Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Extraction and Solubilization of Proteins for Proteomic Studies Richard M. Leimgruber Preparation of Bacterial Samples for 2-D PAGE Brian Berg Vandahl, Gunna Christiansen, and Svend Birkelund Preparation of Yeast Samples for 2-D PAGE Joakim Norbeck Preparation of Mammalian Tissue Samples for Two-Dimensional Electrophoresis Frank A. Witzmann Differential Detergent Fractionation of Eukaryotic Cells Melinda L. Ramsby and Gregory S. Makowski Serum or Plasma Sample Preparation for Two-Dimensional Gel Electrophoresis Anthony G. Sullivan, Stephen Russell, Henry Brzeski, Richard I. Somiari, and Craig D. Shriver Preparation of Plant Protein Samples for 2-D PAGE David W. M. Leung Laser-Assisted Microdissection in Proteomic Analyses Darrell L. Ellsworth, Stephen Russell, Brenda Deyarmin, Anthony G. Sullivan, Henry Brzeski, Richard I. Somiari, and Craig D. Shriver Purification of Cellular and Organelle Populations by Fluorescence-Activated Cell Sorting for Proteome Analysis William L. Godfrey, Colette J. Rudd, Sujata Iyer, and Diether Recktenwald Purification of Nucleoli From Lymphoma Cells and Solubilization of Nucleolar Proteins for 2-DE Separation Regis Dieckmann, Yohann Coute, Denis Hochstrasser, Jean-Jacques Diaz, and Jean-Charles Sanchez Prefractionation of Complex Protein Mixture for 2-D PAGE Using Reversed-Phase Liquid Chromatography Volker Badock and Albrecht Otto Fractionation of Complex Proteomes by Microscale Solution Isoelectrofocusing Using ZOOM(TM) IEF Fractionators to Improve Protein Profiling Xun Zuo, Ki-Boom Lee, and David W. Speicher Large-Format 2-D Polyacrylamide Gel Electrophoresis Henry Brzeski, Stephen Russell, Anthony G. Sullivan, Richard I. Somiari, and Craig D. Shiver Analysis of Membrane Proteins by Two-Dimensional Gels MichaelFountoulakis 2-D PAGE of High-Molecular-Mass Proteins Masamichi Oh-Ishi and Tadakazu Maeda Using Ultra-Zoom Gels for High-Resolution Two-Dimensional Polyacrylamide Gel Electrophoresis Sjouke Hoving, Hans Voshol, and Jan van Oostrum NEpHGE and pI Strip Proteomic 2-D Gel Electrophoretic Mapping of Lipid-Rich Membranes Steven E. Pfeiffer, Yoshihide Yamaguchi, Cecilia B. Marta, Rashmi Bansal, and Christopher M. Taylor Silver Staining of 2-D Gels Julia Poland, Thierry Rabilloud, and Pranav Sinha Zn2+ Reverse Staining Technique Carlos Fernandez-Patron Multiplexed Proteomics Technology for the Fluorescence Detection of Glycoprotein Levels and Protein Expression Levels Using Pro-Q(R) Emerald and SYPRO(R) Ruby Dyes Birte Schulenberg and Wayne F. Patton Multiplexed Proteomics Technology for the Fluorescence Detection of Phosphorylation and Protein Expression Levels Using Pro-Q(R) Diamond and SYPRO(R) Ruby Dyes Birte Schulenberg, Terrie Goodman, Thomas H. Steinberg, and Wayne F. Patton Sensitive Quantitative Fluorescence Detection of Proteins in Gels Using SYPRO(R) Ruby Protein Gel Stain Birte Schulenberg, Nancy Ahnert, and Wayne F. Patton Rapid, Sensitive Detection of Proteins in Minigels With Fluorescent Dyes: Coomassie Fluor Orange, SYPRO(R) Orange, SYPRO Red, and SYPRO Tangerine Protein Gel Stains Thomas H. Steinberg, Courtenay R. Hart, and Wayne F. Patton Differential In-Gel Electrophoresis in a High-Throughput Environment Richard I. Somiari, Stephen Russell, Stella B. Somiari, Anthony G. Sullivan, Darrell L. Ellsworth, Henry Brzeski, and Craig D. Shriver Statistical Analysis of 2-D Gel Patterns Francoise Seillier-Moiseiwitsch 2-DE Databases on the World Wide Web Christine Hoogland, Khaled Mostaguir, and Ron D. Appel Computer Analysis of 2-D Images Patricia M. Palagi,

The proteomics protocols handbook

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Proteomics is an increasingly powerful and indispensable technology in molecular cell biology. It can be used to identify the components of small protein complexes and large organelles, to determine post-translational modifications and in sophisticated functional screens. The key — but little understood — technology in mass-spectrometry-based proteomics is peptide sequencing, which we describe and review here in an easily accessible format.

/pdf/the-abc-s-and-xyz-s-of-peptide-sequencing-58wrj1in70.pdf

The ABC's (and XYZ's) of peptide sequencing

Various technologies can be used to produce genome-scale, or 'omics', data sets that provide systems-level measurements for virtually all types of cellular components in a model organism. These data yield unprecedented views of the cellular inner workings. However, this abundance of information also presents many hurdles, the main one being the extraction of discernable biological meaning from multiple omics data sets. Nevertheless, researchers are rising to the challenge by using omics data integration to address fundamental biological questions that would increase our understanding of systems as a whole.

The model organism as a system: integrating 'omics' data sets.

Proteomics is the systematic study of the many and diverse properties of proteins in a parallel manner with the aim of providing detailed descriptions of the structure, function and control of biological systems in health and disease. Advances in methods and technologies have catalyzed an expansion of the scope of biological studies from the reductionist biochemical analysis of single proteins to proteome-wide measurements. Proteomics and other complementary analysis methods are essential components of the emerging 'systems biology' approach that seeks to comprehensively describe biological systems through integration of diverse types of data and, in the future, to ultimately allow computational simulations of complex biological systems.

Proteomics: the first decade and beyond.

The present invention provides a method for diagnosing and detecting diseases associated with pancreas. The present invention provides one or more proteins or fragments thereof, peptides or nucleic acid molecules differentially expressed in pancreatic diseases (PCAT) and antibodies binds to PCAT. The present invention provides that PCAT is used as targets for screening agents that modulates the PCAT activities. Further, the present invention provides methods for treating diseases associated with pancreas.

Pancreatic cancer targets and uses thereof

Proteomics: drug target discovery on an industrial scale

Quantitation of phosphopeptides using affinity chromatography and stable isotope labeling

The targets of the majority of drugs on the market and in development are proteins, and the efficient analysis of these molecules is critical to defining targets for better therapeutic intervention. Mass spectrometry is currently the key technology for parallel protein analysis (also referred to as proteomics). In this review we will describe recent advances in mass spectrometry instrumentation, methods and applications that are likely to impact drug discovery.

Enabling parallel protein analysis through mass spectrometry.

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