An Untargeted NMR Metabolomics Platform for Functional Annotation of Natural Product Extracts.
An Untargeted NMR Metabolomics Platform for Functional Annotation of Natural Product Extracts.
批准号:
10261675
负责人:
Joseph Michael Egan
金额:
$0.42万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-01 至 2021-08-31
关键词:
CharacteristicsCollectionComplexComputer softwareConsensusConstitutionCrude ExtractsDataDatabasesDevelopmentDiagnosticEvaluationFamilyFingerprintFutureGenerationsGoalsIndividualInvestigationIonsLibrariesLiteratureMethodsModernizationMolecularNatural Product DrugNatural ProductsOrganismPatternPrevalenceProcessResearchResearch PersonnelSamplingSignal TransductionSourceStructureSystemTOCSYTechniquesTechnologyanalytical toolbasedesigndrug discoveryexperimental studyimprovedinnovationinsightinstrumentinterestion mobilityionizationmembermetabolomicsnovelpreventscaffoldsuccesstooltrend
中文摘要
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英文摘要
PROJECT SUMMARY
Investigations of natural products have, historically, been extremely challenging
due to the complexity of most natural product sources. Attempts at accurately
describing the constitution of complex these samples remains a challenge, despite
advancements in analytical technologies. Although no singular process exists to
solve this issue, combinations of data from orthogonal techniques can be
combined to improve the annotation of a mixture. Utilizing NMR as an analytical
platform, an approach was developed that integrates orthogonal information from
HSQC and TOCSY experiments, enabling the construction of spin system features
that can be used for identification and description of individual constituents in a
mixture. Using this approach, it is possible to network related constituents across
multiple samples to visualize relationships of the features aiding in further
hypothesis driven investigations. Approaching this problem from a metabolomics
perspective takes advantage of already existing natural product workflows that
may result in a large number of collections of source organisms. Further evaluation
of these networks show promise in accurately describing components in enough
detail to not only track their prevalence across extracts, but provide the
investigators with enough detail to know the molecular scaffold and structural
features of these metabolites prior to extensive chromatographic separation.
Future efforts and applications included in this proposal aim to connect the
structural information afforded by this NMR system to structurally determined
phenomena in other analytical platforms, predominantly with MS.
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