Estimating influence of cofragmentation on peptide quantification and identification in iTRAQ experiments by...

Area of Science:

• Proteomics
• Mass Spectrometry
• Quantitative Biology

Background:

• Isobaric tag-based quantification (e.g., iTRAQ, TMT) offers high throughput in proteomics.
• Cofragmentation of peptides in mass spectrometry leads to inaccurate quantification and identification.
• Filtering spectra with low precursor isolation purity (PIP) reduces experimental coverage.

Purpose of the Study:

• To estimate the impact of cofragmentation on peptide quantification and identification in iTRAQ experiments.
• To develop a method for calculating corrected cutoff values for identifying differentially expressed peptides (DEPs) from cofragmented spectra.
• To reduce false positives in DEP identification using isobaric labeling techniques.

Main Methods:

• Generated multiplexed spectra with varying PIP by mixing 4-plex iTRAQ-labeled gastric tumor-normal tissue pair MS/MS spectra.
• Evaluated peptide identification accuracy using three database search engines (MODa, MS-GF+, Proteome Discoverer).
• Estimated quantification distortions and developed a corrected cutoff calculation method based on PIP and error probabilities.

Main Results:

• Over 99% of multiplexed spectra with PIP > 80% were correctly identified.
• Cofragmentation compressed cancer-to-normal expression ratios in 74% of spectra, with some showing ratio inflation.
• Applying corrected cutoffs to spectra with PIP >= 70% identified reliable DEPs, removing ~25% of potential false positives.

Conclusions:

• Cofragmentation significantly impacts peptide quantification accuracy in isobaric tag-based proteomics.
• The developed simulation and corrected cutoff calculation methods effectively reduce false positives in DEP identification.
• This approach enhances the reliability of quantitative proteomics using isobaric labeling techniques.