Measurements of mass spectral peak intensities and spectral counts are promising methods for quantifying protein abundance changes in shotgun proteomic analyses. We describe Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance. Dynamic range and linearity using a three-dimensional ion trap were tested using standard proteins spiked into a complex sample. Linearity and good agreement between observed versus expected protein ratios were obtained after normalization and background subtraction of peak area intensity measurements and correction of spectral counts to eliminate discontinuity in ratio estimates. Peak intensity values useful for protein quantitation ranged from 10 7 to 10 11 counts with no obvious saturation effect, and proteins in replicate samples showed variations of less than 2-fold within the 95% range (؎2) when >3 peptides/protein were shared between samples. Protein ratios were determined with high confidence from spectral counts when maximum spectral counts were >4 spectra/protein, and replicates showed equivalent measurements well within 95% confidence limits. In further tests, complex samples were separated by gel exclusion chromatography, quantifying changes in protein abundance between different fractions. Linear behavior of peak area intensity measurements was obtained for peptides from proteins in different fractions. Protein ratios determined by spectral counting agreed well with those determined from peak area intensity measurements, and both agreed with independent measurements based on gel staining intensities. Overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios. Finally these methods were used to analyze differential changes in protein expression in human erythroleukemia K562 cells stimulated under conditions that promote cell differentiation by mitogenactivated protein kinase pathway activation. Protein changes identified with p < 0

Identifying proteins in cell extracts by shotgun proteomics involves digesting the proteins, sequencing the resulting peptides by data-dependent mass spectrometry (MS/MS), and searching protein databases to identify the proteins from which the peptides are derived. Manual analysis and direct spectral comparison reveal that scores from two commonly used search programs (Sequest and Mascot) validate less than half of potentially identifiable MS/MS spectra (class positive) from shotgun analyses of the human erythroleukemia K562 cell line. Here we demonstrate increased sensitivity and accuracy using a focused search strategy along with a peptide sequence validation script that does not rely exclusively on XCorr or Mowse scores generated by Sequest or Mascot, but uses consensus between the search programs, along with chemical properties and scores describing the nature of the fragmentation spectrum (ion score and RSP). The approach yielded 4.2% false positive and 8% false negative frequencies in peptide assignments. The protein profile is then assembled from peptide assignments using a novel peptide-centric protein nomenclature that more accurately reports protein variants that contain identical peptide sequences. An Isoform Resolver algorithm ensures that the protein count is not inflated by variants in the protein database, eliminating approximately 25% of redundant proteins. Analysis of soluble proteins from a human K562 cells identified 5130 unique proteins, with approximately 100 false positive protein assignments.

SUMMARY A complicating factor for protein identification within complex mixtures by LC/MS/MS is the problem of “chimera” spectra, where two or more precursor ions with similar mass and retention time are co-sequenced by MS/MS. Chimera spectra show reduced scores due to unidentifiable fragment ions derived from contaminating parents. However, the extent of chimeras in LC/MS/MS datasets and their impact on protein identification workflows are incompletely understood. We report ChimeraCounter, a software program which detects chimeras in datasets collected on an Orbitrap/LTQ instrument. Evaluation of synthetic chimeras created from pairs of well-defined peptide MS/MS spectra reveal that chimeras reduce database search scores most significantly when contaminating fragment ion intensities exceed 20% of the targeted fragment ion intensities. In large scale datasets, the identification rate for chimera MS/MS is 2-fold lower compared to non-chimera spectra. Importantly, this occurs in a manner which depends not on absolute precursor ion intensity, but on intensity relative to the median precursor intensity distribution. We further show that chimeras reduce the number of accepted peptide identifications by increasing false negatives while showing little increase in false positives. The results provide a framework for identifying chimeras and characterizing their contribution to the poorly understood false negative class of MS/MS.