Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Antigen retrieval for proteomic characterization of formalin-fixed and paraffin-embedded tissues

Formalin-fixed and paraffin-embedded tissues represent the vast majority of archived tissue. Access to such tissue specimens via shotgun-based proteomic analyses may open new avenues for both prospective and retrospective translational research. In this study, we evaluate the effects of fixation time on antigen retrieval for the purposes of shotgun proteomics. For the first time, we demonstrate the capability of a capillary isotachophoresis (CITP)-based proteomic platform for the shotgun proteomic analysis of proteins recovered from FFPE tissues. In comparison to our previous studies utilizing capillary isoelectric focusing, the CITP-based analysis is more robust and increases proteome coverage. In this case, results from three FFPE liver tissues yield a total of 4098 distinct Swiss-Prot identifications at a 1% false-discovery rate. To judge the accuracy of these assignments, immunohistochemistry is performed on a panel of 17 commonly assayed proteins. These proteins span a wide range of protein abundances as inferred from relative quantitation via spectral counting. Among the panel were 4 proteins identified by a single peptide hit, including three clusters of differentiation (CD) markers: CD74, CD117, and CD45. Because single peptide hits are often regarded with skepticism, it is notable that all proteins tested by IHC stained positive.

Phosphoproteome analysis of fission yeast

Phosphorylation is a key regulator of many events in eukaryotic cells. The acquisition of large-scale phosphorylation data sets from model organisms can pinpoint conserved regulatory inputs and reveal kinase-substrate relationships. Here, we provide the first large-scale phosphorylation analysis of the fission yeast, Schizosaccharomyces pombe. Protein from thiabendazole-treated cells was separated by preparative SDS-PAGE and digested with trypsin. The resulting peptides were subjected to either IMAC or TiO2 phosphopeptide enrichment methods and then analyzed by LC-MS/MS using an LTQ-Orbitrap mass spectrometer. In total, 2887 distinct phosphorylation sites were identified from 1194 proteins with an estimated false-discovery rate of <0.5% at the peptide level. A comparison of the two different enrichment methods is presented, supporting the finding that they are complementary. Finally, phosphorylation sites were examined for phosphorylation-specific motifs and evolutionary conservation. These analyses revealed both motifs and specific phosphorylation events identified in S. pombe were conserved and predicted novel phosphorylation in mammals.

Application of proteomics in the discovery of candidate protein biomarkers in a diabetes autoantibody standardization program sample subset

Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted "-omics" approaches are underutilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program, with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. alpha-2-Glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples.

A new strategy to filter out false positive identifications of peptides in SEQUEST database search results

Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.

The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.

Yeast Chfr homologs retard cell cycle at G1 and G2/M via Ubc4 and Ubc13/Mms2-dependent ubiquitination

Checkpoint with forkhead-associated and RING (Chfr) is a ubiquitin ligase (E3) that establishes an antephase or prometaphase checkpoint in response to mitotic stress. Though ubiquitination is essential for checkpoint function, the sites, linkages and ubiquitin conjugating enzyme (E2) specificity are controversial. Here we dissect the function of the two Chfr homologs in S. cerevisiae, Chf1 and Chf2, overexpression of which retard cell cycle at both G(1) and G(2). Using a genetic assay, we establish that Ubc4 is required for Chf2-dependent G(1) cell cycle delay and Chf protein turnover. In contrast, Ubc13/Mms2 is required for G(2) delay and does not contribute to Chf protein turnover. By reconstituting cis and trans-ubiquitination activities of Chf proteins in purified systems and characterizing sites modified and linkages formed by tandem mass spectrometry, we discovered that Ubc13/Mms2- dependent modifications are a distinct subset of those catalyzed by Ubc4. Mutagenesis of Lys residues identified in vitro indicates that site-specific Ubc4-dependent Chf protein autoubiquitination is responsible for Chf protein turnover. Thus, combined genetic and biochemical analyses indicate that Chf proteins have dual E2 specificity accounting for different functions in the cell cycle.

High-throughput analysis of rat liver plasma membrane proteome by a nonelectrophoretic in-gel tryptic digestion coupled with mass spectrometry identification

