Differential Attenuation of NMR Signals by Complementary Ion‐Exchange Resin Beads for De Novo Analysis of Complex Metabolomics Mixtures

Zhang, Bo; Yuan, Jijun; Brüschweiler, Rafael

doi:10.1002/chem.201701572

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…DOSY-type experiments applied to complex mixtures, on the other hand, may not have sufficient resolution along the diffusion dimension to discriminate between different molecules. An approach to address this situation has been proposed recently, which is based on complementary ion exchange resin beads (e.g., 30–300 μm) to differentially attenuate 2D NMR cross-peaks that belong to different metabolites [ 46 ]. Based on their characteristic attenuation patterns, cross-peaks could be clustered and assigned to individual molecules, including metabolites with multiple spin systems, as demonstrated for a metabolite model mixture and E. coli cell lysate.…”

Section: Nanoparticle-metabolite Interaction Assist Metabolite Detmentioning

confidence: 99%

Nanoparticle-Assisted Metabolomics

et al. 2018

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Understanding and harnessing the interactions between nanoparticles and biological molecules is at the forefront of applications of nanotechnology to modern biology. Metabolomics has emerged as a prominent player in systems biology as a complement to genomics, transcriptomics and proteomics. Its focus is the systematic study of metabolite identities and concentration changes in living systems. Despite significant progress over the recent past, important challenges in metabolomics remain, such as the deconvolution of the spectra of complex mixtures with strong overlaps, the sensitive detection of metabolites at low abundance, unambiguous identification of known metabolites, structure determination of unknown metabolites and standardized sample preparation for quantitative comparisons. Recent research has demonstrated that some of these challenges can be substantially alleviated with the help of nanoscience. Nanoparticles in particular have found applications in various areas of bioanalytical chemistry and metabolomics. Their chemical surface properties and increased surface-to-volume ratio endows them with a broad range of binding affinities to biomacromolecules and metabolites. The specific interactions of nanoparticles with metabolites or biomacromolecules help, for example, simplify metabolomics spectra, improve the ionization efficiency for mass spectrometry or reveal relationships between spectral signals that belong to the same molecule. Lessons learned from nanoparticle-assisted metabolomics may also benefit other emerging areas, such as nanotoxicity and nanopharmaceutics.

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Section: Nanoparticle-metabolite Interaction Assist Metabolite Detmentioning

confidence: 99%

Nanoparticle-Assisted Metabolomics

et al. 2018

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“…Um die Identifizierung neuer Metabolite in biologischen Matrices zu verbessern, wurde eine Trennvorschrift vorgeschlagen, bei der die Lösung einer NMR‐Metabolomik‐Mischung durch eine Serie von Filtern gegeben wird, die entweder mit anionischen oder kationischen Ionenaustauscherharz‐Kügelchen (IERB, ion exchange resin beads) beladen sind. Auf diesem Wege werden die Metabolite nach ihren spezifischen physikochemischen Eigenschaften aufgetrennt . Daher können die 1 H‐ 13 C‐HSQC‐2D‐Spektren des Durchflusses und des Eluats für eine bessere Zuordnung der Metabolite verwendet werden.…”

Section: Technische Verbesserungenunclassified

Hochdurchsatz‐Metabolomik mit 1D‐NMR

et al. 2018

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Metabolomik befasst sich mit der Gesamtheit aller Metabolite, dem Metabolom. Als eine der “Omics”‐Wissenschaften hat sie Querbeziehungen zur Biologie, Physiologie, Pathologie und Medizin; andererseits sind Metabolite chemische Verbindungen, kleine organische Moleküle oder anorganische Ionen. Ihre korrekte Identifizierung und Quantifizierung in komplexen biologischen Matrizen erfordert daher eine solide chemische Grundlage. Im Vergleich beispielsweise zu DNA unterliegen Metabolite sehr viel stärker der Oxidation oder dem enzymatischen Abbau: Wir können große Teile eines Mammut‐Genoms aus einer kleinen Probe rekonstruieren, sind aber nicht in der Lage, dasselbe mit seinem Metabolom zu tun, das innerhalb weniger Stunden nach dem Tod des Tieres wahrscheinlich weitgehend abgebaut war. Erforderlich sind daher Standardverfahren, gute chemische Fertigkeiten der Probenvorbereitung für die Lagerung und anschließende Analyse, genaue analytische Protokolle, eine umfangreiche Kenntnis der Chemometrie, erweiterte statistische Verfahren und fundiertes Wissen über mindestens eine der beiden Metabolomik‐typischen Verfahren, MS oder NMR. All diese Fertigkeiten besitzen traditionell die Chemiker. Entsprechend betrachten wir die Metabolomik aus chemischer Perspektive und beschränken uns auf NMR. Der Analytiker findet Argumente für und gegen NMR, jedoch bietet das Verfahren eine besondere ganzheitliche Perspektive, die für künftige Anwendungen als eine bevölkerungsweite Screeningtechnik für Reihenuntersuchungen sprechen.

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“…Recent progress in machine learning has enabled the development of de novo molecule generators, [23][24][25][26][27] which are expected to design molecules with desired properties. 28 For instance, we developed a molecule generator, ChemTS, 27 which combines Monte Carlo tree search (MCTS) with a recurrent neural network (RNN), and successfully showed that ChemTS coupled with quantum chemical calculations can produce realistic molecules that have desired properties. 29 So far, most de novo molecule generators have only been tested or applied on quantifiable chemical properties such as HOMO-LUMO gaps.…”

Section: Introductionmentioning

confidence: 99%

NMR-TS: De Novo Molecule Identification from NMR Spectra

Zhang¹,

Terayama²,

Sumita³

et al. 2020

Preprint

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<div>NMR spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify any molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS. <br></div>

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Differential Attenuation of NMR Signals by Complementary Ion‐Exchange Resin Beads for De Novo Analysis of Complex Metabolomics Mixtures

Cited by 5 publications

References 21 publications

Nanoparticle-Assisted Metabolomics

Nanoparticle-Assisted Metabolomics

Hochdurchsatz‐Metabolomik mit 1D‐NMR

NMR-TS: De Novo Molecule Identification from NMR Spectra

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