SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches

Effective selenium detoxification in the seed proteins of a hyperaccumulator plant: the analysis of selenium-containing proteins of monkeypot nut (Lecythis minor) seeds

A shotgun proteomic approach was applied to characterize the selenium (Se)-containing proteins of the selenium hyperaccumulator monkeypot nut (Lecythis minor) seeds. The exceptionally high Se content (>4,000 mg kg(-1)) of the sample enabled a straightforward procedure without the need for multiple preconcentration and fractionation steps. The proteins identified were sulfur-rich seed proteins, namely, 11S globulin (Q84ND2), 2S albumin (B6EU54), 2S sulfur-rich seed storage proteins (P04403 and P0C8Y8) and a 11S globulin-like protein (A0EM48). Database directed search for theoretically selenium-containing peptides was assisted by manual spectra evaluation to achieve around 25% coverage on sulfur analogues. Remarkable detoxification mechanisms on the proteome level were revealed in the form of multiple selenomethionine-methionine substitution and the lack of selenocysteine residues. The degree of selenomethionine substitution could be characterized by an exponential function that implies the inhibition of protein elongation by selenomethionine. Our results contribute to the deeper understanding of selenium detoxification procedures in hyperaccumulator plants.

Identification of B6T173 (ZmPrx35) as the prevailing peroxidase in highly insect-resistant maize (Zea mays, p84C3) kernels by activity-directed purification

Plant peroxidases (PODs) are involved in diverse physiological processes, including defense against pathogens and insects. Contrary to their biological importance, only very few plant PODs have been proven on protein level, because their low abundance makes them difficult to detect in standard proteomics work-flows. A statistically significant positive correlation between POD activity and post-harvest insect resistance has been found for maize (Zea mays, p84C3) kernels. In combining activity-directed protein purification, genomic and proteomic tools we found that protein B6T173 (ZmPrx35) is responsible for the majority of the POD activity of the kernel. We successfully produced recombinant ZmPrx35 protein in Escherichia coli and demonstrate both, in vitro activity and the presence of a haem (heme) cofactor of the enzyme. Our findings support the screening for insect resistant maize variants and the construction of genetically optimized maize plants.

Preprocessing significantly improves the peptide/protein identification sensitivity of high-resolution isobarically labeled tandem mass spectrometry data

Isobaric labeling techniques coupled with high-resolution mass spectrometry have been widely employed in proteomic workflows requiring relative quantification. For each high-resolution tandem mass spectrum (MS/MS), isobaric labeling techniques can be used not only to quantify the peptide from different samples by reporter ions, but also to identify the peptide it is derived from. Because the ions related to isobaric labeling may act as noise in database searching, the MS/MS spectrum should be preprocessed before peptide or protein identification. In this article, we demonstrate that there are a lot of high-frequency, high-abundance isobaric related ions in the MS/MS spectrum, and removing isobaric related ions combined with deisotoping and deconvolution in MS/MS preprocessing procedures significantly improves the peptide/protein identification sensitivity. The user-friendly software package TurboRaw2MGF (v2.0) has been implemented for converting raw TIC data files to mascot generic format files and can be downloaded for free from https://github.com/shengqh/RCPA.Tools/releases as part of the software suite ProteomicsTools. The data have been deposited to the ProteomeXchange with identifier PXD000994.

An update on LNCipedia: a database for annotated human lncRNA sequences

The human genome is pervasively transcribed, producing thousands of non-coding RNA transcripts. The majority of these transcripts are long non-coding RNAs (lncRNAs) and novel lncRNA genes are being identified at rapid pace. To streamline these efforts, we created LNCipedia, an online repository of lncRNA transcripts and annotation. Here, we present LNCipedia 3.0 (http://www.lncipedia.org), the latest version of the publicly available human lncRNA database. Compared to the previous version of LNCipedia, the database grew over five times in size, gaining over 90,000 new lncRNA transcripts. Assessment of the protein-coding potential of LNCipedia entries is improved with state-of-the art methods that include large-scale reprocessing of publicly available proteomics data. As a result, a high-confidence set of lncRNA transcripts with low coding potential is defined and made available for download. In addition, a tool to assess lncRNA gene conservation between human, mouse and zebrafish has been implemented.

