
Marine natural products (MNPs)
Marine-derived actinobacteria
Methicillin-resistant Staphylococcus aureus (MRSA)
Molecular descriptors
NMR descriptors
Quantitative structure–activity relationship (QSAR)
Drug Discovery
description Financial support from Fundacao para a Ciencia e Tecnologia (FCT) Portugal, under Project PTDC/QUIQUI/119116/2010 and grants SFRH/BPD/108237/2015 (F.P.) and IF/00700/2014 (S.P.G.) are greatly appreciated. This work was supported by the LAQV, which is financed by national funds from FCT/MEC (UID/QUI/50006/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007265). This work was also supported by the UCIBIO, which is financed by national funds from FCT/MEC (UID/Multi/04378/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-010145-FEDER-007728). The NMR spectrometers are part of The National NMR Facility, supported by FCT (RECI/BBB-BQB/0230/2012).
publishDate 2019
dc.date.none.fl_str_mv 2019-09-18T22:51:27Z
2019-01-01
2019-01-01T00:00:00Z
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 https://doi.org/10.3390/md17010016
url https://doi.org/10.3390/md17010016
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1660-3397
PURE: 14715929
http://www.scopus.com/inward/record.url?scp=85059277666&partnerID=8YFLogxK
https://doi.org/10.3390/md17010016
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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
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