Postexperiment Monoisotopic Mass Filtering and Refinement (PE-MMR) of Tandem Mass Spectrometric Data Increases Accuracy of Peptide Identification in LC/MS/MS

Abstract

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes ( MS has evolved into a robust and powerful tool for analyzing biomolecules. For example, MS is presently a preferred analytical tool for proteomics experiments in which dynamic populations of proteins are to be identified and quantified. In shotgun proteomics, hundreds or thousands of proteins are routinely identified through LC/MS/MS experiments using the proteolytic digests of proteome samples. During these LC/ MS/MS experiments, peptides are dynamically selected for fragmentation; the resultant tandem mass spectra (usually on the order of 10 5 spectra) are subsequently subjected to database searching (e.g. SEQUEST or Mascot) to best describe or annotate the peptide.Searching a large set of tandem mass spectra against a protein sequence database is a computationally expensive process; the resultant peptide spectral matches (PSMs) 1 are prone to false-positive (FP) identifications, which have led to the development of several validation methods (1, 2). Acquisition of high quality tandem mass spectra is critical to the whole process of protein identification through the shotgun approach because the measured fragment ion masses are compared with the theoretical values predicted by the search algorithms; any factors that adversely affect the acquisition of tandem mass spectra would undoubtedly lead to incorrect peptide assignments. Venable and Yates (3) systematically studied the effects that variability in tandem mass spectral quality had on peptide identification (i.e. peptide scores) and suggested an approach involving both presearch spectral averaging and postsearch XCorr averaging.Large portions (up to 90%) of tandem mass spectral data are not assigned to peptide sequences (4, 5) for many reasons, such as the presence of unknown protein sequences, the possibility of chemical (or post-translational) modification having occurred, insufficient spectral quality for high confi-

DtaRefinery, a Software Tool for Elimination of Systematic Errors from Parent Ion Mass Measurements in Tandem Mass Spectra Data Sets

“…Without such correction of the systematic errors, one would have to allow up to Ϯ10-ppm mass measurement error for identified peptides to retain the majority of the true identifications. Removal of the systematic error can be as simple as zero centering the entire histogram that is shifting by about Ϫ2.5 ppm with corresponding recalculation of parent ion masses, shifting and recalculating the parent ion masses for the individual LC-MS/MS data sets as suggested before (26), or as sophisticated as applying multidimensional non-parametric regression models to the individual data sets (Fig. 7B).…”

“…Initially, such recalibration approaches have been limited mostly to peptide identifications based on either high accuracy measurements of peptide masses alone (21) or in combination with LC retention times (13,20). Recently, we and others have proposed that partial sample knowledge can also be utilized for recalibrating parent ion masses in MS/MS data sets obtained on hybrid instrumentation (12,13,(23)(24)(25)(26). In one implementation, described as "postexperiment monoisotopic mass filtering and refinement" (26), the parent ion masses in the .dta files were replaced with the mass of the ion averaged over all scans in which it was observed followed by a simple recalibration.…”

“…Recently, we and others have proposed that partial sample knowledge can also be utilized for recalibrating parent ion masses in MS/MS data sets obtained on hybrid instrumentation (12,13,(23)(24)(25)(26). In one implementation, described as "postexperiment monoisotopic mass filtering and refinement" (26), the parent ion masses in the .dta files were replaced with the mass of the ion averaged over all scans in which it was observed followed by a simple recalibration. That recalibration assumes one constant value of the systematic error for the entire data set, which can be estimated by zero centering the parent ion MME distribution.…”

Quantitative Proteomics Reveals β2 Integrin-mediated Cytoskeletal Rearrangement in Vascular Endothelial Growth Factor (VEGF)-induced Retinal Vascular Hyperpermeability

“…For each MS/MS dataset, post-experiment monoisotopic mass refinement method was used to process the MS/MS data, which was previously demonstrated to accurately assign precursor mass to the tandem mass spectrometric data (18). The resultant MS/MS data from post-experiment monoisotopic mass refinement process (i.e.…”

Ultra-Specific Enrichment of GST-Tagged Protein by GSH-Modified Nanoparticles

“…The glutathione solutions were blended with SC-SPDPNPs or LC-SPDP-NPs (5 mg) in Tris-HCl buffer (pH 7. In order to mimic complex protein mixtures, yeast proteome mixture was produced by mixing the soluble fraction of yeast proteome 30 and GST-Ub (3 µg); human serum mixture was similarly produced by mixing a depleted human serum and GST-Ub (3 µg). The depleted serum sample was prepared by using the multiple affinity removal (MARS, Agilent Technologies, Santa Clara, CA) column on a crude serum sample.…”