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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...
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MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry
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Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Published on: October 24, 2018

Protein identification using top-down.

Xiaowen Liu1, Yakov Sirotkin, Yufeng Shen

  • 1Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, San Diego, California 92093, USA.

Molecular & Cellular Proteomics : MCP
|October 27, 2011
PubMed
Summary
This summary is machine-generated.

MS-Align+ is a new algorithm that improves top-down mass spectrometry for identifying hundreds of proteins in complex samples. It significantly enhances protein identification and detects unexpected modifications, outperforming existing tools.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Top-down mass spectrometry (MS) has advanced, enabling analysis of complex samples and identification of thousands of proteins.
  • Current computational tools for database searching in top-down MS are underdeveloped, limiting comprehensive protein identification.
  • The need for efficient algorithms to analyze complex proteomic data and identify post-translational modifications is critical.

Purpose of the Study:

  • To introduce MS-Align+, a novel algorithm for rapid top-down protein identification using spectral alignment.
  • To develop a statistical method for evaluating the significance of top-down protein identifications.
  • To benchmark MS-Align+ against existing software (MASCOT, OMSSA, ProSightPC) using diverse proteomic datasets.

Main Methods:

  • Development of MS-Align+, a spectral alignment-based algorithm for top-down mass spectrometry data analysis.
  • Implementation of a statistical framework to assess the reliability of identified proteins.
  • Comparative analysis of MS-Align+ performance against MASCOT, OMSSA, and ProSightPC on Saccharomyces cerevisiae and Salmonella typhimurium datasets.

Main Results:

  • MS-Align+ significantly increased the number of identified spectra compared to MASCOT and OMSSA across both datasets.
  • MS-Align+ demonstrated comparable performance to ProSightPC on the Salmonella typhimurium dataset.
  • MS-Align+ outperformed ProSightPC on the more complex Saccharomyces cerevisiae dataset, highlighting its scalability.

Conclusions:

  • MS-Align+ represents a significant advancement in computational tools for top-down mass spectrometry.
  • The algorithm facilitates the identification of a greater number of proteins and enables the discovery of unexpected post-translational modifications.
  • MS-Align+ offers a robust and efficient solution for analyzing complex proteomic samples, advancing the field of proteomics.