Electrospray-MS Charge Deconvolutions without Compromise an Enhanced Data Reconstruction Algorithm utilising Variable Peak Modelling

Transcription

1 Electrospray-MS Charge Deconvolutions without Compromise an Enhanced Data Reconstruction Algorithm utilising Variable Peak Modelling Overview A.Ferrige1, S.Ray1, R.Alecio1, S.Ye2 and K.Waddell2 1 PPL, Isleham, Cambs, UK.; 2 Applied Biosystems, Framingham, MA 171, USA Data reconstruction methods utilising peak models provide the most detailed results for charge deconvolutions. However, their quality will be compromised for high mass proteins unless the change in peak width with m/z is taken into account. The increased information content of zero-charge results is demonstrated for interferon and a large glycoprotein and this work shows the benefits of accounting for varying peak widths. A. Using a varying peak model as opposed to a constant model provides more reliable peak tables with smaller errors. B. Subsequent charge deconvolutions provide cleaner, more highly resolved zero-charge results with both more detail and improved mass errors. Introduction The peak width increases with m/z for ESI spectra of high mass proteins in both m/z units and sampling intervals. This change can be by up to at least a factor of 3 on quadrupole based systems. Time of Flight data are somewhat less affected due to the decrease in the number of points/da with increasing m/z and using an average model will frequently still provide excellent results. However, for heterogeneous high mass data there is the risk that where the peak width is narrow compared with the model, close or overlapped peaks at low m/z will not be resolved. At high m/z where the model may be far too narrow there is the risk that single peaks will be split into more than one component, potentially creating anomalies in the charge deconvolved result. The quality of charge deconvolutions is therefore compromised unless peak width variations are taken into account. In this work the ReSpect data reconstruction algorithm was modified to determine the way the peak profile parameters change with m/z and to accommodate the found peak width and shape variations. The benefits of this improved methodology are illustrated for two proteins. Results reported have been compared with data from the use of a single and constant peak model. Methods From two or more relatively crude estimates of the peak profile at different points in the data, the ReSpect algorithm is used to determine how the four peak parameters left width, right width, left shape & right shape that define a peak model change with m/z. To accomplish this, the data are first Fourier transformed to produce a decaying signal. As its starting point, the program computes the most likely position and intensity of the centroids that would be consistent with the data and the user models. The Fourier transform of the predicted centroids is a non-decaying signal. The convolution of this signal with the correct profile will provide the best possible fit to the data. The algorithm performs this task in a few iterations to provide a highly reliable estimate of the way the four peak profile parameters change with m/z. This knowledge is then used to perform a spectrum deconvolution that is not compromised by any peak width variation.

2 The data used to show the new technique in operation were obtained from a glycoprotein analysed on a QSTAR Pulsar Hybrid LC/MS/MS system in electrospray time of flight mode and from the protein called interferon analysed on a Finnigan quadrupole instrument. Results Results are presented in two parts Part 1 Part One shows the data from the Interferon protein. The Interferon was cloned from a single cell line that had been degraded (part of a stability trial) by heating in a moist atmosphere. Water is added to the molecule to give differences of ~18. As more water molecules are added, so the conformation changes allowing more to add on. The raw data in figure 1 shows the problem in trying to interpret this spectrum. The peaks resulting from the addition of the water are not clearly resolved from each other and the peaks tail off into the noise. The question is how many additions of water molecules are present. Figure 2 demonstrates the issue of an increasing number of data points across the peak as the mass range increases or as the charge state of the peak decreases. The top trace shows the raw data from the peak with z=19, the middle from z=13, the bottom from z=8. The number of data pts and hence the peak width alters by a factor of E Figure 1: Electrospray MS of Interferon

3 Data Pts = E Data Pts = E Data Pts = E Figure 2: Raw data showing peak width change in data points with m/z The comparison in the deconvolution of the data can be seen in figure 3. As the peak cluster used for the model for the constant model is that around m/z 164 (using a similar method to the FT technique mentioned in the Methods), the deconvolved result using the constant peak model is very accurate and provides a slightly sharper and a more confident peak assignment than for the variable model (where the model is derived from a low order polynomial describing the model parameters changing throughout the data). It should be noted that the peak definition in these deconvolved spectra is a representation of the confidence that the program has in the peak being a peak (treat the width as the programs assessment of the error in the peak and the total area as the original peak intensity). However when the program proceeds to deconvolve the spectra at the two ends of the charge states, the results are very different. At the low mass end or on the peaks at a charge state of 19 (Figure 4) the data shows a marked difference between the use of the variable peak model and the constant model. The constant model is too broad for the true peaks and has lost information in the raw data- the program tries to fit the data to too broad a peak width. In fact the repeat addition of 18 da is lost, which in turn will cause errors in the final calculation of a charge deconvolved spectrum.

