Peptizer, a tool for assessing false positive peptide identifications and manually validating selected results

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

N-terminomics identifies Prli42 as a membrane miniprotein conserved in Firmicutes and critical for stressosome activation in Listeria monocytogenes

To adapt to changing environments, bacteria have evolved numerous pathways that activate stress response genes. In Gram-positive bacteria, the stressosome, a cytoplasmic complex, relays external cues and activates the sigma B regulon. The stressosome is structurally well-characterized in Bacillus, but how it senses stress remains elusive. Here, we report a genome-wide N-terminomic approach in Listeria that strikingly led to the discovery of 19 internal translation initiation sites and 6 miniproteins, among which one, Prli42, is conserved in Firmicutes. Prli42 is membrane-anchored and interacts with orthologues of Bacillus stressosome components. We reconstituted the Listeria stressosome in vitro and visualized its supramolecular structure by electron microscopy. Analysis of a series of Prli42 mutants demonstrated that Prli42 is important for sigma B activation, bacterial growth following oxidative stress and for survival in macrophages. Taken together, our N-terminonic approach unveiled Prli42 as a long-sought link between stress and the stressosome.

Combined use of peptide ion and normalized delta scores to evaluate milk authenticity by ion-trap based proteomics coupled with error tolerant searching

A fundamental issue in proteomics is the peptide identification by database searching and the assessment of the goodness of fit between experimental and theoretical data. Despite the different number of ways to measure the quality of search results, the definition of a scoring criterion is still highly desirable in ion-trap based proteomics. Indeed, in order to fully take advantage of a low resolution MS/MS dataset, it is essential to strike a balance between greater information capture and reduced number of incorrect peptide assignments. In addition, the development of user-specified rules is a crucial aspect when very similar proteins of the same family are analyzed in order to infer the origin species. In this study, a post-processing validation scheme is provided for the evaluation of proteomic data in shot-gun ion-trap proteomics, when a flexible database searching based on the error tolerant mode is adopted in combination with a low-specificity enzyme to maximize sequence coverage. To validate peptide assignments, we used standard β-casein digested with trypsin/chymotrypsin or trypsin alone and the popular search engine MASCOT to identify the relevant (known) peptide sequences. A linear combination between peptide ion score and normalized delta score (i.e. the difference between the best and the second best ion score, divided by the best score) is proposed to increase the accuracy in sequence assignments from low-resolution tandem mass spectra. Finally, the optimized post-processing database validation was successfully applied to the direct analysis of milk tryptic/chymotryptic digests of different origin, without resorting to two-dimensional electrophoresis that is usually performed for protein separation in ion-trap proteomics. The identification of species-specific amino acidic sequences among the validated peptide spectrum matches has allowed to fully discriminate between the animal species, so evaluating accurately the milk authenticity.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

Fast profiling of protease specificity reveals similar substrate specificities for cathepsins K, L and S

Proteases are important effectors of numerous physiological and pathological processes. Reliable determination of a protease's specificity is crucial to understand protease function and to develop activity-based probes and inhibitors. During the last decade, various proteomic approaches for profiling protease substrate specificities were reported. Although most of these approaches can identify up to thousands of substrate cleavage events in a single experiment, they are often time consuming and methodologically challenging as some of these approaches require rather complex sample preparation procedures. For such reasons their application is often limited to those labs that initially introduced them. Here, we report on a fast and simple approach for proteomic profiling of protease specificities (fast profiling of protease specificity (FPPS)), which can be applied to complex protein mixtures. FPPS is based on trideutero-acetylation of novel N-termini generated by the action of proteases and subsequent peptide fractionation on Stage Tips containing ion-exchange and reverse phase chromatographic resins. FPPS can be performed in 2 days and does not require extensive fractionation steps. Using this approach, we have determined the specificity profiles of the cysteine cathepsins K, L and S. We further validated our method by comparing the results with the specificity profiles obtained by the N-terminal combined fractional diagonal chromatography method. This comparison pointed to almost identical substrate specificities for all three cathepsins and confirmed the reliability of the FPPS approach. All MS data have been deposited in the ProteomeXchange with identifiers PXD001536 and PXD001553 (http://proteomecentral.proteomexchange.org/dataset/PXD001536; http://proteomecentral.proteomexchange.org/dataset/PXD001553).

