Large precursor tolerance database search - a simple approach for estimation of the amount of spectra with precursor mass shifts in proteomic data.

Mass measurement and precursor mass assignment are independent processes in proteomic data acquisition. Due to misassignments to C-13 peak, or for other reasons, extensive precursor mass shifts (i.e., deviations of the measured from calculated precursor neutral masses) in LC-MS/MS data obtained with the high-accuracy LTQ-Orbitrap mass spectrometers have been reported in previous studies. Although computational methods for post-acquisition reassignment to monoisotopic mass have been developed to curate the MS/MS spectra prior to database search, a simpler method for estimating the fraction of spectra with precursor mass shift so as to determine whether the data require curation remains desirable. Here, we provide the evidence that an easy approach, which applies a large precursor tolerance (2.1Da or higher) in SEQUEST search against a forward and decoy protein sequence database and then filters the data with PeptideProphet peptide identification probability (p≥0.9), could detect most of the MS/MS spectra containing inaccurate precursor masses. Furthermore, through the implementation of artificial mass shifts on 4000 randomly selected MS/MS spectra, which originally had accurate precursor mass assigned by the mass spectrometers, we demonstrated that the accuracy of the precursor mass has almost negligible influence on the efficacy and fidelity of peptide identification. BIOLOGICAL SIGNIFICANCE: Integral precursor mass shift is a known problem and thus proteomic data should be handled and analyzed properly to avoid losing important protein identification and/or quantification information. A quick and easy approach for estimating the number of MS/MS spectra with inaccurate precursor mass assignments would be helpful for evaluating the performance of the instrument, determining whether the data requires curation prior to database search or should be searched with specific search parameter(s). Here we demonstrated most of the MS/MS spectra with inaccurate mass assignments (integral or non-integral changes) that could be easily identified by database search with large precursor tolerance windows.

Metabolic capabilities and systems fluctuations in Haloarcula marismortui revealed by integrative genomics and proteomics analyses.

The 1310 Haloarcula marismortui proteins identified from mid-log and late-log phase soluble and membrane proteomes were analyzed in metabolic and cellular process networks to predict the available systems and systems fluctuations upon environmental stresses. When the connected metabolic reactions with identified proteins were examined, the availability of a number of metabolic pathways and a highly connected amino acid metabolic network were revealed. Quantitative spectral count analyses suggested 300 or more proteins might have expression changes in late-log phase. Among these, integrative network analyses indicated approximately 106 were metabolic proteins that might have growth-phase dependent changes. Interestingly, a large proportion of proteins in affected biomodules had the same trend of changes in spectral counts. Disregard the magnitude of changes, we had successfully predicted and validated the expression changes of nine genes including the rimK, gltCP, rrnAC0132, and argC in lysine biosynthesis pathway which were downregulated in late-log phase. This study had not only revealed the expressed proteins but also the availability of biological systems in two growth phases, systems level changes in response to the stresses in late-log phase, cellular locations of identified proteins, and the likely regulated genes to facilitate further analyses in the postgenomic era.

New structural proteins of Halobacterium salinarum gas vesicle revealed by comparative proteomics analysis.

The Halobacterium salinarum gas vesicle (GV) is an extremely stable intracellular organelle with air trapped inside a proteinaceous membrane. Reported here is a comparative proteomics analysis of GV and GV depleted lysate (GVD) to reveal the membrane structural proteins. Ten proteins encoded by gvp-1 (gvpMLKJIHGFED-1 and gvpACNO-1) and five proteins encoded by gvp-2 (gvpMLKJIHGFED-2 and gvpACNO-2) gene clusters for the biogenesis of spindle- and cylindrical-, respectively, shaped GV were identified by LC-MS/MS. The peptides of GvpA1, I1, J1, A2, and J2 were exclusively identified in purified GV, GvpD1, H1, L1, and F2 only in GVD, and GvpC1, N1, O1, F1, H2, and O2 in both samples. The identification of GvpA1, C1, F1, J1, and A2 in GV is in agreement with their previously known structural function. In addition, the detection of GvpI1, N1, O1, H2, J2, and O2 in GV suggested they are new structural proteins. Among these, the structural role of GvpI1 and N1 in GV was further validated by immuno-detection of protein A-tagged GvpI1 and N1 fusion proteins in purified GV. Thus, LC-MS/MS could reveal at least a half dozen gas vesicle structural proteins in the predominant spindle-shaped GV that may be helpful for studying its biogenesis.

Increasing information from shotgun proteomic data by accounting for misassigned precursor ion masses.

Although mass spectrometers are capable of providing high mass accuracy data, assignment of true monoisotopic precursor ion mass is complicated during data-dependent ion selection for LC-MS/MS analysis of complex mixtures. The complication arises when chromatographic peak widths for a given analyte exceed the time required to acquire a precursor ion mass spectrum. The result is that many measured monoisotopic masses are misassigned due to calculation from a single mass spectrum with poor ion statistics based on only a fraction of the total available ions for a given analyte. Such data in turn produces errors in automated database searches, where precursor m/z value is one search parameter. We propose here a postacquisition approach to correct misassigned monoisotopic m/z values that involves peak detection over the entire elution profile and correction of the precursor ion monoisotopic mass. As a result of using this approach to reprocess shotgun proteomic data we increased peptide sequence assignments by 10% while reducing the estimated false positive ratio from 1 to 0.2%. We also show that 4% of the salvaged identifications may be accounted for by correction of mixed tandem mass spectra resulting from fragmentation of multiple peptides simultaneously, a situation which we refer to as accidental CID.