Heuristic charge assignment for deconvolution of electrospray ionization mass spectra

Area of Science:

• Analytical Chemistry
• Mass Spectrometry
• Biochemistry

Background:

• Electrospray ionization mass spectrometry (ESI-MS) generates multiply charged ions, complicating spectral interpretation.
• Deconvolution algorithms are crucial for identifying parent ions and determining molecular weights from complex ESI-MS data.
• Existing methods may struggle with accurate charge state assignment, impacting spectral clarity and quantitative accuracy.

Purpose of the Study:

• To develop and validate a new algorithm for ESI-MS deconvolution based on direct charge assignment.
• To evaluate the performance of entropy-based and multiplicative-correlation heuristics for charge assignment.
• To assess the algorithm's accuracy in deconvoluting single-component spectra and quantifying peptide mixtures.

Main Methods:

• A novel deconvolution algorithm directly assigns charge to signals at each mass-to-charge ratio (m/z).
• Two charge assignment heuristics were investigated: entropy-based and multiplicative-correlation.
• The algorithm was tested on spectra of human beta-endorphin and myoglobin, and a mixture of two peptides.

Main Results:

• The algorithm successfully produced clean deconvoluted spectra with a single dominant peak and minimal artifacts for both human beta-endorphin and myoglobin.
• The entropy-based heuristic demonstrated insensitivity to overestimates of maximum ion charge (zmax).
• Relative peak heights in the deconvoluted spectrum of a peptide mixture accurately reflected their concentrations, assuming equal ionization efficiency.

Conclusions:

• The proposed deconvolution algorithm offers a robust method for analyzing ESI-MS data.
• Direct charge assignment provides accurate spectral deconvolution and reliable quantification of peptide mixtures.
• This approach enhances the utility of ESI-MS for complex sample analysis.