MS-GF+ makes progress towards a universal database search tool for proteomics

Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but the software tools to analyze tandem mass spectra are lagging behind. We present a database search tool MS-GF+ that is sensitive (it identifies more peptides than most other database search tools) and universal (it works well for diverse types of spectra, different configurations of MS instruments and different experimental protocols). We benchmark MS-GF+ using diverse spectral datasets: (i) spectra of varying fragmentation methods; (ii) spectra of multiple enzyme digests; (iii) spectra of phosphorylated peptides; (iv) spectra of peptides with unusual fragmentation propensities produced by a novel alpha-lytic protease. For all these datasets, MS-GF+ significantly increases the number of identified peptides compared to commonly used methods for peptide identifications. We emphasize that while MS-GF+ is not specifically designed for any particular experimental set-up, it improves upon the performance of tools specifically designed for these applications (e.g., specialized tools for phosphoproteomics).

Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

Exploring the human plasma proteome for humoral mediators of remote ischemic preconditioning--a word of caution

Despite major advances in early revascularization techniques, cardiovascular diseases are still the leading cause of death worldwide, and myocardial infarctions contribute heavily to this. Over the past decades, it has become apparent that reperfusion of blood to a previously ischemic area of the heart causes damage in and of itself, and that this ischemia reperfusion induced injury can be reduced by up to 50% by mechanical manipulation of the blood flow to the heart. The recent discovery of remote ischemic preconditioning (RIPC) provides a non-invasive approach of inducing this cardioprotection at a distance. Finding its endogenous mediators and their operative mode is an important step toward increasing the ischemic tolerance. The release of humoral factor(s) upon RIPC was recently demonstrated and several candidate proteins were published as possible mediators of the cardioprotection. Before clinical applicability, these potential biomarkers and their efficiency must be validated, a task made challenging by the large heterogeneity in reported data and results. Here, in an attempt to reproduce and provide more experimental data on these mediators, we conducted an unbiased in-depth analysis of the human plasma proteome before and after RIPC. From the 68 protein markers reported in the literature, only 28 could be mapped to manually reviewed (Swiss-Prot) protein sequences. 23 of them were monitored in our untargeted experiment. However, their significant regulation could not be reproducibly estimated. In fact, among the 394 plasma proteins we accurately quantified, no significant regulation could be confidently and reproducibly assessed. This indicates that it is difficult to both monitor and reproduce published data from experiments exploring for RIPC induced plasma proteomic regulations, and suggests that further work should be directed towards small humoral factors. To simplify this task, we made our proteomic dataset available via ProteomeXchange, where scientists can mine for novel potential targets.

A proteogenomics approach integrating proteomics and ribosome profiling increases the efficiency of protein identification and enables the discovery of alternative translation start sites

Next-generation transcriptome sequencing is increasingly integrated with MS to enhance MS-based protein and peptide identification. Recently, a breakthrough in transcriptome analysis was achieved with the development of ribosome profiling (ribo-seq). This technology is based on the deep sequencing of ribosome-protected mRNA fragments, thereby enabling the direct observation of in vivo protein synthesis at the transcript level. In order to explore the impact of a ribo-seq-derived protein sequence search space on MS/MS spectrum identification, we performed a comprehensive proteome study on a human cancer cell line, using both shotgun and N-terminal proteomics, next to ribosome profiling, which was used to delineate (alternative) translational reading frames. By including protein-level evidence of sample-specific genetic variation and alternative translation, this strategy improved the identification score of 69 proteins and identified 22 new proteins in the shotgun experiment. Furthermore, we discovered 18 new alternative translation start sites in the N-terminal proteomics data and observed a correlation between the quantitative measures of ribo-seq and shotgun proteomics with a Pearson correlation coefficient ranging from 0.483 to 0.664. Overall, this study demonstrated the benefits of ribosome profiling for MS-based protein and peptide identification and we believe this approach could develop into a common practice for next-generation proteomics.

