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Related Concept Videos

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Mass Spectrometry: Molecular Fragmentation Overview01:20

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The ionization of a molecule into a molecular ion inside the mass spectrometer causes instability in the molecule's structure due to the loss of an electron. This eventually leads to the fragmentation or breaking of some bonds in the molecule. The fragmentation occurs predominantly at specific bonds to yield relatively stable fragments.
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Mass Spectrometry: Branched Alkane Fragmentation01:29

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This lesson delves into the mass spectrometry of branched alkane fragmentation. Branched alkanes possess secondary or tertiary carbon atoms, which generate relatively stable carbocations if the cleavage occurs at the branching point. The high stability of carbocations drives the instant fragmentation of branched alkanes. Accordingly, the branched alkane's molecular ion peak is very weak or invisible in the mass spectra, especially in comparison to a linear alkane.
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Mass Spectrometry: Aromatic Compound Fragmentation01:23

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Upon ionization, aromatic compounds generate a molecular ion that is observed as a prominent peak in their mass spectra. For example, the molecular ion peak for benzene appears at a mass-to-charge ratio of 78, while toluene is observed at a mass-to-charge ratio of 92. The molecular ion benzene is highly stable and does not readily undergo further fragmentation due to the significant amount of energy required to disrupt the aromatic stability of the benzene ring. In contrast, the molecular ion...
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Mass Spectrometry: Long-Chain Alkane Fragmentation01:18

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The molecular ions of linear alkanes prefer to fragment at the carbon-carbon bond away from the end of the chain since the cleavage of an inner bond creates a stable carbocation and a stable radical. Consequently, the mass signals of linear alkanes feature intense peaks in the middle of the mass-to-charge ratio plot with weaker peaks on either end. The fragmentation of each carbon-carbon bond with the release of a methyl group in each splitting leads to prominent peaks in the mass spectra...
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Mass Spectrometry: Alkene Fragmentation00:59

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Alkenes lose one electron from the unsaturated π bond upon ionization and form stable molecular ions. Further fragmentation of alkenes occurs through three different reaction pathways. The most prominent fragmentation is the cleavage at the allylic position. The resultant allylic carbocation is resonance stabilized. In the mass spectra of terminal alkenes, this fragment appears at a mass-to-charge ratio of 41. In the internal alkenes, where there are two choices of allylic cleavage, the...
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Metabolite identification through multiple kernel learning on fragmentation trees.

Huibin Shen1, Kai Dührkop2, Sebastian Böcker2

  • 1Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, GermanyDepartment of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.

Bioinformatics (Oxford, England)
|June 17, 2014
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Summary
This summary is machine-generated.

This study enhances metabolite identification in metabolomics by combining fragmentation trees with machine learning. The new approach significantly improves molecular fingerprint prediction, doubling the number of correctly identified metabolites.

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Area of Science:

  • Computational metabolomics
  • Bioinformatics
  • Mass spectrometry analysis

Background:

  • Metabolite identification from tandem mass spectrometry (MS/MS) data is crucial in metabolomics.
  • Existing computational methods include fragmentation tree analysis and machine learning for spectral-to-fingerprint mapping.
  • These methods aim to improve the accuracy of identifying molecular structures from mass spectral data.

Purpose of the Study:

  • To develop an improved computational method for metabolite identification using tandem mass spectrometry data.
  • To integrate fragmentation tree computations with kernel-based machine learning for enhanced molecular structure identification.

Main Methods:

  • A novel approach combining fragmentation tree computations with kernel-based machine learning was developed.
  • A new family of kernels was introduced to capture the similarity of fragmentation trees.
  • Multiple kernel learning approaches were utilized to combine these kernels for improved predictions.

Main Results:

  • The combined methods significantly improved the accuracy of molecular fingerprint prediction.
  • Experiments on two large reference datasets demonstrated the effectiveness of the new approach.
  • Metabolite identification accuracy was enhanced, doubling the number of top-ranked candidate metabolites.

Conclusions:

  • The integration of fragmentation tree similarity kernels and machine learning offers a powerful new tool for metabolite identification.
  • This approach substantially advances the capabilities of computational metabolomics.
  • The method shows significant promise for accelerating discoveries in biological and chemical research.