A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra

The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass spectra containing multiple charge states and potentially without isotopic resolution. Although the...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2020-03, Vol.92 (6), p.4395-4401
Main Author: Marty, Michael T
Format: Article
Language:English
Subjects:
Algorithms
Analytical chemistry
Charge distribution
Chemistry
Computer simulation
Data analysis
Deconvolution
Electrospraying
Ions
Mass spectra
Mass spectrometry
Mass spectroscopy
Pipelines
Proteins
Proteins - analysis
Proteomics
Smoothness
Spectrometry, Mass, Electrospray Ionization
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Summary:The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass spectra containing multiple charge states and potentially without isotopic resolution. Although there are multiple deconvolution algorithms, there is no consensus for how to judge the quality of the deconvolution, and many scoring schemes are not published. Here, an intuitive universal score (UniScore) for ESI deconvolution is presented. The UniScore is the weighted average of deconvolution scores (DScores) for each peak multiplied by the R 2 of the fit to the data. Each DScore is composed of separate components to score (1) the uniqueness and fit of the deconvolution to the data, (2) the consistency of the peak shape across different charge states, (3) the smoothness of the charge state distribution, and (4) symmetry and separation of the peak. Example scores are provided for a range of experimental and simulated data. By providing a means of judging the quality of the overall deconvolution as well as individual mass peaks, the UniScore scheme provides a foundation for standardizing ESI data analysis of larger molecules and enabling the use of ESI deconvolution in automated data analysis pipelines.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.9b05272