In-gel digestion is commonly used after proteins are resolved by polyacrylamide gel electrophoresis (SDS-PAGE, 2-DE). It can also be used on its own in conjunction with tandem mass spectrometry (MS/MS) for the direct analysis of complex proteins. Here, we describe a strategy combining isolation of purified plasma membrane, efficient digestion of plasma membrane proteins in polyacrylamide gel, and high-sensitivity analysis by advanced mass spectrometry to create a new rapid and high-throughput method. The plasma membrane protein mixture is directly incorporated into a polyacrylamide gel matrix, After formation of the gel, proteins in the gel section are digested with trypsin, and the resulting peptides are subjected to reversed-phase, high-performance liquid chromatography followed by electrospray ion-trap tandem mass analysis. Using this optimized strategy, we have identified 883 rat liver membrane proteins, of which 490 had a gene ontology (GO) annotation indicating a cellular component, and 294 (60%) of the latter were known integral membrane proteins or membrane proteins. In total, 333 proteins are predicted by the TMHMM 2.0 algorithm to have transmembrane domains (TMDs) and 52% (175 of 333) proteins to contain 2-16 TMDs. The identified membrane proteins provide a broad representation of the rat plasma membrane proteome with little bias evident due to protein p I and molecular weight (MW). Also, membrane proteins with a high GRAVY score (grand average hydrophobicity score) were identified, and basic and acidic membrane proteins were evenly represented. This study not only offered an efficient and powerful method in shotgun proteomics for the identification of proteins of complex plasma membrane samples but also allowed in-depth study of liver membrane proteomes, such as of rat models of liver-related disease. This work represents one of the most comprehensive proteomic analyses of the membrane subproteome of rat liver plasma membrane in general.

What's driving false discovery rates?

The "Paris Guidelines" have begun the process of standardizing reporting for proteomics. New bioinformatics tools have improved the process for estimating error rates of peptide identifications. This perspective seeks to consider these advances in the context of proteomics' short history. As increasing numbers of proteomics papers come from biologists rather than technologists, developing consensus standards for estimating error will be increasingly necessary. Standardizing this assessment should be welcomed as a reflection of the growing impact of proteomic technologies.

The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry

The success in profiling the phosphoproteome by mass spectrometry-based proteomics has been intimately related to the availability of methods that selectively enrich for phosphopeptides. To this end, we describe a protocol that combines two sequential enrichment steps. First, strong cation exchange (SCX) chromatography separates peptides by solution charge. Phosphate groups contribute to solution charge by adding a negative charge at pH 2.7. Therefore, at that pH, phosphopeptides are expected to elute earlier than their nonphosphorylated homologs. Second, immobilized metal affinity chromatography (IMAC) takes advantage of phosphate's affinity for metal ions such as Fe(3+) to uniformly enrich for phosphopeptides from the previously collected SCX fractions. We have successfully employed the SCX/IMAC enrichment strategy in the exploration of phosphoproteomes from several systems including mouse liver and Drosophila embryos characterizing over 5,500 and 13,000 phosphorylation events, respectively. The SCX/IMAC enrichment protocol requires 2 days, and the entire procedure from cells to a phosphorylation data set can be completed in less than 10 days.

Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling

Reliable statistical validation of peptide and protein identifications is a top priority in large-scale mass spectrometry based proteomics. PeptideProphet is one of the computational tools commonly used for assessing the statistical confidence in peptide assignments to tandem mass spectra obtained using database search programs such as SEQUEST, MASCOT, or X! TANDEM. We present two flexible methods, the variable component mixture model and the semiparametric mixture model, that remove the restrictive parametric assumptions in the mixture modeling approach of PeptideProphet. Using a control protein mixture data set generated on an linear ion trap Fourier transform (LTQ-FT) mass spectrometer, we demonstrate that both methods improve parametric models in terms of the accuracy of probability estimates and the power to detect correct identifications controlling the false discovery rate to the same degree. The statistical approaches presented here require that the data set contain a sufficient number of decoy (known to be incorrect) peptide identifications, which can be obtained using the target-decoy database search strategy.

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.

Modes of inference for evaluating the confidence of peptide identifications

Several modes of inference are currently used in practice to evaluate the confidence of putative peptide identifications resulting from database scoring algorithms such as Mascot, SEQUEST, or X!Tandem. The approaches include parametric methods, such as classic PeptideProphet, and distribution free methods, such as methods based on reverse or decoy databases. Because of its parametric nature, classic PeptideProphet, although highly robust, was not highly flexible and was difficult to apply to new search algorithms or classification scores. While commonly applied, the decoy approach has not yet been fully formalized and standardized. And, although they are distribution-free, they like other approaches are not free of assumptions. Recent manuscripts by Kall et al., Choi and Nesvizhskii, and Choi et al. help advance these methods, specifically by formalizing an alternative formulation of decoy databases approaches and extending the PeptideProphet methods to make explicit use of decoy databases, respectively. Taken together with standardized decoy database methods, and expectation scores computed by search engines like Tandem, there exist at least four different modes of inference used to assign confidence levels to individual peptides or groups of peptides. We overview and compare the assumptions of each of these approaches and summarize some interpretation issues. We also discuss some suggestions, which may make the use of decoy databases more computationally efficient in practice.

