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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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...
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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...
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MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Mass Spectrometry: Complex Analysis01:21

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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.
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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...
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Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation01:01

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The fragmentation patterns observed for compounds such as carboxylic acids, esters, and amides in the mass spectra include ⍺-cleavage and McLafferty rearrangement. Fragmentation by ⍺-cleavage preferentially occurs at the carbon-carbon bond at the ⍺-position next to the carboxylic group to generate a neutral radical and a cation. Long chain compounds with hydrogen at their γ-carbon undergo McLafferty rearrangement to give a radical cation and a neutral alkene.
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Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry.

John T Halloran1, Jeff A Bilmes1, William S Noble2

  • 1Dept. of Electrical Engineering University of Washington Seattle, WA 98195, USA.

Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence
|October 10, 2014
PubMed
Summary

We developed a new dynamic Bayesian network (DBN) method for identifying peptide spectra from tandem mass spectrometry (MS/MS). This generative approach models peptide fragmentation, learns alignment probabilities, and outperforms existing tools.

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Area of Science:

  • Proteomics
  • Computational Biology
  • Analytical Chemistry

Background:

  • Tandem mass spectrometry (MS/MS) is crucial for peptide identification in proteomics.
  • Accurate peptide-spectrum alignment is essential for reliable MS/MS data analysis.
  • Existing methods often lack the ability to learn from data or handle spectral complexities.

Purpose of the Study:

  • To introduce a novel peptide-spectrum alignment strategy using dynamic Bayesian networks (DBNs).
  • To develop a generative model that accurately represents peptide fragmentation in MS/MS.
  • To improve the accuracy and robustness of spectral identification compared to current state-of-the-art methods.

Main Methods:

  • Implementation of a dynamic Bayesian network (DBN) for peptide-spectrum alignment.
  • Modeling peptide fragmentation in MS/MS as a physical process within a generative framework.
  • Learning alignment probabilities from high-quality peptide-spectrum pairs and accounting for noise and missing peaks.

Main Results:

  • The DBN-based method demonstrates superior performance on a majority of tested datasets.
  • Outperformed several widely used, state-of-the-art database search tools for spectrum identification.
  • The generative structure provides unique insights not available from other methods.

Conclusions:

  • The proposed DBN strategy offers a powerful and extensible framework for MS/MS data analysis.
  • This generative approach enhances the accuracy of peptide identification by learning from data.
  • The method effectively handles spectral noise and missing theoretical peaks, leading to more reliable results.