Proteomic analysis of formalin-fixed paraffin-embedded glomeruli suggests depletion of glomerular filtration barrier proteins in two-kidney, one-clip hypertensive rats

Background

It is well known that hypertension may cause glomerular damage, but the molecular mechanisms involved are still incompletely understood.

Methods

In the present study, we used formalin-fixed paraffin-embedded (FFPE) tissue to investigate changes in the glomerular proteome in the non-clipped kidney of two-kidney one-clip (2K1C) hypertensive rats, with special emphasis on the glomerular filtration barrier. 2K1C hypertension was induced in 6-week-old Wistar Hannover rats (n = 6) that were sacrificed 23 weeks later and compared with age-matched sham-operated controls (n = 6). Tissue was stored in FFPE tissue blocks and later prepared on tissue slides for laser microdissection. Glomeruli without severe morphological damage were isolated, and the proteomes were analysed using liquid chromatography–tandem mass spectrometry.

Results

2K1C glomeruli showed reduced abundance of proteins important for slit diaphragm complex, such as nephrin, podocin and neph1. The podocyte foot process had a pattern of reduced abundance of transmembrane proteins but unchanged abundances of the podocyte cytoskeletal proteins synaptopodin and α-actinin-4. Lower abundance of important glomerular basement membrane proteins was seen. Possible glomerular markers of damage with increased abundance in 2K1C were transgelin, desmin and acyl-coenzyme A thioesterase 1.

Conclusions

Microdissection and tandem mass spectrometry could be used to investigate the proteome of isolated glomeruli from FFPE tissue. Glomerular filtration barrier proteins had reduced abundance in the non-clipped kidney of 2K1C hypertensive rats.

In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR)

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.

Effects of blood contamination and the rostro-caudal gradient on the human cerebrospinal fluid proteome

Over the last years there has been an increased focus on the importance of knowing the effect of pre-analytical influence on the proteomes under study, particularly in the field of biomarker discovery. We present three proteomics studies examining the effect of blood contamination and the rostro-caudal gradient (RCG) on the cerebrospinal fluid (CSF) proteome, in addition to plasma/CSF protein ratios. The studies showed that the central nervous system (CNS) derived proteins appeared to be unaffected by the RCG, while the plasma-derived proteins showed an increase in concentration towards the lumbar area. This implies that the concentration of the plasma-derived proteins in CSF will vary depending on the volume of CSF that is collected. In the CSF samples spiked with blood, 262 of 814 quantified proteins showed an abundance increase of more than 1.5 fold, while 403 proteins had a fold change of less than 1.2 and appeared to be unaffected by blood contamination. Proteins with a high plasma/CSF ratio appeared to give the largest effect on the CSF proteome upon blood contamination. The results give important background information on how factors like blood contamination, RCG and blood-CNS-barrier influences the CSF proteome. This information is particularly important in the field of biomarker discovery, but also for routine clinical measurements. The data from the blood contamination and RCG discovery studies have been deposited to the ProteomeXchange with identifier PXD000401.

DeNovoGUI: an open source graphical user interface for de novo sequencing of tandem mass spectra

De novo sequencing is a popular technique in proteomics for identifying peptides from tandem mass spectra without having to rely on a protein sequence database. Despite the strong potential of de novo sequencing algorithms, their adoption threshold remains quite high. We here present a user-friendly and lightweight graphical user interface called DeNovoGUI for running parallelized versions of the freely available de novo sequencing software PepNovo+, greatly simplifying the use of de novo sequencing in proteomics. Our platform-independent software is freely available under the permissible Apache2 open source license. Source code, binaries, and additional documentation are available at http://denovogui.googlecode.com.

MASSyPup--an 'out of the box' solution for the analysis of mass spectrometry data

