Liquid Chromatography Electron Capture Dissociation Tandem Mass Spectrometry (LC-ECD-MS/MS) versus Liquid Chromatography Collision-induced Dissociation Tandem Mass Spectrometry (LC-CID-MS/MS) for the Identification of Proteins

Electron capture dissociation (ECD) offers many advantages over the more traditional fragmentation techniques for the analysis of peptides and proteins, although the question remains: How suitable is ECD for incorporation within proteomic strategies for the identification of proteins? Here, we compare LC-ECD-MS/MS and LC-CID-MS/MS as techniques for the identification of proteins.Experiments were performed on a hybrid linear ion trap–Fourier transform ion cyclotron resonance mass spectrometer. Replicate analyses of a six-protein (bovine serum albumin, apo-transferrin,lysozyme, cytochrome c, alcohol dehydrogenase, and β-galactosidase) tryptic digest were performed and the results analyzed on the basis of overall protein sequence coverage and sequence tag lengths within individual peptides. The results show that although protein coverage was lower for LC-ECDMS/MS than for LC-CID-MS/MS, LC-ECD-MS/MS resulted in longer peptide sequence tags,providing greater confidence in protein assignment

Electron Capture Dissociation Mass Spectrometry of Tyrosine Nitrated Peptides

In vivo protein nitration is associated with many disease conditions that involve oxidative stress and inflammatory response. The modification involves addition of a nitro group at the position ortho to the phenol group of tyrosine to give 3-nitrotyrosine. To understand the mechanisms and consequences of protein nitration, it is necessary to develop methods for identification of nitrotyrosine-containing proteins and localization of the sites of modification.Here, we have investigated the electron capture dissociation (ECD) and collision-induced association (CID) behavior of 3-nitrotyrosine-containing peptides. The presence of nitration did not affect the CID behavior of the peptides. For the doubly-charged peptides, addition of nitration severely inhibited the production of ECD sequence fragments. However, ECD of the triply-charged nitrated peptides resulted in some singly-charged sequence fragments. ECD of the nitrated peptides is characterized by multiple losses of small neutral species including hydroxyl radicals, water and ammonia. The origin of the neutral losses has been investigated by use of activated ion (AI) ECD. Loss of ammonia appears to be the result of non-covalent interactions between the nitro group and protonated lysine side-chains

The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called O(i) value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (O(i)). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances. Results: The APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis. Conclusion: The APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.National Institute of Allergy and Infectious Diseases (NIAID) N01-AI15447National Institutes of HealthNational Science Foundation, the Welsh and Packard FoundationsInternational Human Frontier Science ProgramCenter for Systems and Synthetic Biolog

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics