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Related Concept Videos

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: 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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Sample Preparation for Endopeptidomic Analysis in Human Cerebrospinal Fluid
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Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification.

Aaron A Klammer1, Sheila M Reynolds, Jeff A Bilmes

  • 1Department of Genome Sciences, University of Washington, Seattle, WA, USA.

Bioinformatics (Oxford, England)
|July 1, 2008
PubMed
Summary

This study introduces Riptide, a novel hybrid dynamic Bayesian network (DBN)/support vector machine (SVM) approach to improve protein identification using tandem mass spectrometry (MS/MS). Riptide enhances peptide identification accuracy by modeling fragmentation chemistry, increasing correct identifications by up to 12%.

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Sample Preparation for Endopeptidomic Analysis in Human Cerebrospinal Fluid
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Area of Science:

  • Proteomics
  • Computational Biology
  • Analytical Chemistry

Background:

  • Tandem mass spectrometry (MS/MS) is crucial for protein identification from complex mixtures.
  • Accurate peptide identification relies on understanding complex peptide fragmentation patterns, which are not fully exploited by current algorithms.

Purpose of the Study:

  • To develop an improved computational approach for peptide identification in MS/MS.
  • To enhance the accuracy and reliability of protein identification by better modeling peptide fragmentation.

Main Methods:

  • A hybrid dynamic Bayesian network (DBN)/support vector machine (SVM) model named Riptide was developed.
  • DBNs were trained on high-confidence peptide-spectrum matches to model peptide fragmentation chemistry.
  • SVMs were used to evaluate likelihood scores generated by Riptide for improved peptide identification.

Main Results:

  • Riptide identified new trends in peptide fragmentation, such as a-ion prevalence at cleavage sites.
  • The Riptide approach significantly improved discrimination between correct and incorrect peptide-spectrum matches.
  • This method increased positive identifications by up to 12% at a 1% false discovery rate compared to existing algorithms.

Conclusions:

  • The Riptide hybrid DBN/SVM model offers a more accurate and robust method for MS/MS-based protein identification.
  • This approach effectively leverages peptide fragmentation chemistry for enhanced proteomic data analysis.
  • The improved discrimination capabilities of Riptide advance the field of computational proteomics.