Mass spectrometry has evolved to a key technology in the areas of metabolomics and proteomics. Centralized facilities generate vast amount of data, which frequently need to be processed off-site. Therefore, the distribution of data and software, as well as the training of personnel in the analysis of mass spectrometry data, becomes increasingly important. Thus, we created a comprehensive collection of mass spectrometry software which can be run directly from different media such as DVD or USB without local installation. MASSyPup is based on a Linux Live distribution and was complemented with programs for conversion, visualization and analysis of mass spectrometry (MS) data. A special emphasis was put on protein analysis and proteomics, encompassing the measurement of complete proteins, the identification of proteins based on Peptide Mass Fingerprints (PMF) or LC-MS/MS data, and de novo sequencing. Another focus was directed to the study of metabolites and metabolomics, covering the detection, identification and quantification of compounds, as well as subsequent statistical analyses. Additionally, we added software for Mass Spectrometry Imaging (MSI), including hardware support for self-made MSI devices. MASSyPup represents a 'ready to work' system for teaching or MS data analysis, but also represents an ideal platform for the distribution of MS data and the development of related software. The current Live DVD version can be downloaded free of charge from http://www.bioprocess.org/massypup.

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.

Tyr728 in the kinase domain of the murine kinase suppressor of RAS 1 regulates binding and activation of the mitogen-activated protein kinase kinase

In metazoans, the highly conserved MAPK signaling pathway regulates cell fate decision. Aberrant activation of this pathway has been implicated in multiple human cancers and some developmental disorders. KSR1 functions as an essential scaffold that binds the individual components of the cascade and coordinates their assembly into multiprotein signaling platforms. The mechanism of KSR1 regulation is highly complex and not completely understood. In this study, we identified Tyr(728) as a novel regulatory phosphorylation site in KSR1. We show that Tyr(728) is phosphorylated by LCK, uncovering an additional and unexpected link between Src kinases and MAPK signaling. To understand how phosphorylation of Tyr(728) may regulate the role of KSR1 in signal transduction, we integrated structural modeling and biochemical studies. We demonstrate that Tyr(728) is involved in maintaining the conformation of the KSR1 kinase domain required for binding to MEK. It also affects phosphorylation and activation of MEK by RAF kinases and consequently influences cell proliferation. Moreover, our studies suggest that phosphorylation of Tyr(728) may affect the intrinsic kinase activity of KSR1. Together, we propose that phosphorylation of Tyr(728) may regulate the transition between the scaffolding and the catalytic function of KSR1 serving as a control point used to fine-tune cellular responses.

Paleoproteomic study of the Iceman's brain tissue

The Tyrolean Iceman, a Copper-age ice mummy, is one of the best-studied human individuals. While the genome of the Iceman has largely been decoded, tissue-specific proteomes have not yet been investigated. We studied the proteome of two distinct brain samples using gel-based and liquid chromatography-mass spectrometry-based proteomics technologies together with a multiple-databases and -search algorithms-driven data-analysis approach. Thereby, we identified a total of 502 different proteins. Of these, 41 proteins are known to be highly abundant in brain tissue and 9 are even specifically expressed in the brain. Furthermore, we found 10 proteins related to blood and coagulation. An enrichment analysis revealed a significant accumulation of proteins related to stress response and wound healing. Together with atomic force microscope scans, indicating clustered blood cells, our data reopens former discussions about a possible injury of the Iceman's head near the site where the tissue samples have been extracted.

Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities

Metaproteomics of microbial communities promises to add functional information to the blueprint of genes derived from metagenomics. Right from its beginning, the achievements and developments in metaproteomics were closely interlinked with metagenomics. In addition, the evaluation, visualization, and interpretation of metaproteome data demanded for the developments in bioinformatics. This review will give an overview about recent strategies to use genomic data either from public databases or organismal specific genomes/metagenomes to increase the number of identified proteins obtained by mass spectrometric measurements. We will review different published metaproteogenomic approaches in respect to the used MS pipeline and to the used protein identification workflow. Furthermore, different approaches of data visualization and strategies for phylogenetic interpretation of metaproteome data are discussed as well as approaches for functional mapping of the results to the investigated biological systems. This information will in the end allow a comprehensive analysis of interactions and interdependencies within microbial communities.

Current methods for global proteome identification

In a time frame of a few decades, protein identification went from laborious single protein identification to automated identification of entire proteomes. This shift was enabled by the emergence of peptide-centric, gel-free analyses, in particular the so-called shotgun approaches, which not only rely on extensive experiments, but also on cutting-edge data processing methods. The present review therefore provides an overview of a shotgun proteomics identification workflow, listing the state-of-the-art methods involved and software that implement these. The authors focus on freely available tools where possible. Finally, data analysis in the context of emerging across-omics studies will also be discussed briefly, where proteomics goes beyond merely delivering a list of protein accession numbers.

