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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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Updated: Jun 25, 2026

Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies
08:00

Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies

Published on: November 4, 2021

A ranking-based scoring function for peptide-spectrum matches.

Ari M Frank1

  • 1Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0404 La Jolla, California 92093-0404, USA. arf@cs.ucsd.edu

Journal of Proteome Research
|February 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach for scoring peptide-spectrum matches in proteomics data. This new method enhances the accuracy and efficiency of peptide identification in tandem mass spectrometry analysis.

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Last Updated: Jun 25, 2026

Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies
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Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies

Published on: November 4, 2021

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Tandem mass spectrometry (MS/MS) generates vast amounts of proteomics data requiring automated analysis.
  • Accurate peptide identification relies on effective scoring functions for peptide-spectrum matches (PSMs).

Purpose of the Study:

  • To develop a novel machine learning-based approach for scoring PSMs.
  • To improve the performance of de novo sequencing and database search algorithms in proteomics.

Main Methods:

  • Utilized a discriminative boosting-based machine learning algorithm for PSM scoring.
  • Developed diverse feature functions to assess various qualities of PSMs.
  • Integrated the new scoring function into the PepNovo+ software.

Main Results:

  • Significantly improved the performance of a de novo sequencing algorithm.
  • Substantially enhanced the performance of database search programs.
  • Enabled large-scale MS/MS analysis, including proteogenomic searches, with a 15x speedup and 60% increase in identified peptides compared to InsPecT.

Conclusions:

  • Machine learning ranking algorithms are effective for PSM scoring in proteomics.
  • The new scoring method enhances the efficiency and sensitivity of peptide identification.
  • The approach facilitates large-scale proteomics studies and proteogenomic analyses.