Functional neuropeptidomics in invertebrates

Neuropeptides are key messengers in almost all physiological processes. They originate from larger precursors and are extensively processed to become bioactive. Neuropeptidomics aims to comprehensively identify the collection of neuropeptides in an organism, organ, tissue or cell. The neuropeptidome of several invertebrates is thoroughly explored since they are important model organisms (and models for human diseases), disease vectors and pest species. The charting of the neuropeptidome is the first step towards understanding peptidergic signaling. This review will first discuss the latest developments in exploring the neuropeptidome. The physiological roles and modes of action of neuropeptides can be explored in two ways, which are largely orthogonal and therefore complementary. The first way consists of inferring the functions of neuropeptides by a forward approach where neuropeptide profiles are compared under different physiological conditions. Second is the reverse approach were neuropeptide collections are used to screen for receptor-binding. This is followed by localization studies and functional tests. This review will focus on how these different functional screening methods contributed to the field of invertebrate neuropeptidomics and expanded our knowledge of peptidergic signaling. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

GRIM REAPER peptide binds to receptor kinase PRK5 to trigger cell death in Arabidopsis

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor.

Problems inherent to a meta-analysis of proteomics data: a case study on the plants' response to Cd in different cultivation conditions

This meta-analysis focuses on plant-proteome responses to cadmium (Cd) stress. Initially, some general topics related to a proteomics meta-analysis are discussed: (1) obstacles encountered during data analysis, (2) a consensus in proteomic research, (3) validation and good reporting practices for protein identification and (4) guidelines for statistical analysis of differentially abundant proteins. In a second part, the Cd responses in leaves and roots obtained from a proteomics meta-analysis are discussed in (1) a time comparison (short versus long term exposure), and (2) a culture comparison (hydroponics versus soil cultivation). Data of the meta-analysis confirmed the existence of an initial alarm phase upon Cd exposure. Whereas no metabolic equilibrium is established in hydroponically exposed plants, an equilibrium seems to be manifested in roots of plants grown in Cd-contaminated soil after long term exposure. In leaves, the carbohydrate metabolism is primarily affected independent of the exposure time and the cultivation method. In addition, a metabolic shift from CO2-fixation towards respiration is manifested, independent of the cultivation system. Finally, some ideas for the improvement of proteomics setups and for comparisons between studies are discussed.

BIOLOGICAL SIGNIFICANCE

This meta-analysis focuses on the plant responses to Cd stress in leaves and roots at the proteome level. This meta-analysis points out the encountered obstacles when performing a proteomics meta-analysis related to inherent technologies, but also related to experimental setups. Furthermore, the question is addressed whether an extrapolation of results obtained in hydroponic cultivation towards soil-grown plants is possible.

Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective

Data processing, management and visualization are central and critical components of a state of the art high-throughput mass spectrometry (MS)-based proteomics experiment, and are often some of the most time-consuming steps, especially for labs without much bioinformatics support. The growing interest in the field of proteomics has triggered an increase in the development of new software libraries, including freely available and open-source software. From database search analysis to post-processing of the identification results, even though the objectives of these libraries and packages can vary significantly, they usually share a number of features. Common use cases include the handling of protein and peptide sequences, the parsing of results from various proteomics search engines output files, and the visualization of MS-related information (including mass spectra and chromatograms). In this review, we provide an overview of the existing software libraries, open-source frameworks and also, we give information on some of the freely available applications which make use of them. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

The acetylproteome of Gram-positive model bacterium Bacillus subtilis

N(ε) -lysine acetylation, a reversible and highly regulated PTM, has been shown to occur in the model Gram-negative bacteria Escherichia coli and Salmonella enterica. Here, we extend this acetylproteome analysis to Bacillus subtilis, a model Gram-positive bacterium. Through anti-acetyllysine antibody-based immunoseparation of acetylpeptides followed by nano-HPLC/MS/MS analysis, we identified 332 unique lysine-acetylated sites on 185 proteins. These proteins are mainly involved in cellular housekeeping functions such as central metabolism and protein synthesis. Fifity-nine of the lysine-acetylated proteins showed homology with lysine-acetylated proteins previously identified in E. coli, suggesting that acetylated proteins are more conserved. Notably, acetylation was found at or near the active sites predicted by Prosite signature, including SdhA, RocA, Kbl, YwjH, and YfmT, indicating that lysine acetylation may affect their activities. In 2-amino-3-ketobutyrate CoA ligase Kbl, a class II aminotransferase, a lysine residue involved in pyridoxal phosphate attachment was found to be acetylated. This data set provides evidence for the generality of lysine acetylation in eubacteria and opens opportunities to explore the consequences of acetylation modification on the molecular physiology of B. subtilis.

Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report

The workshop "Bioinformatics for Biotechnology Applications (HavanaBioinfo 2012)", held December 8-11, 2012 in Havana, aimed at exploring new bioinformatics tools and approaches for large-scale proteomics, genomics and chemoinformatics. Major conclusions of the workshop include the following: (i) development of new applications and bioinformatics tools for proteomic repository analysis is crucial; current proteomic repositories contain enough data (spectra/identifications) that can be used to increase the annotations in protein databases and to generate new tools for protein identification; (ii) spectral libraries, de novo sequencing and database search tools should be combined to increase the number of protein identifications; (iii) protein probabilities and FDR are not yet sufficiently mature; (iv) computational proteomics software needs to become more intuitive; and at the same time appropriate education and training should be provided to help in the efficient exchange of knowledge between mass spectrometrists and experimental biologists and bioinformaticians in order to increase their bioinformatics background, especially statistics knowledge.

Profiling protease activities by dynamic proteomics workflows

Proteases play prominent roles in many physiological processes and the pathogenesis of various diseases, which makes them interesting drug targets. To fully understand the functional role of proteases in these processes, it is necessary to characterize the target specificity of the enzymes, identify endogenous substrates and cleavage products as well as protease activators and inhibitors. The complexity of these proteolytic networks presents a considerable analytic challenge. To comprehensively characterize these systems, quantitative methods that capture the spatial and temporal distributions of the network members are needed. Recently, activity-based workflows have come to the forefront to tackle the dynamic aspects of proteolytic processing networks in vitro, ex vivo and in vivo. In this review, we will discuss how mass spectrometry-based approaches can be used to gain new insights into protease biology by determining substrate specificities, profiling the activity-states of proteases, monitoring proteolysis in vivo, measuring reaction kinetics and defining in vitro and in vivo proteolytic events. In addition, examples of future aspects of protease research that go beyond mass spectrometry-based applications are given.

Analysis of the resolution limitations of peptide identification algorithms

Proteome identification using peptide-centric proteomics techniques is a routinely used analysis technique. One of the most powerful and popular methods for the identification of peptides from MS/MS spectra is protein database matching using search engines. Significance thresholding through false discovery rate (FDR) estimation by target/decoy searches is used to ensure the retention of predominantly confident assignments of MS/MS spectra to peptides. However, shortcomings have become apparent when such decoy searches are used to estimate the FDR. To study these shortcomings, we here introduce a novel kind of decoy database that contains isobaric mutated versions of the peptides that were identified in the original search. Because of the supervised way in which the entrapment sequences are generated, we call this a directed decoy database. Since the peptides found in our directed decoy database are thus specifically designed to look quite similar to the forward identifications, the limitations of the existing search algorithms in making correct calls in such strongly confusing situations can be analyzed. Interestingly, for the vast majority of confidently identified peptide identifications, a directed decoy peptide-to-spectrum match can be found that has a better or equal match score than the forward match score, highlighting an important issue in the interpretation of peptide identifications in present-day high-throughput proteomics.

A case study on the comparison of different software tools for automated quantification of peptides

MS-driven proteomics has evolved over the past two decades to a high tech and high impact research field. Two distinct factors clearly influenced its expansion: the rapid growth of an arsenal of instrument and proteomic techniques that led to an explosion of high quality data and the development of software tools to analyze and interpret these data which boosted the number of scientific discoveries. In analogy with the benchmarking of new instruments and proteomic techniques, such software tools must be thoroughly tested and analyzed. Recently, new tools were developed for automatic peptide quantification in quantitative proteomic experiments. Here we present a case study where the most recent and frequently used tools are analyzed and compared.