Increased WD-repeat containing protein 1 in interstitial fluid from ovarian carcinomas shown by comparative proteomic analysis of malignant and healthy gynecological tissue

We aimed to identify differentially expressed proteins in interstitial fluid from ovarian cancer employing multiple fractioning and high resolution mass spectrometry-based proteomic analysis, and asked whether specific proteins that may serve as biomarker candidates or therapeutic targets could be identified. High throughput proteomics was conducted on immunodepleted and fractioned interstitial fluid from pooled samples of ovarian carcinomas, using endometrial carcinomas and healthy ovarian tissue as controls. Differential analysis revealed the up-regulation of extracellular proteasomes in tumor interstitial fluid compared to the healthy control. Moreover, a number of differentially expressed proteins in interstitial fluid from ovarian carcinomas compared with control tissues were identified. Detection of proteasome 20S related proteins in TIF compared to IF from healthy tissue indicates that the 20S proteasome can have a role in the tumor microenvironment. Six selected proteins, CEACAM5, FREM2, MUC5AC, TFF3, PYCARD and WDR1, were independently validated in individual tumor lysates from ovarian carcinomas by multiple reaction monitoring initiated detection and sequence analysis, Western blot and/or selected reaction monitoring. Quantification of specific proteins revealed substantial heterogeneity between individual samples. Nevertheless, WD repeat-containing protein 1 was confirmed as being significantly overexpressed in interstitial fluid from ovarian carcinomas compared to healthy ovarian tissue by Orbitrap analysis of individual native interstitial fluid from ovarian and endometrial carcinomas and healthy ovarian tissue. We suggest that this protein should be explored as a therapeutic target in ovarian carcinomas. This article is part of a Special Issue entitled: An Updated Secretome.

Identification of new protein coding sequences and signal peptidase cleavage sites of Helicobacter pylori strain 26695 by proteogenomics

Correct annotation of protein coding genes is the basis of conventional data analysis in proteomic studies. Nevertheless, most protein sequence databases almost exclusively rely on gene finding software and inevitably also miss protein annotations or possess errors. Proteogenomics tries to overcome these issues by matching MS data directly against a genome sequence database. Here we report an in-depth proteogenomics study of Helicobacter pylori strain 26695. MS data was searched against a combined database of the NCBI annotations and a six-frame translation of the genome. Database searches with Mascot and X! Tandem revealed 1115 proteins identified by at least two peptides with a peptide false discovery rate below 1%. This represents 71% of the predicted proteome. So far this is the most extensive proteome study of Helicobacter pylori. Our proteogenomic approach unambiguously identified four previously missed annotations and furthermore allowed us to correct sequences of six annotated proteins. Since secreted proteins are often involved in pathogenic processes we further investigated signal peptidase cleavage sites. By applying a database search that accommodates the identification of semi-specific cleaved peptides, 63 previously unknown signal peptides were detected. The motif LXA showed to be the predominant recognition sequence for signal peptidases.

BIOLOGICAL SIGNIFICANCE

The results of MS-based proteomic studies highly rely on correct annotation of protein coding genes which is the basis of conventional data analysis. However, the annotation of protein coding sequences in genomic data is usually based on gene finding software. These tools are limited in their prediction accuracy such as the problematic determination of exact gene boundaries. Thus, protein databases own partly erroneous or incomplete sequences. Additionally, some protein sequences might also be missing in the databases. Proteogenomics, a combination of proteomic and genomic data analyses, is well suited to detect previously not annotated proteins and to correct erroneous sequences. For this purpose, the existing database of the investigated species is typically supplemented with a six-frame translation of the genome. Here, we studied the proteome of the major human pathogen Helicobacter pylori that is responsible for many gastric diseases such as duodenal ulcers and gastric cancer. Our in-depth proteomic study highly reliably identified 1115 proteins (FDR<0.01%) by at least two peptides (FDR<1%) which represent 71% of the predicted proteome deposited at NCBI. The proteogenomic data analysis of our data set resulted in the unambiguous identification of four previously missed annotations, the correction of six annotated proteins as well as the detection of 63 previously unknown signal peptides. We have annotated proteins of particular biological interest like the ferrous iron transport protein A, the coiled-coil-rich protein HP0058 and the lipopolysaccharide biosynthesis protein HP0619. For instance, the protein HP0619 could be a drug target for the inhibition of the LPS synthesis pathway. Furthermore it has been proven that the motif "LXA" is the predominant recognition sequence for the signal peptidase I of H. pylori. Signal peptidases are essential enzymes for the viability of bacterial cells and are involved in pathogenesis. Therefore signal peptidases could be novel targets for antibiotics. The inclusion of the corrected and new annotated proteins as well as the information of signal peptide cleavage sites will help in the study of biological pathways involved in pathogenesis or drug response of H. pylori.

