Quantitative strategies relying on stable isotope labeling and isotope dilution mass spectrometry have proven to be a very robust alternative to the well established gel-based techniques for the study of the dynamic proteome. Postdigestion 18 O labeling is becoming very popular mainly due to the simplicity of the enzyme-catalyzed exchange reaction, the peptide handling and storage procedures, and the flexibility and versatility introduced by decoupling protein digestion from peptide labeling. Despite recent progresses, peptide quantification by postdigestion 18 O labeling still involves several computational problems. In this work we analyzed the behavior of large collections of peptides when they were subjected to postdigestion labeling and concluded that this process can be explained by a universal kinetic model. On the basis of this observation, we developed an advanced quantification algorithm for this kind of labeling. Our method fits the entire isotopic envelope to parameters related with the kinetic exchange model, allowing at the same time an accurate calculation of the relative proportion of peptides in the original samples and of the specific labeling efficiency of each one of the peptides. We demonstrated that the new method eliminates artifacts produced by incomplete oxygen exchange in subsets of peptides that have a relatively low labeling efficiency and that may be considered indicative of false protein ratio deviations. Finally using a rigorous statistical analysis based on the calculation of error rates associated with false expression changes, we showed the validity of the method in the practice by detecting significant expression changes, produced by the activation of a model preparation of T cells, with only 5 g of protein in three proteins among a pool of more than 100. By allowing a full control over potential artifacts, our method may improve automation of the procedures for relative protein quantification using this labeling strategy. Beyond the control of synthesis and degradation of mRNA, living organisms possess a wide variety of regulatory mechanisms not affecting mRNA levels, such as protein synthesis, degradation, posttranslational modification or control of sub cellular location. Large-scale quantitative measurements at the protein level, also called quantitative proteomics, are thus complementary to the well established mRNA-based gene expression profiling techniques and will probably become one of the cornerstones of systems biology in the near future.In recent years, alternative strategies to the classical twodimensional gel electrophoresis approaches based on the chromatographic separation of complex mixtures of protease-generated peptides, also referred to as "shotgun proteomics," have been developed. Peptides eluting from an RP 1 -HPLC column are sprayed into a mass spectrometer that detects, isolates, and fragments specific peptide ions to obtain structural information, which is correlated against protein databases to identify the peptide sequence. Recently these approaches h...

High throughput identification of peptides in databases from tandem mass spectrometry data is a key technique in modern proteomics. Common approaches to interpret large scale peptide identification results are based on the statistical analysis of average score distributions, which are constructed from the set of best scores produced by large collections of MS/MS spectra by using searching engines such as SEQUEST. Other approaches calculate individual peptide identification probabilities on the basis of theoretical models or from single-spectrum score distributions constructed by the set of scores produced by each MS/MS spectrum. In this work, we study the mathematical properties of average SEQUEST score distributions by introducing the concept of spectrum quality and expressing these average distributions as compositions of single-spectrum distributions. We predict and demonstrate in the practice that average score distributions are dominated by the quality distribution in the spectra collection, except in the low probability region, where it is possible to predict the dependence of average probability on database size. Our analysis leads to a novel indicator, the probability ratio, which takes optimally into account the statistical information provided by the first and second best scores. The probability ratio is a non-parametric and robust indicator that makes spectra classification according to parameters such as charge state unnecessary and allows a peptide identification performance, on the basis of false discovery rates, that is better than that obtained by other empirical statistical approaches. The probability ratio also compares favorably with statistical probability indicators obtained by the construction of single-spectrum SEQUEST score distributions. These results make the robustness, conceptual simplicity, and ease of automation of the probability ratio algorithm a very attractive alternative to determine peptide identification confidences and error rates in high throughput experiments. Molecular & Cellular Proteomics 7:1135-1145, 2008.Modern proteomics is mainly based on the analysis of proteins by mass spectrometry. With the increasing use of multidimensional peptide separation coupled to tandem mass spectrometry as an alternative to gel-based approaches for protein expression analysis and high throughput protein identification, there is a growing interest in the development of scoring systems for automated, large scale peptide identification.SEQUEST (1-3), one of the first and most popular scoring schemes, measures the degree of correlation between the experimentally observed and the theoretical MS/MS spectra of peptides present in protein databases and determines the peptide sequence yielding the best correlation score, or Xcorr. Another related SEQUEST parameter is the delta score, or ⌬C n , which measures the difference between the best and second best scores (1-3). SEQUEST results must be further processed to determine whether the peptide identification is correct or not. Filtering of SEQUEST...

Quantitative proteomics using stable isotopic 16O/18O labeling has emerged as a very powerful tool, since it has a number of advantages over other methods, including the simplicity of chemistry, the constant mass tag at the C termini and its general applicability. However, due to the small mass difference between labeled and unlabeled peptide species, this approach has usually been restricted to high-resolution mass spectrometers. In this study we explored whether the high-resolution scanning mode, together with the extremely high scanning speed of the linear IT allows the 16O/18O-labeling method to be used for accurate, large-scale quantitative analysis of proteomes. A protocol, including digestion, desalting, labeling, MS and quantitative analysis was developed and tested using protein standards and whole proteome extracts. Using this method we were able to identify and quantify 140 proteins from only 10 mug of a proteome extract from mesenchymal stem cells. Relative expression changes larger than twofold can be identified with this method at the 95% confidence level. Our results demonstrate that accurate quantitative analysis using 16O/18O labeling can be performed in the practice using linear IT MS, without compromising large-scale peptide identification efficiency.