False discovery rates and related statistical concepts in mass spectrometry-based proteomics

Development of statistical methods for assessing the significance of peptide assignments to tandem mass spectra obtained using database searching remains an important problem. In the past several years, several different approaches have emerged, including the concept of expectation values, target-decoy strategy, and the probability mixture modeling approach of PeptideProphet. In this work, we provide a background on statistical significance analysis in the field of mass spectrometry-based proteomics, and present our perspective on the current and future developments in this area.

Posterior error probabilities and false discovery rates: two sides of the same coin

A variety of methods have been described in the literature for assigning statistical significance to peptides identified via tandem mass spectrometry. Here, we explain how two types of scores, the q-value and the posterior error probability, are related and complementary to one another.

Tandem mass spectrometry in physiology

Tandem mass spectrometry coupled to liquid chromatography (LC-MS/MS) allows identification of proteins in a complex mixture without need for protein purification ("shotgun" proteomics). Recent progress in LC-MS/MS-based quantification, phosphoproteomic analysis, and targeted LC-MS/MS using multiple reaction monitoring (MRM) has made LC-MS/MS a powerful tool for the study of cell physiology.

Accurate annotation of peptide modifications through unrestrictive database search

Proteins are extensively modified after translation due to cellular regulation, signal transduction, or chemical damage. Peptide tandem mass spectrometry can discover post-translational modifications, as well as sequence polymorphisms. Recent efforts have studied modifications at the proteomic scale. In this context, it becomes crucial to assess the accuracy of modification discovery. We discuss methods to quantify the false discovery rate from a search and demonstrate how several features can be used to distinguish valid modifications from search artifacts. We present a tool, PTMFinder, which implements these methods. We summarize the corpus of post-translational modifications identified on large data sets. Thousands of known and novel modification sites are identified, including site-specific modifications conserved over vast evolutionary distances.

Large-scale identification and evolution indexing of tyrosine phosphorylation sites from murine brain

Metazoans employ reversible tyrosine phosphorylation to regulate innumerable biological processes. Thus, the large-scale identification of tyrosine phosphorylation sites from primary tissues is an essential step toward a molecular systems understanding of dynamic regulation in vivo. The relative paucity of phosphotyrosine has greatly limited its identification in large-scale phosphoproteomic experiments. However, using antiphosphotyrosine peptide immunoprecipitations, we report the largest study to date of tyrosine phosphorylation sites from primary tissue, identifying 414 unique tyrosine phosphorylation sites from murine brain. To measure the conservation of phosphorylated tyrosines and their surrounding residues, we constructed a computational pipeline and identified patterns of conservation within the signature of phosphotyrosine.

Mass spectrometry-based functional proteomics: from molecular machines to protein networks

The study of protein-protein interactions by mass spectrometry is an increasingly important part of post-genomics strategies to understand protein function. A variety of mass spectrometry-based approaches allow characterization of cellular protein assemblies under near-physiological conditions and subsequent assignment of individual proteins to specific molecular machines, pathways and networks, according to an increasing level of organizational complexity. An appropriate analytical strategy can be individually tailored--from an in-depth analysis of single complexes to a large-scale characterization of entire molecular pathways or even an analysis of the molecular organization of entire expressed proteomes. Here we review different options regarding protein-complex purification strategies, mass spectrometry analysis and bioinformatic methods according to the specific question that is being addressed.

Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome

Background

The chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned.

Results

We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.

Conclusion

We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.

A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry

MOTIVATION

Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing.

RESULTS

We developed a hierarchical statistical model (HSM) that jointly models the uncertainty of the identified peptides and proteins and can be applied to any scoring system. With data sets of a standard mixture and the yeast proteome, we demonstrate that the HSM offers a reliable or at least conservative false discovery rate (FDR) estimate for peptide and protein identifications. The probability measure of HSM also offers a powerful discriminating score for peptide identification.

AVAILABILITY

The algorithm is available upon request from the authors.

Combined enzymatic and data mining approaches for comprehensive phosphoproteome analyses: application to cell signaling events of interferon-gamma-stimulated macrophages

Protein phosphorylation is a central cell signaling event that underlies a broad spectrum of key physiological processes. Advances in affinity chromatography and mass spectrometry are now providing the ability to identify and quantitate thousands of phosphorylation sites simultaneously. Comprehensive phosphoproteome analyses present sizable analytical challenges in view of suppression effects of phosphopeptides and the variable quality of MS/MS spectra. This work presents an integrated enzymatic and data mining approach enabling the comprehensive detection of native and putative phosphopeptides following alkaline phosphatase digestion of titanium dioxide (TiO2)-enriched cell extracts. The correlation of retention times of more than 750 phospho- and dephosphopeptide pairs from J774 macrophage cell extracts indicated that removal of the phosphate groups can impart a gain or a loss in hydrophobicity that is partly explained by the formation of a salt bridge with proximal amino groups. Dephosphorylation also led to an average 2-fold increase in MS sensitivity that facilitated peptide sequencing. More importantly, alkaline phosphatase digestion enhanced the overall population of putative phosphopeptides from TiO2-enriched cell extracts providing a unique approach to profile multiphosphorylated cognates that would have remained otherwise undetected. The application of this approach is demonstrated for differential phosphoproteome analyses of mouse macrophages exposed to interferon-gamma for 5 min. TiO2 enrichment enabled the identification of 1143 phosphopeptides from 432 different proteins of which 125 phosphopeptides showed a 2-fold change upon interferon-gamma exposure. The use of alkaline phosphatase nearly doubled the number of putative phosphopeptides assignments leading to the observation of key interferon-gamma signaling events involved in vesicle trafficking, production of reactive oxygen species, and mRNA translation.

