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.2008 Sep;7(9):3755-64.
doi: 10.1021/pr800031f. Epub 2008 Jul 25.

Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage

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Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage

Phu T Van et al. J Proteome Res.2008 Sep.

Abstract

The relatively small numbers of proteins and fewer possible post-translational modifications in microbes provide a unique opportunity to comprehensively characterize their dynamic proteomes. We have constructed a PeptideAtlas (PA) covering 62.7% of the predicted proteome of the extremely halophilic archaeon Halobacterium salinarum NRC-1 by compiling approximately 636 000 tandem mass spectra from 497 mass spectrometry runs in 88 experiments. Analysis of the PA with respect to biophysical properties of constituent peptides, functional properties of parent proteins of detected peptides, and performance of different mass spectrometry approaches has highlighted plausible strategies for improving proteome coverage and selecting signature peptides for targeted proteomics. Notably, discovery of a significant correlation between absolute abundances of mRNAs and proteins has helped identify low abundance of proteins as the major limitation in peptide detection. Furthermore, we have discovered that iTRAQ labeling for quantitative proteomic analysis introduces a significant bias in peptide detection by mass spectrometry. Therefore, despite identifying at least one proteotypic peptide for almost all proteins in the PA, a context-dependent selection of proteotypic peptides appears to be the most effective approach for targeted proteomics.

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Figures

Figure 1
Figure 1. A threshold of peptide detections has been achieved in theH. salinarum NRC-1 PA
The cumulative number of peptides detected withP ≥ 0.9 is plotted as a function of the cumulative number of MS/MS spectra. We observe an increase in new unique peptides with an increase in the numbers of MS/MS spectra added to the PA. However, it appears that we have reached a threshold given the five different approaches that we have used thus far. Peptides detected within each type of proteomic experimental approach are color-coded (see legend for details). Numbers in legend indicate total numbers of peptides detected uniquely in each experiment type. For example, 978 peptides were detected only in ICAT experiments and not in any of the other proteomic experiments represented in the PA.
Figure 2
Figure 2. Characteristics of peptides detected inH. salinarum NRC-1 proteomics experiments included in the PeptideAtlas. (A) Influence of molecular weight (MW) on peptide detection
A comparison of total predicted (grey bars) vs. total detected (black bars) tryptic peptides indicates the expected peptide detection range of mass spectrometry as a function of peptide size.(B) Influence of charge on detection of peptides. A similar plot of total expected vs. detected peptides as in (A) but as a function of isoelectric point (pI) shows a bias against the detection of basic peptides. (C) Peptide detection as a function of relative hydrophobicity. Comparison of predicted vs. observed peptides as a function of hydrophobicity shows optimal detection was for peptides of hydrophobicity of ∼30.(D) Detection of membrane vs. soluble proteins. The membrane association predictions are based on hydropathy plots as in (C). This plot shows that despite enrichment of membrane proteins in some experiments there is a significant bias in detection of membrane proteins (∼34%) relative to soluble proteins (∼70%).
Figure 3
Figure 3. Significant correlation between absolute mRNA and protein abundance. (A) Concordance between transcript abundance and per-protein sequence coverage
Comparison of peptide coverage per protein (Y-axis) and transcript abundance (X-axis), each of which was calculated as described in Materials and Methods. Each point on the scatterplot corresponds to one of the 1,646 genes whose proteins were detected in the PA. The Spearman correlation coefficient between the two datasets is shown on the graph (Rs = 0.511;P < 10−7), and the bold grey line represents the correlation squared (R2). (B) Concordance between transcript abundance and per-protein spectral counts. Arithmetic mean of the number of spectra counted per protein (Y-axis; Materials and Methods) was plotted as a function of each protein's cognate transcript abundance (X-axis). (C) Concordance between transcript abundance and proteome coverage. Average mRNA signal intensities for each gene are organized into 100 bins with 100 intensities per bin (i.e. bin 1 = intensity 0−99; bin 2 = 100−199; etc). Note that the first three bins are empty (i.e. transcripts with low intensities were detected neither at the mRNA nor at the protein level). Cumulative proteome coverage (black connected squares; right-hand Y-axis) was calculated by adding the total number of proteins detected per transcript intensity bin as each successively higher bin was added to the analysis. Note that although the analysis was carried out to intensities of 50,000, for brevity we have terminated the graph at 10,000 on the X-axis and 60% cumulative coverage on the right-hand Y-axis , since we observed an increase of new detections of only ∼3% between intensities of 10,000 and 50,000. Total protein count (grey vertical bars; left-hand Y-axis) is represented by the height of each bar, denoting the total number of proteins detected per bin.
Figure 4
Figure 4. The majority of proteins detected by theH. salinarum NRC-1 PA are observed in a small number of μLC-ESI-MS/MS runs
The number of μLC-ESI-MS/MS runs is plotted on the X-axis. The cumulative number of proteins detected with the addition of each successive MS run is plotted on the Y-axis.
Figure 5
Figure 5. Comparison of all experiments included in theH. salinarum PA
Numbers in parentheses indicate peptide detections by each approach, numbers in boxes indicate unique detections, and shading indicates the percentage of peptides detected by both approaches. Experiment names refer to those introduced in Table 1.
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