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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...
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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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Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry
11:54

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

BPDA2d--a 2D global optimization-based Bayesian peptide detection algorithm for liquid chromatograph-mass

Youting Sun1, Jianqiu Zhang, Ulisses Braga-Neto

  • 1Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, USA.

Bioinformatics (Oxford, England)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

BPDA2d improves peptide detection in mass spectrometry (MS) proteomics by modeling spectra as mixtures of candidate signals. This approach enhances the identification of low-abundance peptides and outperforms existing methods in accuracy and sensitivity.

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Area of Science:

  • Proteomics
  • Mass Spectrometry (MS)
  • Bioinformatics

Background:

  • Peptide detection is vital for mass spectrometry (MS)-based proteomics.
  • Existing algorithms struggle with overlapping peptides and error propagation due to greedy isotope template matching.
  • Current methods often analyze charge states separately, hindering low-abundance peptide detection.

Purpose of the Study:

  • To develop a novel algorithm, BPDA2d, for improved peptide detection in MS data.
  • To address limitations of existing methods in handling complex spectra and low-abundance peptides.

Main Methods:

  • BPDA2d models spectra as a mixture of candidate peptide signals, evaluating all combinations.
  • It integrates elution profiles, charge state distributions, and isotope patterns for signal inference.
  • The algorithm performs global optimization across all candidates and utilizes information across charge states.

Main Results:

  • BPDA2d enhances the identification of low-abundance peptides by integrating cross-charge state information.
  • The method outperforms state-of-the-art detection techniques in sensitivity and accuracy on both simulated and real LC-MS data.
  • BPDA2d provides posterior existence probabilities for detected peptides, enabling probability-based evaluation.

Conclusions:

  • BPDA2d offers a more robust and accurate approach to peptide detection in MS proteomics.
  • The algorithm's global optimization and cross-charge state analysis improve detection rates and reliability.
  • BPDA2d is available as a software package for broader application.