Implementation and application of a versatile clustering tool for tandem mass spectrometry data

High-throughput proteomics experiments typically generate large amounts of peptide fragmentation mass spectra during a single experiment. There is often a substantial amount of redundant fragmentation of the same precursors among these spectra, which is usually considered a nuisance. We here discuss the potential of clustering and merging redundant spectra to turn this redundancy into a useful property of the dataset. To this end, we have created the first general-purpose, freely available open-source software application for clustering and merging MS/MS spectra. The application also introduces a novel approach to calculating the similarity of fragmentation mass spectra that takes into account the increased precision of modern mass spectrometers, and we suggest a simple but effective improvement to single-linkage clustering. The application and the novel algorithms are applied to several real-life proteomic datasets and the results are discussed. An analysis of the influence of the different algorithms available and their parameters is given, as well as a number of important applications of the overall approach.

Cardioinductive network guiding stem cell differentiation revealed by proteomic cartography of tumor necrosis factor alpha-primed endodermal secretome

In the developing embryo, instructive guidance from the ventral endoderm secures cardiac program induction within the anterolateral mesoderm. Endoderm-guided cardiogenesis, however, has yet to be resolved at the proteome level. Here, through cardiopoietic priming of the endoderm with the reprogramming cytokine tumor necrosis factor alpha (TNFalpha), candidate effectors of embryonic stem cell cardiac differentiation were delineated by comparative proteomics. Differential two-dimensional gel electrophoretic mapping revealed that more than 75% of protein species increased >1.5-fold in the TNFalpha-primed versus unprimed endodermal secretome. Protein spot identification by linear ion trap quadrupole (LTQ) tandem mass spectrometry (MS/MS) and validation by shotgun LTQ-Fourier transform MS/MS following multidimensional chromatography mapped 99 unique proteins from 153 spot assignments. A definitive set of 48 secretome proteins was deduced by iterative bioinformatic screening using algorithms for detection of canonical and noncanonical indices of secretion. Protein-protein interaction analysis, in conjunction with respective expression level changes, revealed a nonstochastic TNFalpha-centric secretome network with a scale-free hierarchical architecture. Cardiovascular development was the primary developmental function of the resolved TNFalpha-anchored network. Functional cooperativity of the derived cardioinductive network was validated through direct application of the TNFalpha-primed secretome on embryonic stem cells, potentiating cardiac commitment and sarcomerogenesis. Conversely, inhibition of primary network hubs negated the procardiogenic effects of TNFalpha priming. Thus, proteomic cartography establishes a systems biology framework for the endodermal secretome network guiding stem cell cardiopoiesis.

Performance Characteristics of Electron Transfer Dissociation Mass Spectrometry

We performed a large scale study of electron transfer dissociation (ETD) performance, as compared with ion trap collision-activated dissociation (CAD), for peptides ranging from approximately 1000 to 5000 Da (n approximately 4000). These data indicate relatively little overlap in peptide identifications between the two methods ( approximately 12%). ETD outperformed CAD for all charge states greater than 2; however, regardless of precursor charge a linear decrease in percent fragmentation, as a function of increasing precursor m/z, was observed with ETD fragmentation. We postulate that several precursor cation attributes, including peptide length, charge distribution, and total mass, could be relevant players. To examine these parameters unique ETD-identified peptides were sorted by length, and the ratio of amino acid residues per precursor charge (residues/charge) was calculated. We observed excellent correlation between the ratio of residues/charge and percent fragmentation. For peptides of a given residue/charge ratio, there is no correlation between peptide mass and percent fragmentation; instead we conclude that the ratio of residues/charge is the main factor in determining a successful ETD outcome. As charge density decreases so does the probability of non-covalent interactions that can bind a newly formed c/z-type ion pair. Recently we have described a supplemental activation approach (ETcaD) to convert these non-dissociative electron transfer product ions to useful c- and z-type ions. Automated implementation of such methods should remove this apparent precursor m/z ceiling. Finally, we evaluated the role of ion density (both anionic and cationic) and reaction duration for an ETD experiment. These data indicate that the best performance is achieved when the ion trap is filled to its space charge limit with anionic reagents. In this largest scale study of ETD to date, ETD continues to show great promise to propel the field of proteomics and, for small- to medium-sized peptides, is highly complementary to ion trap CAD.

ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

Background

Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.

Results

ProtQuant was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. ProtQuant has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,ProtQuant implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.

Conclusion

ProtQuant is a tool for protein quantification built for multi-platform use with an intuitive user interface. ProtQuant efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, ProtQuant is available as a self-installing executable for the Windows environment used by many bench scientists.

PhosphoPep--a phosphoproteome resource for systems biology research in Drosophila Kc167 cells

The ability to analyze and understand the mechanisms by which cells process information is a key question of systems biology research. Such mechanisms critically depend on reversible phosphorylation of cellular proteins, a process that is catalyzed by protein kinases and phosphatases. Here, we present PhosphoPep, a database containing more than 10 000 unique high-confidence phosphorylation sites mapping to nearly 3500 gene models and 4600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line. This constitutes the most comprehensive phosphorylation map of any single source to date. To enhance the utility of PhosphoPep, we also provide an array of software tools that allow users to browse through phosphorylation sites on single proteins or pathways, to easily integrate the data with other, external data types such as protein-protein interactions and to search the database via spectral matching. Finally, all data can be readily exported, for example, for targeted proteomics approaches and the data thus generated can be again validated using PhosphoPep, supporting iterative cycles of experimentation and analysis that are typical for systems biology research.

Clinical proteomics in neurodegenerative diseases

Investigation of the human specimens is an essential element for understanding the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. The studies hold promise for identifying biomarkers for diagnosis and prognosis, elucidating disease mechanisms, and accelerating the development of new strategies for therapeutic intervention. Here, we review proteomics studies of human brain samples in light of recent advances of mass spectrometry, focusing on the general strategies for experimental design and analysis (e.g., sample pooling and replication, selection of proteomics platforms, and false discovery rate in data processing), because quantitative analysis of clinical samples is confounded by a number of variables, including genetic differences, antemortem and postmortem factors, and experimental errors. Diverse proteomics platforms are also discussed with respect to sensitivity, throughput, and accuracy. Regarding the enormous complexity of the human brain and the limitation of current proteomics technologies, it may be more practical to analyze a subset of proteome in a functional context, in order to facilitate the identification of important disease-related proteins in the substantial noise reflecting biological and technical variances.

Targeted analysis of protein termini

We describe a targeted analysis of protein isoforms by selective enrichment and identification of in vivo acetylated protein N-termini and protein C-termini. Our method allows the characterization of these protein termini regardless of their annotation in protein databases and requires no chemical derivatization. Using an iterative database search strategy that takes account of the enrichment protocol, 263 IPI annotated and 87 unpredicted acetylated N-termini were identified in the crude membrane fraction of human embryonic carcinoma cells. The N-acetylated peptides conform to the reported criteria for in vivo modification. In addition, 168 IPI annotated and 193 unpredicted C-termini were identified. Additionally, and for the first time, we also report on in vivo N-terminal propionylation. The significant number of unknown protein N- and C-termini suggests a high degree of novel transcription independent of annotated gene boundaries and/or specific protein processing. Biological relevance of several of these unpredicted protein termini could be curated from the literature, adding further weight to the argument to go beyond routine database search strategies. Our method will improve the correct annotation of genes and proteins in databases.

Analysis and validation of proteomic data generated by tandem mass spectrometry

The analysis of the large amount of data generated in mass spectrometry-based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.

Development of BIATECH-54 standard mixtures for assessment of protein identification and relative expression

Mixtures of known proteins have been very useful in the assessment and validation of methods for high-throughput (HTP) MS (MS/MS) proteomics experiments. However, these test mixtures have generally consisted of few proteins at near equal concentration or of a single protein at varied concentrations. Such mixtures are too simple to effectively assess the validity of error rates for protein identification and differential expression in HTP MS/MS studies. This work aimed at overcoming these limitations and simulating studies of complex biological samples. We introduced a pair of 54-protein standard mixtures of variable concentrations with up to a 1000-fold dynamic range in concentration and up to ten-fold expression ratios with additional negative controls (infinite expression ratios). These test mixtures comprised 16 off-the-shelf Sigma-Aldrich proteins and 38 Shewanella oneidensis proteins produced in-house. The standard proteins were systematically distributed into three main concentration groups (high, medium, and low) and then the concentrations were varied differently for each mixture within the groups to generate different expression ratios. The mixtures were analyzed with both low mass accuracy LCQ and high mass accuracy FT-LTQ instruments. In addition, these 54 standard proteins closely follow the molecular weight distributions of both bacterial and human proteomes. As a result, these new standard mixtures allow for a much more realistic assessment of approaches for protein identification and label-free differential expression than previous mixtures. Finally, methodology and experimental design developed in this work can be readily applied in future to development of more complex standard mixtures for HTP proteomics studies.

