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A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis
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Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm.

Pratik Jagtap1, Sricharan Bandhakavi, LeeAnn Higgins

  • 1Minnesota Supercomputing Institute, Minneapolis, MN, USA. pratik@msi.umn.edu

Proteomics
|May 25, 2012
PubMed
Summary

MaxQuant raw data processing enhances Orbitrap data analysis by improving peaklist quality and spectral utilization. This workflow boosts protein identification, especially for modified peptides in complex samples like whole saliva.

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Area of Science:

  • Proteomics
  • Mass Spectrometry Data Analysis

Background:

  • Traditional analysis of LTQ Orbitrap data using ProteinPilot can be limited.
  • High mass accuracy data processing is crucial for comprehensive proteomic analysis.

Purpose of the Study:

  • To improve protein identification from LTQ Orbitrap mass spectrometry data.
  • To evaluate the combined utility of MaxQuant and ProteinPilot for complex proteomic datasets.
  • To explore enhanced analysis of modified peptides in whole saliva.

Main Methods:

  • Utilized MaxQuant for raw data processing of LTQ Orbitrap data, focusing on precursor-level high mass accuracy.
  • Employed ProteinPilot for subsequent analysis of MaxQuant-processed peaklists.
  • Applied complementary analysis workflows to a 3D fractionated whole saliva dataset (ProteoMiner).

Main Results:

  • MaxQuant processing improved peaklist quality, leading to enhanced spectral utilization and higher precision.
  • High precursor mass accuracy (HPMA) significantly benefited ProteinPilot analysis of Orbitrap data.
  • The combined workflow demonstrated advantages in identifying modified peptides, particularly after ProteoMiner treatment.

Conclusions:

  • Complementary data analysis approaches provide comprehensive results for complex proteomic datasets.
  • MaxQuant raw data processing is a valuable strategy for enhancing Orbitrap data analysis.
  • The established workflow enables improved protein and modified peptide identification from high mass accuracy data.