Selecting protein N-terminal peptides by combined fractional diagonal chromatography

In recent years, procedures for selecting the N-terminal peptides of proteins with analysis by mass spectrometry have been established to characterize protease-mediated cleavage and protein α-N-acetylation on a proteomic level. As a pioneering technology, N-terminal combined fractional diagonal chromatography (COFRADIC) has been used in numerous studies in which these protein modifications were investigated. Derivatization of primary amines--which can include stable isotope labeling--occurs before trypsin digestion so that cleavage occurs after arginine residues. Strong cation exchange (SCX) chromatography results in the removal of most of the internal peptides. Diagonal, reversed-phase peptide chromatography, in which the two runs are separated by reaction with 2,4,6-trinitrobenzenesulfonic acid, results in the removal of the C-terminal peptides and remaining internal peptides and the fractionation of the sample. We describe here the fully matured N-terminal COFRADIC protocol as it is currently routinely used, including the most substantial improvements (including treatment with glutamine cyclotransferase and pyroglutamyl aminopeptidase to remove pyroglutamate before SCX, and a sample pooling scheme to reduce the overall number of liquid chromatography-tandem mass spectrometry analyses) that were made since its original publication. Completion of the N-terminal COFRADIC procedure takes ~5 d.

Bioinformatics analysis of a Saccharomyces cerevisiae N-terminal proteome provides evidence of alternative translation initiation and post-translational N-terminal acetylation

Initiation of protein translation is a well-studied fundamental process, albeit high-throughput and more comprehensive determination of the exact translation initiation sites (TIS) was only recently made possible following the introduction of positional proteomics techniques that target protein N-termini. Precise translation initiation is of crucial importance, as truncated or extended proteins might fold, function, and locate erroneously. Still, as already shown for some proteins, alternative translation initiation can also serve as a regulatory mechanism. By applying N-terminal COFRADIC (combined fractional diagonal chromatography), we here isolated N-terminal peptides of a Saccharomyces cerevisiae proteome and analyzed both annotated and alternative TIS. We analyzed this N-terminome of S. cerevisiae which resulted in the identification of 650 unique N-terminal peptides corresponding to database annotated TIS. Furthermore, 56 unique N(α)-acetylated peptides were identified that suggest alternative TIS (MS/MS-based), while MS-based evidence of N(α)-acetylation led to an additional 33 such peptides. To improve the overall sensitivity of the analysis, we also included the 5' UTR (untranslated region) in-frame translations together with the yeast protein sequences in UniProtKB/Swiss-Prot. To ensure the quality of the individual peptide identifications, peptide-to-spectrum matches were only accepted at a 99% probability threshold and were subsequently analyzed in detail by the Peptizer tool to automatically ascertain their compliance with several expert criteria. Furthermore, we have also identified 60 MS/MS-based and 117 MS-based N(α)-acetylated peptides that point to N(α)-acetylation as a post-translational modification since these peptides did not start nor were preceded (in their corresponding protein sequence) by a methionine residue. Next, we evaluated consensus sequence features of nucleic acids and amino acids across each of these groups of peptides and evaluated the results in the context of publicly available data. Taken together, we present a list of 706 annotated and alternative TIS for yeast proteins and found that under normal growth conditions alternative TIS might (co)occur in S. cerevisiae in roughly one tenth of all proteins. Furthermore, we found that the nucleic acid and amino acid features proximate to these alternative TIS favor either guanine or adenine nucleotides following the start codon or acidic amino acids following the initiator methionine. Finally, we also observed an unexpected high number of N(α)-acetylated peptides that could not be related to TIS and therefore suggest events of post-translational N(α)-acetylation.

Proteomic analysis and discovery using affinity proteomics and mass spectrometry

Antibody-based microarrays are a rapidly evolving affinity-proteomic methodology that recently has shown great promise in clinical applications. The resolution of these proteomic analyses is, however, directly related to the number of data-points, i.e. antibodies, included on the array. Currently, this is a key bottleneck because of limited availability of numerous highly characterized antibodies. Here, we present a conceptually new method, denoted global proteome survey, opening up the possibility to probe any proteome in a species-independent manner while still using a limited set of antibodies. We use context-independent-motif-specific antibodies directed against short amino acid motifs, where each motif is present in up to a few hundred different proteins. First, the digested proteome is exposed to these antibodies, whereby motif-containing peptides are enriched, which then are detected and identified by mass spectrometry. In this study, we profiled extracts from human colon tissue, yeast cells lysate, and mouse liver tissue to demonstrate proof-of-concept.

