Exact p-value calculation for XCorr scoring of high-resolution MS/MS data.

Area of Science:

• Proteomics
• Mass Spectrometry
• Computational Biology

Background:

• Peptide-spectrum match (PSM) scoring is crucial for identifying peptides in MS/MS data.
• Existing exact p-value (XPV) methods struggle with high-resolution mass spectrometry data due to binning inaccuracies.
• Dot product-like scores (e.g., XCorr) are common but require accurate calibration.

Purpose of the Study:

• To develop and validate a novel method for calibrating PSM scores using high-resolution MS/MS data.
• To address the limitations of standard XPV methods in handling high-resolution fragmentation patterns.
• To enhance the accuracy and reliability of peptide identification in mass spectrometry.

Main Methods:

• Introduced the high-resolution exact p-value (HR-XPV) method, an extension of XPV.
• Incorporated remainder mass tracking throughout fragmentation to improve fragment bin assignment.
• Applied HR-XPV to calibrate dot product-like scores (e.g., XCorr) for high-resolution spectra.
• Validated the method using four distinct mass spectrometry datasets.

Main Results:

• HR-XPV effectively calibrates PSM scores for high-resolution MS/MS spectra.
• The method accurately assigns fragments to correct bins, leveraging high-resolution data.
• Experimental results demonstrate well-calibrated scores across diverse datasets.
• Improved spectrum annotations were observed at all tested false discovery rate levels.

Conclusions:

• HR-XPV provides a robust solution for calibrating PSM scores in high-resolution mass spectrometry.
• The method enhances the trustworthiness of spectrum annotations by improving score calibration.
• HR-XPV enables more accurate peptide identification from modern, high-resolution mass spectrometry data.