PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Accurate peptide precursor ion detection is vital for tandem mass spectrometry-based peptide identification.
• Utilizing multiple precursors per spectrum can improve identification numbers and spectrum explainability.
• Current methods may not fully leverage precursor information for comprehensive peptide discovery.

Purpose of the Study:

• To introduce PepPre, a novel method for detecting and scoring peptide precursor ions.
• To improve the accuracy and comprehensiveness of peptide identification in mass spectrometry.
• To evaluate PepPre's performance against existing methods on various peptide datasets.

Main Methods:

• PepPre decomposes spectral peaks into isotope clusters using linear programming.
• Detected precursors are scored and ranked to prioritize high-confidence candidates.
• The method was evaluated on regular and cross-linked peptide datasets, compared against 11 other methods.

Main Results:

• PepPre significantly increased peptide spectrum matches (PSMs) by 203% and peptide identifications by 68% for regular peptides.
• For cross-linked peptides, PepPre achieved 99% increase in PSMs and 27% in peptide pair identifications.
• PepPre outperformed all 11 compared methods and demonstrated reliability in identified results.
• Utilizing wide-window data acquisition with PepPre increased PSMs by at least 64%.

Conclusions:

• PepPre offers a substantial improvement in peptide identification rates and reliability in mass spectrometry.
• The method demonstrates the utility of advanced precursor detection and supports wide-window data acquisition strategies.
• PepPre is an open-source tool, facilitating broader adoption in proteomics research.