PepServe: a web server for peptide analysis, clustering and visualization

Peptides, either as protein fragments or as naturally occurring entities are characterized by their sequence and function features. Many times the researchers need to massively manage peptide lists concerning protein identification, biomarker discovery, bioactivity, immune response or other functionalities. We present a web server that manages peptide lists in terms of feature analysis as well as interactive clustering and visualization of the given peptides. PepServe is a useful tool in the understanding of the peptide feature distribution among a group of peptides. The PepServe web application is freely available at http://bioserver-1.bioacademy.gr/Bioserver/PepServe/.

Peptide identification quality control

Identification of large proteomics data sets is routinely performed using sophisticated software tools called search engines. Yet despite the importance of the identification process, its configuration and execution is often performed according to established lab habits, and is mostly unsupervised by detailed quality control. In order to establish easily obtainable quality control criteria that can be broadly applied to the identification process, we here introduce several simple quality control methods. An unbiased quality control of identification parameters will be conducted using target/decoy searches providing significant improvement over identification standards. MASCOT identifications were for instance increased by 13% at a constant level of confidence. The target/decoy approach can however not be universally applied. We therefore also quality control the application of this strategy itself, providing useful and intuitive metrics for evaluating the precision and robustness of the obtained false discovery rate.

Data management in mass spectrometry-based proteomics

Mass spectrometry-based proteomics has made the transition from the analysis of a single or only a few proteins per experiment to a high-throughput platform for discovery/validation that can analyze several hundreds to thousands of proteins in a single experiment. This increase in analytical capability hinged on four main components: (1) innovative methodologies, (2) improved instruments, (3) ready availability of comprehensive protein sequence databases, and (4) development of sophisticated software algorithms to match fragmentation mass spectra against these databases. But as the throughput of the approach increased, so did the necessity to manage and automate the data processing workflow. Indeed, modern instruments generate tens of thousands of fragmentation spectra per hour, providing a substantial bioinformatics challenge. This chapter provides insight into the specifics of this challenge, by looking at a typical workflow, the data types and user roles involved, and a broad overview of available software solutions. Finally, the increasingly important link between a local data management system and the global, centralized dissemination of proteomics data is discussed.

compomics-utilities: an open-source Java library for computational proteomics

Background

The growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool.

Results

In order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development.

Conclusions

As a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.

Visualize: a free and open source multifunction tool for proteomics data analysis

A major challenge in the field of high-throughput proteomics is the conversion of the large volume of experimental data that is generated into biological knowledge. Typically, proteomics experiments involve the combination and comparison of multiple data sets and the analysis and annotation of these combined results. Although there are some commercial applications that provide some of these functions, there is a need for a free, open source, multifunction tool for advanced proteomics data analysis. We have developed the Visualize program that provides users with the abilities to visualize, analyze, and annotate proteomics data; combine data from multiple runs, and quantitate differences between individual runs and combined data sets. Visualize is licensed under GNU GPL and can be downloaded from http://proteomics.mcw.edu/visualize. It is available as compiled client-based executable files for both Windows and Mac OS X platforms as well as PERL source code.

Proteomics databases and repositories

With the advent of more powerful and sensitive analytical techniques and instruments, the field of mass spectrometry based proteomics has seen a considerable increase in the amount of generated data. Correspondingly, the need to make these data publicly available in centralized online databases has also become more pressing. As a result, several such databases have been created, and steps are currently being taken to integrate these different systems under a single worldwide data-sharing umbrella. This chapter will discuss the importance of such databases and the necessary infrastructure that these databases require for efficient operation. Furthermore, the various kinds of information that proteomics databases can store will be described, along with the different types of databases that are available today. Finally, a selection of prominent repositories will be described in more detail, together with the international ProteomExchange consortium that is aimed at uniting all the different databases in a global data sharing collaboration.

