Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm.

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.