With the development of high-resolution hybrid mass spectrometry platforms (e.g. Quadrupole Ion Trap/Fourier Transform-Ion Cyclotron Resonance and Quadrupole Ion Trap/Orbitrap), MS-based proteomics measurements can yield thousands of high-resolution mass spectra along with tens of thousands of MS/MS spectra from a single LC-MS/MS analysis. These hybrid instruments allow acquiring MS/MS data correlated with accurate precursor masses from high-resolution MS spectra (i.e. MS and MS/MS data produced simultaneously). This simultaneous approach of hybrid mass spectrometers greatly improves overall throughput by obtaining “free” MS/MS data simultaneous with MS data acquisition (i.e. no additional time to acquire MS/MS data).
A common experimental strategy using hybrid LC-MS/MS instruments requires the acquisition of a low resolution MS spectrum, obtained “on-the-fly” from a short time domain signal (ca. 180 ms, instead of utilizing full time domain signal of ca. 720 ms for the final spectrum), next this spectrum is analyzed for selection of precursor ions for MS/MS fragmentation in the ion trap. After the MS/MS analysis, m/z
information for the selected precursor ions is recorded in an exclusion list for a fixed time duration to prevent reacquisition of MS/MS spectra for the same precursor ion. The next MS/MS spectrum is then obtained for a different peptide, having lower intensities. While this data-dependent LC-MS/MS experimental design allows for many peptide ions to be analyzed and increases the chance of identifying low abundant peptides, it frequently selects peptide ions for fragmentation that are relatively “weak” (i.e. at an early point in LC elution time). Even though the corresponding MS/MS spectra of the weak precursor ions may be of sufficient quality due to the higher sensitivity of the MS/MS event over the MS measurement, as a result of the selective accumulation of ions over a narrow mass range in the ion trap, the lower MS peak intensity makes it likely that the determined monoisotopic mass of the precursor ion will be less accurate than otherwise achievable; i.e. the number of peptide ions in the MS spectrum is low, resulting in deviations from expected isotopic distributions. The resultant non-statistical distributions with potentially missing peaks can lead to errors in monoisotopic mass determination (especially for larger peptides). This behavior is referred to as the “1–Da problem.” Previously, we estimated that ~ 40% of precursor masses of conventionally processed tandem mass spectrometric data contained wrong monoisotopic mass information, which is attributed to this 1-Da problem.1
There have been several approaches for overcoming this issue.2,3,4,5,6,7,8,9
Gygi and coworkers used a relatively large mass tolerance (50 ppm) for the first database search and subsequently subjected the search results to more stringent filtering (8 ppm).4
This approach significantly reduced the false positive (FP) rate with only a minimal loss of peptide information. They also showed that accurate determination of precursor mass increased the number of phosphopeptide identification5
and improved the accuracy in stable-isotope-based quantitation.6
Similar results were obtained using a low resolution ion trap, but utilizing zoom scans in the data acquisition method.7
Mann and co-worker described MaxQuant, which collectively considers peptide features as three-dimensional objects in m/z
, elution time, and peak intensity, and determines accurate monoisotopic masses for the peptide features by calculating intensity-weighted averages of all MS peak centroids in the three-dimensional objects.8
By applying this method to high resolution Orbitrap data, mass accuracy in the sub-ppm range was achieved, greatly reducing the 1-Da problem. DeconMSn performs THRASH deisotoping to provide better precursor masses, and was shown to effectively reduce incorrect monoisotopic peak assignments. Additionally, DeconMSn performs a summation of MS spectra over the retention time period to provide isotopic distributions with better signal-to-noise ratios.9
It was also demonstrated that the use of summed spectra resulted in improved mass accuracies, along with an increased number of identified peptides over conventional Extract_MSn generation of DTAs. An independent approach, encoded in the software DtaRefinery10,11
, was demonstrated to eliminate systematic errors associated with monoisotopic masses of peptide identifications by using partial knowledge of peptide identifications and multiple regressions for mass accuracy dependencies on different experimental parameters.
PE-MMR was developed to provide accurate assignment of precursor masses to tandem mass spectrometry data.1
It first deisotopes every MS feature in the MS dataset using the THRASH algorithm12
and clusters the MS features of similar monoisotopic mass (within 10 ppm), but different LC elution times (defined by chromatographic peak width) into an unique mass class (UMC). 13
PE-MMR then uses Extract_MSn to generate MS/MS data (DTA files). It subsequently calculates neutral precursor masses from the MS/MS data (i.e. from [M+H]+
mass of DTA files) and performs subtraction of multiples of 1.00235 Da, up to three times.1
PE-MMR uses the resultant four possible precursor masses to find matches with the UMC mass, as calculated by intensity-weighted averaging of monoisotopic masses of component MS features in each UMC. When matches are found, PE-MMR replaces the precursor masses of the MS/MS data with UMC masses. By considering possibilities of selecting an incorrect isotopic mass (i.e. by considering original mass and −1/−2/−3 Da subtracted masses), and using experimentally observed mass information from a UMC, instead of using mass information from a single spectrum to represent the mass of a peptide, it demonstrated an effective solution to the 1-Da problem in many cases, as well as improvements in mass measurement accuracy for identified peptides.
Here, we report substantially improved results by combining DeconMSn, PE-MMR and DtaRefinery into an integrated data analysis pipeline for tandem mass spectrometric data. This combined method, coined “i
, uses DeconMSn to generate MS/MS data (DTA files) resulting in an initial correction of the 1-Da problem. It subsequently uses the DTA files to further correct and refine the precursor masses for the MS/MS data using a modified version of PE-MMR. The resultant refined DTA files are finally subjected to systematic error correction using DtaRefinery. Compared to the conventional method (Extract_MSn) or individual methods (DeconMSn or PE-MMR) of precursor mass correction, i
PE-MMR resulted in a 20% increase in the number of identified peptides, with a decrease in the false positive rate, and an increase in mass measurement accuracy.