Quantitative analysis of isotope distributions in proteomic mass spectrometry using least-squares Fourier transform convolution - PubMed (original) (raw)
. 2008 Jul 1;80(13):4906-17.
doi: 10.1021/ac800080v. Epub 2008 Jun 4.
Affiliations
- PMID: 18522437
- PMCID: PMC2502059
- DOI: 10.1021/ac800080v
Quantitative analysis of isotope distributions in proteomic mass spectrometry using least-squares Fourier transform convolution
Edit Sperling et al. Anal Chem. 2008.
Abstract
Quantitative proteomic mass spectrometry involves comparison of the amplitudes of peaks resulting from different isotope labeling patterns, including fractional atomic labeling and fractional residue labeling. We have developed a general and flexible analytical treatment of the complex isotope distributions that arise in these experiments, using Fourier transform convolution to calculate labeled isotope distributions and least-squares for quantitative comparison with experimental peaks. The degree of fractional atomic and fractional residue labeling can be determined from experimental peaks at the same time as the integrated intensity of all of the isotopomers in the isotope distribution. The approach is illustrated using data with fractional (15)N-labeling and fractional (13)C-isoleucine labeling. The least-squares Fourier transform convolution approach can be applied to many types of quantitative proteomic data, including data from stable isotope labeling by amino acids in cell culture and pulse labeling experiments.
Figures
Figure 1
Composite peaks from unlabeled and fractionally 15N-labeled peptides resulting from pulse labeling. Experimental data points are shown as closed circles, and the least-squares fits using eqs 13–22 are shown as the solid line. (a) Protein S17 (residues 71–76), SWTLVR, z = 1. Fitted values: θ = 0.826, _f_L = 0.736. (b) S13(31–43), AILAAAGIAEDVK, z = 1. Fitted values: θ = 0.822, _f_L = 0.736. (c) S19(55–69), QHVPVFVTDEMVGHK, z = 4. Fitted values: θ = 0.826, _f_L = 0.737.
Figure 2
(a) Histogram of the distribution of the fractional labeling parameter (θ) determined by least-squares fitting for a set of 291 30S ribosomal peptides in a 15N-pulse labeling experiment. (b) Histogram of the distribution of the fraction labeled amplitude parameter (_f_L) determined by least-squares fitting for a set of 405 peptides derived from a 1:3 mixture of unlabeled and “100%” 15N-labeled 30S subunits. For both plots, a box-and-whiskers plot is shown at the top, indicating the median and the quartiles with boxes, and 1.5 times the interquartile range with whiskers. Clear outliers are indicated with open symbols at the top. For both plots, the normal distribution fitted to the entire set of values is shown as the solid line.
Figure 3
Composite peaks from unlabeled and fractionally 13C-Ile-labeled peptides resulting from pulse labeling. Experimental data points are shown as closed circles, and the least-squares fits using eqs 13–22 are shown as the solid line. (a) S9(27–32), IVINQR, z = 2. (b) S3(179–198), ADIDYNTSEAHTTYGVIGVK, z = 2.
Figure 4
Composite peak from an unlabeled and fractional 13C-Ile/2H-Leu-labeled peptide resulting from pulse labeling. Experimental data points are shown as closed circles, and the least-squares fits using eqs 13–22 are shown as the solid line. The peptide sequence is ISELSEGQIDTLRDEVAK. The inset shows a vertical expansion of the fractionally labeled region. The main peaks resulting from the combinatorial residue labeling are indicated by arrows. The residuals from the fit, shown below, are dominated by the unlabeled peak, which has asymmetric tailing.
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