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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
Mass Spectrometry: Molecular Fragmentation Overview01:20

Mass Spectrometry: Molecular Fragmentation Overview

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.
One type of fragmentation pattern is the cleavage of a single bond in the molecular ion. The cleavage leads to a radical and a cation. The cleavage can occur at...

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  3. Predicting Discrete Structural Transformations In Small Molecules From Tandem Mass Spectrometry.
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  3. Predicting Discrete Structural Transformations In Small Molecules From Tandem Mass Spectrometry.

Related Experiment Video

Analyzing Large Protein Complexes by Structural Mass Spectrometry
15:35

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Published on: June 19, 2010

Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry.

Xianghu Wang, Gwendolyn Kiler, Daniela Herrera-Rosero

    Biorxiv : the Preprint Server for Biology
    |May 25, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    We developed the Spectrum Transformation Edit Predictor (STEP) to identify molecular structural changes from tandem mass spectrometry (MS/MS) data. STEP significantly improves metabolite annotation by predicting discrete transformations, accelerating new molecule discovery.

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    Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
    10:37

    Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

    Published on: November 15, 2017

    Related Experiment Videos

    Analyzing Large Protein Complexes by Structural Mass Spectrometry
    15:35

    Analyzing Large Protein Complexes by Structural Mass Spectrometry

    Published on: June 19, 2010

    Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
    10:37

    Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

    Published on: November 15, 2017

    Area of Science:

    • Analytical Chemistry
    • Computational Chemistry
    • Metabolomics

    Background:

    • Tandem mass spectrometry (MS/MS) generates spectral data crucial for molecular annotation.
    • High-throughput MS/MS data acquisition outpaces manual annotation, creating a bottleneck.
    • Existing computational methods like molecular networking offer similarity scores but limited actionable insights for structural elucidation.

    Purpose of the Study:

    • To develop a novel computational method for quantifying discrete structural transformations between molecules using MS/MS data.
    • To improve the accuracy and actionability of metabolite annotation in complex datasets.
    • To accelerate the discovery of new molecules from mass spectrometry data.

    Main Methods:

    • Introduced the Molecular Transformation Graph Edit Measure (MT-GEM) to quantify structural differences via graph edit distance.
  • Developed the Spectrum Transformation Edit Predictor (STEP), an ensemble machine learning model, to predict MT-GEM distances from MS/MS spectra.
  • Evaluated STEP's performance against state-of-the-art similarity metrics using benchmark datasets and human gut microbial data.

    • Main Results:

    • STEP achieved 48.4% average precision in identifying single structural transformations, a tenfold improvement over existing methods.
    • STEP identified three times more single-transformation metabolite pairs than feature-based molecular networking in gut microbial data.
    • STEP successfully identified novel drug metabolites and natural product analogs missed by conventional approaches.

    Conclusions:

    • MT-GEM and STEP provide discrete structural transformation predictions, enabling hypothesis-driven metabolite annotation.
    • The developed method significantly enhances the speed and accuracy of molecular discovery from MS/MS data.
    • This approach promises to accelerate the identification of new molecules in metabolomics research.