State-of-the-art native mass spectrometry (MS) methods have been developed for analysis of highly heterogeneous intact complexes and have provided much insight into the structure and properties of noncovalent assemblies that can be difficult to study using denatured proteins. These native MS methods can often be used to study even highly polydisperse membrane proteins embedded in detergent micelles, nanodiscs, and other membrane mimics. However, characterizing highly polydisperse native complexes which are also heterogeneous presents additional challenges for native MS. Macromolecular mass defect (MMD) analysis aims to characterize heterogeneous ion populations obfuscated by adduct polydispersity and reveal the... More
State-of-the-art native mass spectrometry (MS) methods have been developed for analysis of highly heterogeneous intact complexes and have provided much insight into the structure and properties of noncovalent assemblies that can be difficult to study using denatured proteins. These native MS methods can often be used to study even highly polydisperse membrane proteins embedded in detergent micelles, nanodiscs, and other membrane mimics. However, characterizing highly polydisperse native complexes which are also heterogeneous presents additional challenges for native MS. Macromolecular mass defect (MMD) analysis aims to characterize heterogeneous ion populations obfuscated by adduct polydispersity and reveal the distribution of "base" masses, and was recently implemented in the Bayesian analysis software UniDec. Here, we illustrate an alternative, orthogonal MMD analysis method implemented in the deconvolution program iFAMS, which takes advantage of Fourier transform (FT) to deconvolve low-resolution data with few user-input parameters and which can provide high quality results even for mass spectra with a signal-to-noise ratio of ∼5:1. Agreement between this method, which is based on frequency-domain data, and the mass-domain algorithm of UniDec provides strong evidence that both methods can accurately characterize highly polydisperse and heterogeneous ion populations. The FT algorithm is expected to be very useful in characterizing many types of analytes ranging from membrane proteins to polymer-conjugated proteins, branched polymers, and other large analytes, as well as for reconstructing isotope profiles for highly complex but still isotope-resolved mass spectra.