The effects of mass accuracy, data acquisition speed, and search algorithm choice on peptide identification rates in phosphoproteomics

Proteomic analyses via tandem mass spectrometry have been greatly enhanced by the recent development of fast, highly accurate instrumentation. However, successful application of these developments to high-throughput experiments requires careful optimization of many variables which adversely affect each other, such as mass accuracy and data collection speed. We examined the performance of three shotgun-style acquisition methods ranging in their data collection speed and use of mass accuracy in identifying proteins from yeast-derived complex peptide and phosphopeptide-enriched mixtures. We find that the combination of highly accurate precursor masses generated from one survey scan in the FT-ICR cell, coupled with ten data-dependent tandem MS scans in a lower-resolution linear ion trap, provides more identifications in both mixtures than the other examined methods. For phosphopeptide identifications in particular, this method identified over twice as many unique phosphopeptides as the second-ranked, lower-resolution method from triplicate 90-min analyses (744 +/- 50 vs. 308 +/- 50, respectively). We also examined the performance of four popular peptide assignment algorithms (Mascot, Sequest, OMSSA, and Tandem) in analyzing the results from both high-and low-resolution data. When compared in the context of a false positive rate of approximately 1%, the performance differences between algorithms were much larger for phosphopeptide analyses than for an unenriched, complex mixture. Based upon these findings, acquisition speed, mass accuracy, and the choice of assignment algorithm all largely affect the number of peptides and proteins identified in high-throughput studies.

Investigating MS2/MS3 matching statistics: a model for coupling consecutive stage mass spectrometry data for increased peptide identification confidence

Improvements in ion trap instrumentation have made n-dimensional mass spectrometry more practical. The overall goal of the study was to describe a model for making use of MS(2) and MS(3) information in mass spectrometry experiments. We present a statistical model for adjusting peptide identification probabilities based on the combined information obtained by coupling peptide assignments of consecutive MS(2) and MS(3) spectra. Using two data sets, a mixture of known proteins and a complex phosphopeptide-enriched sample, we demonstrate an increase in discriminating power of the adjusted probabilities compared with models using MS(2) or MS(3) data only. This work also addresses the overall value of generating MS(3) data as compared with an MS(2)-only approach with a focus on the analysis of phosphopeptide data.

PepSplice: cache-efficient search algorithms for comprehensive identification of tandem mass spectra

MOTIVATION

Tandem mass spectrometry allows for high-throughput identification of complex protein samples. Searching tandem mass spectra against sequence databases is the main analysis method nowadays. Since many peptide variations are possible, including them in the search space seems only logical. However, the search space usually grows exponentially with the number of independent variations and may therefore overwhelm computational resources.

RESULTS

We provide fast, cache-efficient search algorithms to screen large peptide search spaces including non-tryptic peptides, whole genomes, dozens of posttranslational modifications, unannotated point mutations and even unannotated splice sites. All these search spaces can be screened simultaneously. By optimizing the cache usage, we achieve a calculation speed that closely approaches the limits of the hardware. At the same time, we control the size of the overall search space by limiting the combinations of variations that can co-occur on the same peptide. Using a hypergeometric scoring scheme, we applied these algorithms to a dataset of 1 420 632 spectra. We were able to identify a considerable number of peptide variations within a modest amount of computing time on standard desktop computers.

Proteomic parsimony through bipartite graph analysis improves accuracy and transparency

Assembling peptides identified from LC-MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, naïve protein assembly can substantially overstate the number of proteins found in samples. We model the peptide-protein relationships in a bipartite graph and use efficient graph algorithms to identify protein clusters with shared peptides and to derive the minimal list of proteins. We test the effects of this parsimony analysis approach using MS/MS data sets generated from a defined human protein mixture, a yeast whole cell extract, and a human serum proteome after MARS column depletion. The results demonstrate that the bipartite parsimony technique not only simplifies protein lists but also improves the accuracy of protein identification. We use bipartite graphs for the visualization of the protein assembly results to render the parsimony analysis process transparent to users. Our approach also groups functionally related proteins together and improves the comprehensibility of the results. We have implemented the tool in the IDPicker package. The source code and binaries for this protein assembly pipeline are available under Mozilla Public License at the following URL: http://www.mc.vanderbilt.edu/msrc/bioinformatics/.

Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics

Background

In proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, ΔCn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now.

