Extracting Charge and Mass Information from Highly Congested Mass Spectra Using Fourier-Domain Harmonics
Native mass spectra of large, polydisperse biomolecules with repeated subunits, such as lipoprotein Nanodiscs, can often be challenging to analyze by conventional methods. The presence of tens of closely spaced, overlapping peaks in these mass spectra can make charge state, total mass, or subunit mass determinations difficult to measure by traditional methods. Recently, we introduced a Fourier Transform-based algorithm that can be used to deconvolve highly congested mass spectra for polydisperse ion populations with repeated subunits and facilitate identification of the charge states, subunit mass, charge-state-specific, and total mass distributions present in the ion population. Here, we extend this method by investigating the advantages of using overtone peaks in the Fourier spectrum, particularly for mass spectra with low signal-to-noise and poor resolution. This method is illustrated for lipoprotein Nanodisc mass spectra acquired on three common platforms, including the first reported native mass spectrum of empty “large” Nanodiscs assembled with MSP1E3D1 and over 300 noncovalently associated lipids. It is shown that overtone peaks contain nearly identical stoichiometry and charge state information to fundamental peaks but can be significantly better resolved, resulting in more reliable reconstruction of charge-state-specific mass spectra and peak width characterization. We further demonstrate how these parameters can be used to improve results from Bayesian spectral fitting algorithms, such as UniDec.
KeywordsNative mass spectrometry Nanodisc Fourier transform Deconvolution
Research reported in this publication was supported by the National Institutes of Health under Award Number R21AI125804 (to J.S.P.) and R01GM103479 and S10RR028893 (to J.A.L.) and the American Society for Mass Spectrometry Postdoctoral Research Award (to H.L.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to thank the Amgen Pharmacokinetics and Drug Metabolism group (PKDM) in South San Francisco for instrument time on the Orbitrap EMR.
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