Precursor-ion mass re-estimation improves peptide identification on hybrid instruments.

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Mass spectrometry-based proteomics is crucial for biological system study.
• Accurate peptide identification relies on precise parent mass assignment.
• Existing methods show significant parent mass deviations, hindering confident protein identification.

Purpose of the Study:

• To introduce msPrefix, a novel program designed to re-estimate peptide parent masses.
• To improve the accuracy of parent mass estimation in mass spectrometry data.
• To enhance protein identification rates and confidence in complex biological mixtures.

Main Methods:

• Development of msPrefix software for parent mass re-estimation using high-accuracy survey spectra.
• Application of msPrefix to analyze 18-protein mixtures and complex biological samples (cell lysate, plasma).
• Comparison of peptide identification results using native data versus msPrefix-assisted data.

Main Results:

• msPrefix reduced parent mass deviations to an average of 1 ppm in mixture experiments.
• Protein identification rates increased substantially: 2295 to 4560 in cell lysate and 326 to 1216 in plasma.
• msPrefix identified MS/MS spectra potentially derived from multiple precursor ions, a common issue in complex samples.

Conclusions:

• msPrefix significantly improves peptide and protein identification accuracy and quantity in proteomics.
• The software addresses limitations in parent mass estimation, a critical step in data analysis.
• Integrating msPrefix into standard workflows offers a powerful enhancement for shotgun proteomics.