Improved Peak Detection and Deconvolution of Native Electrospray Mass Spectra from Large Protein Complexes.

Native electrospray-ionization mass spectrometry (native MS) measures biomolecules under conditions that preserve most aspects of protein tertiary and quaternary structure, enabling direct characterization of large intact protein assemblies. However, native spectra derived from these assemblies are often partially obscured by low signal-to-noise as well as broad peak shapes because of residual solvation and adduction after the electrospray process. The wide peak widths together with the fact that sequential charge state series from highly charged ions are closely spaced means that native spectra containing multiple species often suffer from high degrees of peak overlap or else contain highly interleaved charge envelopes. This situation presents a challenge for peak detection, correct charge state and charge envelope assignment, and ultimately extraction of the relevant underlying mass values of the noncovalent assemblages being investigated. In this report, we describe a comprehensive algorithm developed for addressing peak detection, peak overlap, and charge state assignment in native mass spectra, called PeakSeeker. Overlapped peaks are detected by examination of the second derivative of the raw mass spectrum. Charge state distributions of the molecular species are determined by fitting linear combinations of charge envelopes to the overall experimental mass spectrum. This software is capable of deconvoluting heterogeneous, complex, and noisy native mass spectra of large protein assemblies as demonstrated by analysis of (1) synthetic mononucleosomes containing severely overlapping peaks, (2) an RNA polymerase II/α-amanitin complex with many closely interleaved ion signals, and (3) human TriC complex containing high levels of background noise. Graphical Abstract ᅟ.

Deconvolution method for specific and nonspecific binding of ligand to multiprotein complex by native mass spectrometry.

In native mass spectrometry, it has been difficult to discriminate between specific bindings of a ligand to a multiprotein complex target from the nonspecific interactions. Here, we present a deconvolution model that consists of two levels of data reduction. At the first level, the apparent association binding constants are extracted from the measured intensities of the target/ligand complexes by varying ligand concentration. At the second level, two functional forms representing the specific and nonspecific binding events are fit to the apparent binding constants obtained from the first level of modeling. Using this approach, we found that a power-law distribution described nonspecific binding of α-amanitin to yeast RNA polymerase II. Moreover, treating the concentration of the multiprotein complex as a fitting parameter reduced the impact of inaccuracies in this experimental measurement on the apparent association constants. This model improves upon current methods for separating specific and nonspecific binding to large, multiprotein complexes in native mass spectrometry, by modeling nonspecific binding with a power-law function.