Mass spectrometry-driven proteomics: an introduction

Proteins are reckoned to be the key actors in a living organism. By studying proteins, one engages into deciphering a complex series of events occurring during a protein's life span. This starts at the creation of a protein, which is tightly controlled on both a transcriptional (Williams and Tyler, 2007, Curr Opin Genet Dev 17, 88-93) and a translational level (Van Der Kelen et al., 2009, Crit Rev Biochem Mol Biol 44, 143-168). During translation, a primary strand of amino acids undergoes a complex folding process in order to obtain a native three-dimensional protein structure (Gross et al., 2003, Cell 115, 739-750). Proteins take on a plethora of functions, such as complex formation, receptor activity, and signal transduction, which ultimately adds up to a cellular phenotype. Consequently, protein analysis is of major interest in molecular biology and involves annotating their presence and localization, as well as their modification state and biochemical context. To accomplish this, many methods have been developed over the last decades, and their general principles and important recent advances in large-scale protein analysis or proteomics are discussed in this review.

Bioinformatics challenges in mass spectrometry-driven proteomics

Mass spectrometry-based proteomics has become an essential part of the analytical toolbox of the life sciences. With the ability to identify and quantify hundreds to thousands of proteins in high throughput, the field has contributed its fair share to the data avalanche coming from the so-called omics fields. As a result, the challenges involved in processing and managing this flood of data have grown as well. This chapter will point out and discuss these challenges, starting from the processing of raw mass spectrometry data into peaks, over the identification of peptides and proteins, to the quantification of the identified molecules. Finally, the informatics aspects of the nascent field of targeted proteomics are outlined as well.

A quantitative proteomics design for systematic identification of protease cleavage events

We present here a novel proteomics design for systematic identification of protease cleavage events by quantitative N-terminal proteomics, circumventing the need for time-consuming manual validation. We bypass the singleton detection problem of protease-generated neo-N-terminal peptides by introducing differential isotopic proteome labeling such that these substrate reporter peptides are readily distinguished from all other N-terminal peptides. Our approach was validated using the canonical human caspase-3 protease and further applied to mouse cathepsin D and E substrate processing in a mouse dendritic cell proteome, identifying the largest set of protein protease substrates ever reported and gaining novel insight into substrate specificity differences of these cathepsins.

Who gets cut during cell death?

The recent introduction of positional proteomics made it possible to screen for protease processing events on a proteome-wide scale. As a highly regulated and protease-dependent process, cell death has been particularly well-studied with these emerging technologies. This review provides an overview of the results obtained at the exciting interface between proteomics, protease biology and cell death.

Peptide and protein quantification: a map of the minefield

The increasing popularity of gel-free proteomics technologies has created a strong demand for compatible quantitative analysis methods. As a result, a plethora of different techniques has been proposed to perform gel-free quantitative analysis of proteomics samples. Each of these methods comes with certain strengths and shortcomings, and they often are dedicated to a specific purpose. This review will present a brief overview of the main methods, organized by their underlying concepts, and will discuss the issues they raise with a focus on data processing. Finally, we will list the available software that can help with the data processing from quantitative experiments. We hope that this review will thus enable researchers to find the most appropriate method available for their research objectives, and can also serve as a basis for creating a reliable data processing strategy.

XTandem Parser: an open-source library to parse and analyse X!Tandem MS/MS search results

Identification of proteins by MS plays an important role in proteomics. A crucial step concerns the identification of peptides from MS/MS spectra. The X!Tandem Project (http://www.thegpm.org/tandem) supplies an open-source search engine for this purpose. In this study, we present an open-source Java library called XTandem Parser that parses X!Tandem XML result files into an easily accessible and fully functional object model (http://xtandem-parser.googlecode.com). In addition, a graphical user interface is provided that functions as a usage example and an end-user visualization tool.

MS-driven protease substrate degradomics

Proteolytic processing has recently received increased attention in the field of signal propagation and cellular differentiation. Because of its irreversible nature, protein cleavage has been associated with committed steps in cell function. One aspect of protease biology that boomed the past few years is the detailed characterization of protease substrates by both shotgun as well as targeted MS-driven proteomics techniques. The most promising techniques are discussed in this review and we further elaborate on the bioinformatics challenges that accompany mainly qualitative, MS-driven protease substrate degradome studies.