Results

In this study, we implemented a machine learning approach known as predictive genetic algorithm (GA) for the optimization of filtering criteria to maximize the number of identified peptides at fixed false-discovery rate (FDR) for SEQUEST database searching. As the FDR was directly determined by decoy database search scheme, the GA based optimization approach did not require any pre-knowledge on the characteristics of the data set, which represented significant advantages over statistical approaches such as PeptideProphet. Compared with PeptideProphet, the GA based approach can achieve similar performance in distinguishing true from false assignment with only 1/10 of the processing time. Moreover, the GA based approach can be easily extended to process other database search results as it did not rely on any assumption on the data.

Conclusion

Our results indicated that filtering criteria should be optimized individually for different samples. The new developed software using GA provides a convenient and fast way to create tailored optimal criteria for different proteome samples to improve proteome coverage.

On-line 1D and 2D porous layer open tubular/LC-ESI-MS using 10-microm-i.d. poly(styrene-divinylbenzene) columns for ultrasensitive proteomic analysis

Following on our recent work, on-line one-dimensional (1D) and two-dimensional (2D) porous layer open tubular/liquid chromatography-electrospray ionization-mass spectrometry (PLOT/LC-ESI-MS) platforms using 3.2 mx10 microm i.d. poly(styrene-divinylbenzene) (PS-DVB) PLOT columns have been developed to provide robust, high-performance, and ultrasensitive proteomic analysis. With the use of a PicoClear tee, the dead volume connection between a 50 microm i.d. PS-DVB monolithic micro-SPE column and the PLOT column was minimized. The micro-SPE/PLOT column assembly provided a separation performance similar to that obtained with direct injection onto the PLOT column at a mobile phase flow rate of 20 nL/min. The trace analysis potential of the platform was evaluated using an in-gel tryptic digest sample of a gel fraction (15-40 kDa) of a cervical cancer (SiHa) cell line. As an example of the sensitivity of the system, approximately 2.5 ng of protein in 2 microL of solution, an amount corresponding to 20 SiHa cells, was subjected to on-line micro-SPE-PLOT/LC-ESI-MS/MS analysis using a linear ion trap MS. A total of 237 peptides associated with 163 unique proteins were identified from a single analysis when using stringent criteria associated with a false positive rate of less than 1%. The number of identified peptides and proteins increased to 638 and 343, respectively, as the injection amount was raised to approximately 45 ng of protein, an amount corresponding to 350 SiHa cells. In comparison, only 338 peptides and 231 unique proteins were identified (false positive rate again less than 1%) from 750 ng of protein from the identical gel fraction, an amount corresponding to 6000 SiHa cells, using a typical 15 cmx75 microm i.d. packed capillary column. The greater sensitivity, higher recovery, and higher resolving power of the PLOT column resulted in the increased number of identifications from only approximately 5% of the injected sample amount. The resolving power of the micro-SPE/PLOT assembly was further extended by 2D chromatography via combination of the high-efficiency reversed-phase PLOT column with strong cation-exchange chromatography (SCX). As an example, 1071 peptides associated with 536 unique proteins were identified from 75 ng of protein from the same gel fraction, an amount corresponding to 600 cells, using five ion-exchange fractions in on-line 2D SCX-PLOT/LC-MS. The 2D system, implemented in an automated format, led to simple and robust operation for proteomic analysis. These promising results demonstrate the potential of the PLOT column for ultratrace analysis.

Quantification of the synaptosomal proteome of the rat cerebellum during post-natal development

Large-scale proteomic analysis of the mammalian brain has been successfully performed with mass spectrometry techniques, such as Multidimensional Protein Identification Technology (MudPIT), to identify hundreds to thousands of proteins. Strategies to efficiently quantify protein expression levels in the brain in a large-scale fashion, however, are lacking. Here, we demonstrate a novel quantification strategy for brain proteomics called SILAM (Stable Isotope Labeling in Mammals). We utilized a (15)N metabolically labeled rat brain as an internal standard to perform quantitative MudPIT analysis on the synaptosomal fraction of the cerebellum during post-natal development. We quantified the protein expression level of 1138 proteins in four developmental time points, and 196 protein alterations were determined to be statistically significant. Over 50% of the developmental changes observed have been previously reported using other protein quantification techniques, and we also identified proteins as potential novel regulators of neurodevelopment. We report the first large-scale proteomic analysis of synaptic development in the cerebellum, and we demonstrate a useful quantitative strategy for studying animal models of neurological disease.

Advances in proteomic workflows for systems biology

Mass spectrometry, specifically the analysis of complex peptide mixtures by liquid chromatography and tandem mass spectrometry (shotgun proteomics) has been at the centre of proteomics research for the past decade. To overcome some of the fundamental limitations of the approach, including its limited sensitivity and high degree of redundancy, new proteomic workflows are being developed. Among these, targeting methods in which specific peptides are selectively isolated, identified and quantified are particularly promising. Here we summarize recent incremental advances in shotgun proteomic methods and outline emerging targeted workflows. The development of the target-driven approaches with their ability to detect and quantify identical, non-redundant sets of proteins in multiple repeat analyses will be crucially important for the application of proteomics to biomarker discovery and validation, and to systems biology research.

