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Deconvolution
What is ProMass?
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ProMass Applications
ProMass can be used to automatically deconvolute multiply-charged ESI mass spectra from a wide variety of biomolecules. A few of these application examples are listed below.
Quick Links:
- Proteins
- myoglobin, MW 16951.5 Da
- bovine serum albumin, MW 66430 Da
- human transferrin, MW 80 kDa
- yeast enolase, MW 46671 Da
- protein mix 5-30 kDa LC/MS data: insulin, RNAse A, ribonuclease B, carbonic anhydrase, lysozyme
- mixture example: the chromatogram from the above example processed as a single spectrum
- monoclonal antibody, human IgG-1 kappa, 150 kDa
- human growth hormone, 22 kDa
- mixture of porcine and bovine hemoglobins, 15-16 kDa
- Peptides
- Oligonucleotides
More information about the above examples are described in detail below.
- High-throughput oligonucleotide molecular weight confirmation:
With the new top-level summary page feature and color-coded results indication, ProMass can be used to analyze biomolecules in a high-throughput format. We have developed automated methods using LC/MS which perform rapid on-line desalting of oligos, allowing a sample to be analyzed every ~1 minute. Results color-coding, allows the analyst to focus quickly on the samples that did not provide the expected result. Click on one of the links below to access an example summary report.
- Peptide analysis:
ProMass can be used to process peptide mapping data to deconvolute spectra and generate reports. It can even be used to process data acquired using data-dependent scan modes (e.g., triple play on the LCQ). ProMass extracts only the full scan MS spectra and deconvolutes these scans. These data can be complementary to SEQUEST searches and zoom scan data, since many of the minor components which may be present in the full scan data may not have been acquired in MS/MS or zoom scan modes. A few examples are listed below:
- Protein/oligonucleotide LC/MS analysis:
ProMass can process data from either protein or oligonucleotide samples. Since the Xcalibur processing method defines how peaks are picked you can define almost any kind of peak picking scenario, for example: N biggest peaks, all peaks above a user-defined relative intensity threshold, etc. Peaks can be processed from a mass range chromatogram, TIC, or base peak chromatograms. A few examples are listed below, which show automated processing of the single biggest peak in the LC/MS run:
- Single Spectrum Processing:
You can also use ProMass to deconvolute individual spectra from either a text (mass intensity) file or from the Windows clipboard. This mode is useful for infusion data analysis. An HTML report is also generated in single spectrum mode, except without the chromatograms. Below are some examples from the single spectrum processing mode:
- Human Growth Hormone (hgh): This deconvolution is from the hgh_2.raw demo data set which is part of the Finnigan Bioworks package. You can use this data to see how ZNova compares with BioMass deconvolution.
- Mixture of pig and bovine hemoglobins: This spectrum was acquired at 3000 resolution on a TSQ7000. The spectrum highlights the capability of ZNova in dealing with complex mixtures. The major components at 15000 are separated by only 14 Da. We also used the handy exlusion mass list feature to exclude the heme peak at m/z 616 from the input data.
- Mixture Analysis:
ProMass does a very good job at sorting out mixtures. As an example, we took the LC/MS chromatogram of this protein mixture and summed all of the spectra from 6-13 min to produce a single input spectrum for ProMass. The results are shown here. As you can see, ProMass picked up all of the components that were present in the LC/MS data, even when the mixture was quite complex.
- Deconvolution of low S/N spectra:
ZNova is particularly effective in dealing with low S/N spectra. There are several built-in features that allow for this. The automatic baseline removal is very effective for removing the "hump" that is observed in very heterogeneous ESI spectra of larger proteins. The abililty to apply successive smoothing operations improves data quality. Finally, ZNova scoring allows one to get a measure of confidence about the validity of the deconvolution results. ZNova allows for maximum flexibility in allowing you to discern the information that you desire, quickly and automatically. For example, one may be interested in obtaining just the major components with a high confidence level. Alternatively, one may be interested in hunting for components just above the baseline noise. By appropriate setting of the ZNova parameters, one can accomplish either of these goals. Below are two examples, which show the effects of the parameter settings on the deconvolution of a very hetergeneous sample containing a 40 kD protein.
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