Spectral averaging with outlier rejection algorithms to increase identifications in top-down proteomics

Area of Science:

• Proteomics
• Mass Spectrometry
• Biochemistry

Background:

• Top-down proteomics relies on high-quality fragmentation spectra and accurate neutral mass for proteoform identification.
• Intact proteoform spectra are often complex, featuring overlapping signals, isotopic peaks, and multiple charge states, which reduce signal-to-noise ratios.
• Standard spectral averaging can introduce artifacts, degrading data quality and complicating downstream analyses like deconvolution.

Purpose of the Study:

• To develop and implement advanced algorithms for improving signal-to-noise ratios in mass spectrometry data.
• To enhance the identification and characterization of proteoforms using top-down proteomics.
• To integrate these novel algorithms into existing proteomics software for broader accessibility.

Main Methods:

• Implementation of outlier rejection algorithms for MS1 scans prior to spectral averaging.
• Application of averaging with rejection algorithms in the open-source proteomics search engine MetaMorpheus.
• Testing the algorithms on direct injection and online liquid chromatography mass spectrometry data.

Main Results:

• Averaging with rejection algorithms significantly improved spectral quality, particularly around isotopic envelopes.
• Demonstrated a 45% increase in the number of proteoforms detected in Jurkat T cell lysate.
• Validated the effectiveness of the algorithms in enhancing proteoform identification.

Conclusions:

• Outlier rejection algorithms are effective in overcoming limitations of standard spectral averaging in mass spectrometry.
• The implemented method robustly improves signal-to-noise ratios, leading to more comprehensive proteoform detection.
• This advancement offers a valuable tool for researchers in proteomics and related fields.