Database search strategies for proteomic data sets generated by electron capture dissociation mass spectrometry

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Electron capture dissociation (ECD) generates rich data but requires specialized analysis.
• Existing protein database search engines are optimized for lower-resolution CID data, not FT-ICR ECD data.
• ECD spectra exhibit unique characteristics like z-prime/z-dot ions and neutral losses, differing from CID.

Purpose of the Study:

• To optimize post-acquisition analysis of FT-ICR ECD mass spectra for improved peptide identification.
• To evaluate the impact of pre-search processing, database searching, and post-search processing on identification rates.
• To enhance the identification of doubly charged precursors in ECD fragmentation data.

Main Methods:

• Developed and applied a pre-search processing algorithm for ECD mass spectra.
• Compared Mascot and OMSSA search engines for ECD data analysis.
• Evaluated the effect of different post-acquisition analysis stages on peptide identification success.

Main Results:

• Optimized analysis increased peptide identifications from 630 (19%) to 1643 (49%) for a mouse whole cell lysate dataset.
• Post-search processing demonstrated the most significant impact on identification rates.
• The developed pre-search processing algorithm showed improvements, particularly for doubly charged precursors.

Conclusions:

• Systematic optimization of post-acquisition analysis is crucial for maximizing information from ECD mass spectrometry.
• The developed methods significantly enhance peptide identification rates, especially for challenging precursor charge states.
• This work provides a framework for more effective utilization of large-scale ECD proteomic datasets.