4 Raw data Model parameters derived from all the data for this charge state 2.6E Variable Model deconvolved data 1.6E % Intensity Constant Model deconvolved data 2.7E Figure 3: Raw data comparison with deconvolution spectrum using a constant and variable model. Raw data 2.2E Mass (m/z) E+ Variable Model deconvolved data Mass (m/z) E+ Constant Model deconvolved data Loss of information Figure 4: Comparison of Raw data to deconvolved spectra at the low mass end (charge state z=19)

5 Figure shows the data at the other end of the mass spectrum on the peaks at charge state z=19. Here the program is trying to fit the data to too narrow a width, with the result that the peak confidence is lower (broader deconvolved result) and some peaks appear to split (trying to find too much in the data). Raw data 1.7E Variable Model deconvolved data 9.E Broad peaks low confidence Constant Model deconvolved data Split peak 3.E Figure : Comparison of Raw data to deconvolved spectra at the high mass end (charge state z=8) Figure 6 shows the charge deconvolved results on both the variable and constant model data. As mentioned because the data from using all the charge states provides misleading data at the ends of the charge state envelope, the data for only using 6 charge states is also presented for the constant model. As one can see, for like use of the number of charge states, the constant model yields increased peak errors and missing water molecule additions. Even with the reduced use of just 6 charge states, the peak errors are multiplied. The non use of the other data may affect interpretation of more complex data. Variable model 1 adjacent charge states Constant model 1 adjacent charge states Peaks missing Peak error larger Constant model 6 adjacent charge states All peaks found but variable errors Figure 6: Charge deconvolved results variable and constant models E+.6E+.6E+

6 Figure 7 shows how the variable model peak widths and shape change across the mass range. It should be of note that with the interferon data the major influence is on the peak width rather than the shape, the former changing by a factor of >2 whereas the peak shape alters by 1% and is insignificant. Note the peak model parameters used for the variable follow the black polynomial trend lines LW RW FullW LS RS Poly. (LS) Poly. (RS) mass Peak width change LW=Left width, RW=Right width, Full W=Full width mass Peak Shape change LS=Left shape, RS=Right shape Figure 7: Variable Model how the peak widths and shape varies across the mass range Table 1 & 2 show the mass assignments of the peaks at each charge together with the mass differences between adjacent peaks (the water molecule addition). It is clearly seen that the variable model allows the correct identification of peaks even at the extremes of the charge state envelope, whereas the constant model provides weak data interpretation particularly at higher charge states. The Std deviation figure for the mass difference between adjacent peaks is much higher ( x 2) using the constant model. Table 1: Mass Accuracy data for Variable model after charge deconvolution Interferon: Variable m odel. Prelim inary m odel m easured at all charges to dem onstrate principle Minim um adjacent charges = 1. Mass tolerance =.2 Peak Mass M Err Intensity Evidence (mass, charge...) Adjacent Adjacent Summed Summed Dif (Th) Dif (Fnd) Th-Fnd Dif (Th) Dif (Fnd) Th-Fnd Std Dev

7 Table 2: Mass Accuracy data for Constant model after charge deconvolution Interferon: Constant m odel. Prelim inary m odel m easured at m /z 164 Minim um adjacent charges = 6. Mass tolerance =.2 Peak Mass M Err Intensity Evidence (mass, charge...) Adjacent Adjacent Summed Summed Dif (Th) Dif (Fnd) Th-Fnd Dif (Th) Dif (Fnd) Th-Fnd Std Dev Part 2 Part two concerned the interpretation of a spectrum of a glycoprotein at mass 6,. The protein was known to contain up to 4 sites of glycosylation, where each site has a core of 2 GlcNac and 3 Mannose additions. The protein was assessed to include multiple fucose groups and varying Hex and HexNac additions. The raw electrospray data is shown in figure 8. The complexity of the sample is seen by the way the charge state groupings merge into one another. The resulting deconvolution and charge deconvolution of the data by use of the constant and variable peak model techniques are shown in figure 9. As with the interferon data the variable model technique obtains more information. Figures 1 and 11 show portions of the mass range corresponding to the charge state species for z=41 and 24 respectively. In each case the raw spectrum is shown at the top and the constant model deconvolved data in the middle and the variable model data at the bottom. With the z=41 data, the peak at 1469 is fitted to be one peak by the constant model method, whereas it becomes two peaks by the variable method. This is because the constant model is too broad to be able to discern the correct assessment of two peaks at this point. The z=24 data reveals the constant model to be too narrow causing peaks at m/z 29 to be split into 3 rather than the correct two (as seen using the variable model).

8 Figure 8: Electrospray MS of Glycoprotein B Constant Model data 3.8E Variable model data 7.2E More information Figure 9: Charge deconvolved data for Glycoprotein B

9 Glycoprotein B (z=41) Data; Constant Model; Variable Model m/z Figure 1: Portion of the spectrum at charge state 41. Glycoprotein B (z=24) Data; Constant model; Variable model m/z Figure 11: Portion of the spectrum at charge state 24. Tables 3 and 4 show the mass assignments and calculated errors for each of the peaks for the constant and variable model respectively. Each peak is also assigned to a glycosidic combination. The calculated theoretical mass for each glycoform is calculated and compared with recalibrated found peaks. The recalibration allows any systematic calibration error on the mass spectrometer to be eliminated and allows comparison with the mass error for each peak as determined by the ReSpectTM algorithm. The data clearly show that the variable model technique allows for the identification of many more glycoforms with a resulting improvement in the average Std Deviation over the constant model data.

10 Table 3: Constant model peak identification & assignment from the charge deconvolved result Table 4: Variable model peak identification & assignment from the charge deconvolved spectrum

11 Conclusion The use of a variable model to deconvolve the spectra result in the following advantages: 1. Improved mass assignments on found peaks 2. Improved detailed information content on the spectra, particularly on weak components - more interpretable spectra. 3. Reduction on the number of artefact peaks that occur as a result of over or under fitting data. Future work will include automating the ability to assess the variable model.