Increasing information from shotgun proteomic data by accounting for misassigned precursor ion masses

Area of Science:

• Proteomics
• Analytical Chemistry
• Mass Spectrometry

Background:

• High mass accuracy mass spectrometers are crucial for proteomics.
• Data-dependent acquisition in LC-MS/MS can misassign precursor ion masses due to insufficient ion statistics from narrow chromatographic peaks.
• Misassigned masses lead to errors in automated database searches and reduced peptide identifications.

Purpose of the Study:

• To develop and validate a post-acquisition computational approach for correcting misassigned monoisotopic precursor ion masses in LC-MS/MS data.
• To improve the accuracy and completeness of peptide identifications in complex proteomic mixtures.

Main Methods:

• Implemented a novel algorithm for peak detection across the entire chromatographic elution profile.
• Applied this algorithm to reprocess existing shotgun proteomic datasets.
• Corrected precursor ion monoisotopic masses based on integrated ion signals over the elution profile.

Main Results:

• Reprocessing data with the new method increased peptide sequence assignments by 10%.
• The estimated false positive ratio was reduced from 1% to 0.2%.
• Approximately 4% of salvaged identifications were attributed to the correction of mixed tandem mass spectra (accidental CID).

Conclusions:

• The proposed post-acquisition method effectively corrects misassigned precursor ion masses in LC-MS/MS data.
• This approach significantly enhances the accuracy and yield of peptide identifications in complex samples.
• The method also addresses issues arising from co-eluting peptides, further improving proteomic data reliability.