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Full-Featured Search Algorithm for Negative Electron-Transfer Dissociation.

Nicholas M Riley1,2, Marshall Bern3, Michael S Westphall2

  • 1Department of Chemistry, University of Wisconsin-Madison , Madison, Wisconsin 53706, United States.

Journal of Proteome Research
|July 13, 2016
PubMed
Summary
This summary is machine-generated.

New informatics tools enhance negative electron-transfer dissociation (NETD) analysis for broader proteome characterization. This advancement significantly improves peptide and protein identifications in negative-mode mass spectrometry.

Keywords:
activated ionmultiple proteasesnegative electron-transfer dissociationnegative modepeptide anionssearch algorithmtwo-dimensional target decoy

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Negative electron-transfer dissociation (NETD) is a powerful technique for analyzing peptide anions.
  • It provides access to challenging proteomic regions and acidic post-translational modifications.
  • Current informatics tools for NETD data analysis are limited, hindering whole-proteome scale characterization.

Purpose of the Study:

  • To implement NETD search capabilities into the Byonic platform.
  • To improve the sensitivity and scope of negative-mode data analysis.
  • To benchmark the performance of the enhanced Byonic platform using human embryonic stem cell data.

Main Methods:

  • Integration of NETD search functionality into the Byonic platform.
  • Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of tryptic peptides from human embryonic stem cells.
  • Reanalysis of existing large-scale, multienzyme negative-mode yeast proteome data.

Main Results:

  • Improved identification of spectra by up to 80% compared to existing methods.
  • Identification of 8665 unique peptides, 24,639 peptide spectral matches, and 1338 proteins in human embryonic stem cells.
  • Significant increases in peptide, peptide spectral match identifications, and protein sequence coverage for yeast proteome data.

Conclusions:

  • The new Byonic algorithm substantially enhances the sensitivity of NETD data analysis.
  • Combined advances in instrumentation and informatics tools significantly improve negative-mode proteome characterization.
  • This work expands the capabilities for discovering acidic post-translational modifications and challenging proteomic regions.