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Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)

Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to...

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Detalles Bibliográficos
Autores principales: Kleks, Guy, Holland, Darren C., Porter, Joshua, Carroll, Anthony R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372548/
https://www.ncbi.nlm.nih.gov/pubmed/34476071
http://dx.doi.org/10.1039/d1sc02940a
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author Kleks, Guy
Holland, Darren C.
Porter, Joshua
Carroll, Anthony R.
author_facet Kleks, Guy
Holland, Darren C.
Porter, Joshua
Carroll, Anthony R.
author_sort Kleks, Guy
collection PubMed
description Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to investigate diffusion coefficients of structurally diverse organic compounds such as natural products (NP). The experimental Ds derived from 55 diverse NPs has allowed us to establish a power law relationship between D and molecular weight (MW) and therefore predict MW from experimental D. We have shown that D is also affected by factors such as hydrogen bonding, molar density and molecular shape of the compound and we have generated new models that incorporate experimentally derived variables for these factors so that more accurate predictions of MW can be calculated from experimental D. The recognition that multiple physicochemical properties affect D has allowed us to generate a polynomial equation based on multiple linear regression analysis of eight calculated physicochemical properties from 63 compounds to accurately correlate predicted D with experimental D for any known organic compound. This equation has been used to calculate predicted D for 217 043 compounds present in a publicly available natural product database (DEREP-NP) and to dereplicate known NPs in a mixture based on matching of experimental D and structural features derived from NMR analysis with predicted D and calculated structural features in the database. These models have been validated by the dereplication of a mixture of two known sesquiterpenes obtained from Tasmannia xerophila and the identification of new alkaloids from the bryozoan Amathia lamourouxi. These new methodologies allow the MW of compounds in mixtures to be predicted without the need for MS analysis, the dereplication of known compounds and identification of new compounds based solely on parameters derived by DOSY NMR.
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spelling pubmed-83725482021-09-01 Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY) Kleks, Guy Holland, Darren C. Porter, Joshua Carroll, Anthony R. Chem Sci Chemistry Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to investigate diffusion coefficients of structurally diverse organic compounds such as natural products (NP). The experimental Ds derived from 55 diverse NPs has allowed us to establish a power law relationship between D and molecular weight (MW) and therefore predict MW from experimental D. We have shown that D is also affected by factors such as hydrogen bonding, molar density and molecular shape of the compound and we have generated new models that incorporate experimentally derived variables for these factors so that more accurate predictions of MW can be calculated from experimental D. The recognition that multiple physicochemical properties affect D has allowed us to generate a polynomial equation based on multiple linear regression analysis of eight calculated physicochemical properties from 63 compounds to accurately correlate predicted D with experimental D for any known organic compound. This equation has been used to calculate predicted D for 217 043 compounds present in a publicly available natural product database (DEREP-NP) and to dereplicate known NPs in a mixture based on matching of experimental D and structural features derived from NMR analysis with predicted D and calculated structural features in the database. These models have been validated by the dereplication of a mixture of two known sesquiterpenes obtained from Tasmannia xerophila and the identification of new alkaloids from the bryozoan Amathia lamourouxi. These new methodologies allow the MW of compounds in mixtures to be predicted without the need for MS analysis, the dereplication of known compounds and identification of new compounds based solely on parameters derived by DOSY NMR. The Royal Society of Chemistry 2021-07-15 /pmc/articles/PMC8372548/ /pubmed/34476071 http://dx.doi.org/10.1039/d1sc02940a Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Kleks, Guy
Holland, Darren C.
Porter, Joshua
Carroll, Anthony R.
Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)
title Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)
title_full Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)
title_fullStr Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)
title_full_unstemmed Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)
title_short Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)
title_sort natural products dereplication by diffusion ordered nmr spectroscopy (dosy)
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372548/
https://www.ncbi.nlm.nih.gov/pubmed/34476071
http://dx.doi.org/10.1039/d1sc02940a
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