Deep proteome coverage based on ribosome profiling aids mass spectrometry-based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events

An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing and highly sensitive mass spectrometry (MS) instrumentation. Recently, a strategy, termed ribosome profiling (or RIBO-seq), based on deep sequencing of ribosome-protected mRNA fragments, indirectly monitoring protein synthesis, has been described. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both Swiss-Prot- and RIBO-seq-derived translation products, applicable for MS/MS spectrum identification. To record the impact of using the constructed deep proteome database, we performed two alternative MS-based proteomic strategies as follows: (i) a regular shotgun proteomic and (ii) an N-terminal combined fractional diagonal chromatography (COFRADIC) approach. Although the former technique gives an overall assessment on the protein and peptide level, the latter technique, specifically enabling the isolation of N-terminal peptides, is very appropriate in validating the RIBO-seq-derived (alternative) translation initiation site profile. We demonstrate that this proteogenomic approach increases the overall protein identification rate 2.5% (e.g. new protein products, new protein splice variants, single nucleotide polymorphism variant proteins, and N-terminally extended forms of known proteins) as compared with only searching UniProtKB-SwissProt. Furthermore, using this custom database, identification of N-terminal COFRADIC data resulted in detection of 16 alternative start sites giving rise to N-terminally extended protein variants besides the identification of four translated upstream ORFs. Notably, the characterization of these new translation products revealed the use of multiple near-cognate (non-AUG) start codons. As deep sequencing techniques are becoming more standard, less expensive, and widespread, we anticipate that mRNA sequencing and especially custom-tailored RIBO-seq will become indispensable in the MS-based protein or peptide identification process. The underlying mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000124.

D-score: a search engine independent MD-score

While peptides carrying PTMs are routinely identified in gel-free MS, the localization of the PTMs onto the peptide sequences remains challenging. Search engine scores of secondary peptide matches have been used in different approaches in order to infer the quality of site inference, by penalizing the localization whenever the search engine similarly scored two candidate peptides with different site assignments. In the present work, we show how the estimation of posterior error probabilities for peptide candidates allows the estimation of a PTM score called the D-score, for multiple search engine studies. We demonstrate the applicability of this score to three popular search engines: Mascot, OMSSA, and X!Tandem, and evaluate its performance using an already published high resolution data set of synthetic phosphopeptides. For those peptides with phosphorylation site inference uncertainty, the number of spectrum matches with correctly localized phosphorylation increased by up to 25.7% when compared to using Mascot alone, although the actual increase depended on the fragmentation method used. Since this method relies only on search engine scores, it can be readily applied to the scoring of the localization of virtually any modification at no additional experimental or in silico cost.

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.

Improved proteomic analysis pipeline for LC-ETD-MS/MS using charge enhancing methods

Electron transfer dissociation (ETD) is a useful and complementary activation method for peptide fragmentation in mass spectrometry. However, ETD spectra typically receive a relatively low score in the identifications of 2+ ions. To overcome this challenge, we, for the first time, systematically interrogated the benefits of combining ion charge enhancing methods (dimethylation, guanidination, m-nitrobenzyl alcohol (m-NBA) or Lys-C digestion) and differential search algorithms (Mascot, Sequest, OMSSA, pFind and X!Tandem). A simple sample (BSA) and a complex sample (AMJ2 cell lysate) were selected in benchmark tests. Clearly distinct outcomes were observed through different experimental protocol. In the analysis of AMJ2 cell lines, X!Tandem and pFind revealed 92.65% of identified spectra; m-NBA adduction led to a 5-10% increase in average charge state and the most significant increase in the number of successful identifications, and Lys-C treatment generated peptides carrying mostly triple charges. Based on the complementary identification results, we suggest that a combination of m-NBA and Lys-C strategies accompanied by X!Tandem and pFind can greatly improve ETD identification.

