Two-Dimensional Partial-Covariance Mass Spectrometry of Large Molecules Based on Fragment Correlations

Abstract

well-established technique used for the study of mechanisms of laser-induced molecular ionization and decomposition. It measures statistical correlations between fluctuating signals of pairs of detected species (ions, fragments, electrons). A positive correlation identifies pairs of products originating from the same dissociation or ionization event. A major challenge for covariance-mapping spectroscopy is accessing decompositions of large polyatomic molecules, where true physical correlations are overwhelmed by spurious signals of no physical significance induced by fluctuations in experimental parameters. As a result, successful applications of covariance mapping have so far been restricted to low-mass systems, e.g., organic molecules of around 50 daltons (Da). Partial-covariance mapping was suggested to tackle the problem of spurious correlations by taking into account the independently measured fluctuations in the experimental conditions. However, its potential has never been realized for the decomposition of large molecules, because in these complex situations, determining and continuously monitoring multiple experimental parameters affecting all the measured signals simultaneously becomes unfeasible. We introduce, through deriving theoretically and confirming experimentally, a conceptually new type of partial-covariance mapping-self-correcting partial-covariance spectroscopy-based on a parameter extracted from the measured spectrum itself. We use the readily available total ion count as the selfcorrecting partial-covariance parameter, thus eliminating the challenge of determining experimental parameter fluctuations in covariance measurements of large complex systems. The introduced selfcorrecting partial covariance enables us to successfully resolve correlations of molecules as large as 10 3-10 4 Da, 2 orders of magnitude above the state of the art. This opens new opportunities for mechanistic studies of large molecule decompositions through revealing their fragment-fragment correlations. Moreover, we demonstrate that self-correcting partial covariance is applicable to solving the inverse problem: reconstruction of a molecular structure from its fragment spectrum, within twodimensional partial-covariance mass spectrometry.

Proteomic Database Search Engine for Two-Dimensional Partial Covariance Mass Spectrometry

“…Two-dimensional partial covariance mass spectrometry (2D-PC-MS) is a new kind of twodimensional MS, based entirely on fragment-fragment correlations. 19,20 By mapping the fluc-tuations in fragment ion abundances across a series of repeated fragment mass spectra, 2D-PC-MS identifies fragment ions born in the same or consecutive decomposition reactions of the same parent molecular ion. This is in sharp contrast to the well familiar two-dimensional Fourier transform ion cyclotron resonance (2D FT-ICR) mass spectrometry, which identifies correlations between a parent ion and a fragment ion.…”

“…21 A significant fraction of the fragmentfragment correlations revealed by 2D-PC-MS have been shown to be much more structurally specific than the individual fragment ion signals of the 1D MS/MS. 19 The 2D spectral information accessible via 2D-PC-MS has been used to solve the long-standing problem of resolving diacetylated isomers of histone H3, 22 and its application to top-down measurements of intact proteins provides a straightforward means to deconvolve the overlapping fragment ion peaks of co-isolated intact proteins. 23 2D-PC-MS is based on calculation of the self-correcting partial covariance between the signals of the fragment ions X and Y across a large number of individual fragment mass spectra (e.g.…”

“…the partial covariance parameter derived from the spectrum itself, see Ref. 19 for details. The fragment ions X and Y produced in the same or in the consecutive decomposition processes are characterised by positive peaks of the self-correcting partial covariance (1) on the 2D-PC-MS map, symmetrical with respect to the m X /z X = m Y /z Y diagonal.…”

“…These various types of fragment-fragment correlations revealed by 2D-PC-MS are described in detail in Ref. 19 Efficient discrimination between the true low-intensity 2D-PC-MS correlations (X,Y) and statistical noise stemming from the finite number of scans used for the self-correcting partial covariance in Eq. (1) is achieved using the peak score, S(X, Y ),…”

“…The weights W (X i , Y i ) have been empirically optimised across a series of measurements of synthetic peptide sequences (see Table I of Ref. 19 ), resulting in: W (X i , Y i ) = 0.8 for type (i) correlations, W (X i , Y i ) = 0 for type (ii) correlations and W (X i , Y i ) = 1.0 for type (iii) correlations. The highest score for type iii) correlations is in agreement with the higher calculated estimated false positive rate for matching these correlations.…”