Comparison of Electrokinetics-Based Multidimensional Separations Coupled with Electrospray Ionization-Tandem Mass Spectrometry for Characterization of Human Salivary Proteins

The foundation for saliva-based diagnostics is the development of a complete catalog of secreted proteins detectable in saliva. Besides protein complexity, the greatest bioanalytical challenge facing comprehensive analysis of saliva samples is related to the large variation of protein relative abundances including the presence of high-abundance proteins such as amylases, mucins, proline-rich proteins (PRPs), and secretory IgA complex. Among a number of electrokinetic separation techniques, transient capillary isotachophoresis/capillary zone electrophoresis (CITP/CZE) specifically targets trace amounts of proteins and thus reduces the range of relative protein abundances for providing unparallel advantages toward the identification of low-abundance proteins. By employing a CITP/CZE-based multidimensional separation platform coupled with electrospray ionization-tandem mass spectrometry (ESI-tandem MS), a total of 6112 fully tryptic peptides are sequenced at a 1% false discovery rate (FDR), leading to the identification of 1479 distinct human SwissProt protein entries. By comparing with capillary isoelectric focusing (CIEF) as another electrokinetics-based stacking approach, CITP/CZE not only offers a broad field of application but also is less prone to protein/peptide precipitation during the analysis. The ultrahigh resolving power of CITP/CZE is evidenced by the large number of distinct peptide identifications measured from each CITP fraction together with the low peptide fraction overlapping among identified peptides. Furthermore, when evaluating the protein sequence coverage by the number of distinct peptides mapping to each protein identification, the CITP-based proteome technology similarly achieves the superior performance with 674 proteins (46%) having three or more distinct peptides, 288 (19%) having two distinct peptides, and 517 (35%) having a single distinct peptide.

Selective Enrichment of Tryptophan-Containing Peptides from Protein Digests Employing a Reversible Derivatization with Malondialdehyde and Solid-Phase Capture on Hydrazide Beads

A method for the selective enrichment of tryptophan-containing peptides from complex peptide mixtures such as protein digests is presented. It is based on the reversible reaction of tryptophan with malondialdehyde and trapping of the derivatized Trp-peptides on hydrazide beads via the free aldehyde group of the modified peptides. The peptides are subsequently recovered in their native form by specific cleavage reactions for further (mass spectrometric) analysis. The method was optimized and evaluated using a tryptic digest of a mixture of 10 model proteins, demonstrating a significant reduction in sample complexity while still allowing the identification of all proteins. The applicability of the tryptophan-specific enrichment procedure to complex biological samples is demonstrated for a total yeast cell lysate. Analysis of the processed fraction by 1D-LC-MS/MS confirms the specificity of the enrichment procedure, as more than 85% of the peptides recovered from the enrichment step contained tryptophan. The reduction in sample complexity also resulted in the identification of additional proteins in comparison to the untreated lysate.

A novel cysteine cross-linking method reveals a direct association between claudin-1 and tetraspanin CD9

Tetraspanins serve as molecular organizers of multiprotein microdomains in cell membranes. Hence to understand functions of tetraspanin proteins, it is critical to identify laterally interacting partner proteins. Here we used a novel technical approach involving exposure and cross-linking of membrane-proximal cysteines coupled with LC-MS/MS protein identification. In this manner we identified nine potential tetraspanin CD9 partners, including claudin-1. Chemical cross-linking yielded a CD9-claudin-1 heterodimer, thus confirming direct association and adding claudin-1 to the short list of proteins that can directly associate with CD9. Interaction of CD9 (and other tetraspanins) with claudin-1 was supported by subcellular colocalization and was confirmed in multiple cell lines, although other claudins (claudin-2, -3, -4, -5, and -7) associated to a much lesser extent. Moreover claudin-1 was distributed very similarly to CD9 in sucrose gradients and, like CD9, was released from A431 and A549 cells upon cholesterol depletion. These biochemical features of claudin-1 are characteristic of tetraspanin microdomain proteins. Although claudins are major structural components of intercellular tight junctions, CD9-claudin-1 complexes did not reside in tight junctions, and depletion of key tetraspanins (CD9 and CD151) by small interfering RNA had no effect on paracellular permeability. However, tetraspanin depletion did cause a marked decrease in the stability of newly synthesized claudin-1. In conclusion, these results (a) validate a technical approach that appears to be particularly well suited for identifying protein partners directly associated with tetraspanins or with other proteins that contain membrane-proximal cysteines and (b) provide insight into how non-junctional claudins may be regulated in the context of tetraspanin-enriched microdomains.