Proteomic techniques and activity-based probes for the system-wide study of proteolysis

Proteolysis constitutes a major post-translational modification but specificity and substrate selectivity of numerous proteases have remained elusive. In this review, we highlight how advanced techniques in the areas of proteomics and activity-based probes can be used to investigate i) protease active site specificity; ii) protease in vivo substrates; iii) protease contribution to proteome homeostasis and composition; and iv) detection and localization of active proteases. Peptide libraries together with genetical or biochemical selection have traditionally been used for active site profiling of proteases. These are now complemented by proteome-derived peptide libraries that simultaneously determine prime and non-prime specificity and characterize subsite cooperativity. Cell-contextual discovery of protease substrates is rendered possible by techniques that isolate and quantitate protein termini. Here, a novel approach termed Terminal Amine Isotopic Labeling of Substrates (TAILS) provides an integrated platform for substrate discovery and appropriate statistical evaluation of terminal peptide identification and quantification. Proteolytically generated carboxy-termini can now also be analyzed on a proteome-wide level. Proteolytic regulation of proteome composition is monitored by quantitative proteomic approaches employing stable isotope coding or label free quantification. Activity-based probes specifically recognize active proteases. In proteomic screens, they can be used to detect and quantitate proteolytic activity while their application in cellular histology allows to locate proteolytic activity in situ. Activity-based probes - especially in conjunction with positron emission tomography - are also promising tools to monitor proteolytic activities on an organism-wide basis with a focus on in vivo tumor imaging. Together, this array of methodological possibilities enables unveiling physiological protease substrate repertoires and defining protease function in the cellular- and organism-wide context.

In vitro and in vivo protein-bound tyrosine nitration characterized by diagonal chromatography

A new proteomics technique for analyzing 3-nitrotyrosine-containing peptides is presented here. This technique is based on the combined fractional diagonal chromatography peptide isolation procedures by which specific classes of peptides are isolated following a series of identical reverse-phase HPLC separation steps. Here dithionite is used to reduce 3-nitrotyrosine to 3-aminotyrosine peptides, which thereby become more hydrophilic. Our combined fractional diagonal chromatography technique was first applied to characterize tyrosine nitration in tetranitromethane-modified BSA and further led to a high quality list of 335 tyrosine nitration sites in 267 proteins in a peroxynitrite-treated lysate of human Jurkat cells. We then analyzed a serum sample of a C57BL6/J mouse in which septic shock was induced by intravenous Salmonella infection and identified six in vivo nitration events in four serum proteins, thereby illustrating that our technique is sufficiently sensitive to identify rare in vivo tyrosine nitration sites in a very complex background.

Getting a grip on proteomics data - Proteomics Data Collection (ProDaC)

In proteomics, rapid developments in instrumentation led to the acquisition of increasingly large data sets. Correspondingly, ProDaC was founded in 2006 as a Coordination Action project within the 6th European Union Framework Programme to support data sharing and community-wide data collection. The objectives of ProDaC were the development of documentation and storage standards, setup of a standardized data submission pipeline and collection of data. Ending in March 2009, ProDaC has delivered a comprehensive toolbox of standards and computer programs to achieve these goals.

Strategy for surveying the proteome using affinity proteomics and mass spectrometry

Antibody-based microarrays is a rapidly evolving technology that has gone from the first proof-of-concept studies to more demanding proteome profiling applications, during the last years. Miniaturized microarrays can be printed with large number of antibodies harbouring predetermined specificities, capable of targeting high- as well as low-abundant analytes in complex, nonfractionated proteomes. Consequently, the resolution of such proteome profiling efforts correlate directly to the number of antibodies included, which today is a key limiting factor. To overcome this bottleneck and to be able to perform in-depth global proteome surveys, we propose to interface affinity proteomics with MS-based read-out, as outlined in this technical perspective. Briefly, we have defined a range of peptide motifs, each motif being present in 5-100 different proteins. In this manner, 100 antibodies, binding 100 different motifs commonly distributed among different proteins, would potentially target a protein cluster of 10(4) individual molecules, i.e. around 50% of the nonredundant human proteome. Notably, these motif-specific antibodies would be directly applicable to any proteome in a specie independent manner and not biased towards abundant proteins or certain protein classes. The biological sample is digested, exposed to these immobilized antibodies, whereby motif-containing peptides are specifically captured, enriched and subsequently detected and identified using MS.