LNCipedia: a database for annotated human lncRNA transcript sequences and structures

Here, we present LNCipedia (http://www.lncipedia.org), a novel database for human long non-coding RNA (lncRNA) transcripts and genes. LncRNAs constitute a large and diverse class of non-coding RNA genes. Although several lncRNAs have been functionally annotated, the majority remains to be characterized. Different high-throughput methods to identify new lncRNAs (including RNA sequencing and annotation of chromatin-state maps) have been applied in various studies resulting in multiple unrelated lncRNA data sets. LNCipedia offers 21 488 annotated human lncRNA transcripts obtained from different sources. In addition to basic transcript information and gene structure, several statistics are determined for each entry in the database, such as secondary structure information, protein coding potential and microRNA binding sites. Our analyses suggest that, much like microRNAs, many lncRNAs have a significant secondary structure, in-line with their presumed association with proteins or protein complexes. Available literature on specific lncRNAs is linked, and users or authors can submit articles through a web interface. Protein coding potential is assessed by two different prediction algorithms: Coding Potential Calculator and HMMER. In addition, a novel strategy has been integrated for detecting potentially coding lncRNAs by automatically re-analysing the large body of publicly available mass spectrometry data in the PRIDE database. LNCipedia is publicly available and allows users to query and download lncRNA sequences and structures based on different search criteria. The database may serve as a resource to initiate small- and large-scale lncRNA studies. As an example, the LNCipedia content was used to develop a custom microarray for expression profiling of all available lncRNAs.

Cross-linking/mass spectrometry as a new field and the proteomics information mountain of tomorrow

The European Proteomics Association (EuPA) 2012 Scientific Congress 'New Horizons and Applications for Proteomics', hosted by the British Society for Proteome Research (BSPR) Glasgow, Scotland, UK, 12 July 2012 Cross-linking/mass spectrometry ended decades of method developments and entered the era of applications at this year's European Proteomics Association meeting. The train has started moving, with successful applications of this tool by multiple pioneering laboratories addressing biological and structural problems. Proteomics, on the other side, sees ever increasing data volumes, leading to questions as to how to store the data mountain publically, use it and convert it into testable hypotheses. The European Proteomics Association meeting has been complementary to the American Society for Mass Spectrometry meeting in many ways, also thanks to its more manageable size and the vision of the organizers in inviting some of Europe's best emerging minds.

Intermembrane space proteome of yeast mitochondria

The intermembrane space (IMS) represents the smallest subcompartment of mitochondria. Nevertheless, it plays important roles in the transport and modification of proteins, lipids, and metal ions and in the regulation and assembly of the respiratory chain complexes. Moreover, it is involved in many redox processes and coordinates key steps in programmed cell death. A comprehensive profiling of IMS proteins has not been performed so far. We have established a method that uses the proapoptotic protein Bax to release IMS proteins from isolated mitochondria, and we profiled the protein composition of this compartment. Using stable isotope-labeled mitochondria from Saccharomyces cerevisiae, we were able to measure specific Bax-dependent protein release and distinguish between quantitatively released IMS proteins and the background efflux of matrix proteins. From the known 31 soluble IMS proteins, 29 proteins were reproducibly identified, corresponding to a coverage of >90%. In addition, we found 20 novel intermembrane space proteins, out of which 10 had not been localized to mitochondria before. Many of these novel IMS proteins have unknown functions or have been reported to play a role in redox regulation. We confirmed IMS localization for 15 proteins using in organello import, protease accessibility upon osmotic swelling, and Bax-release assays. Moreover, we identified two novel mitochondrial proteins, Ymr244c-a (Coa6) and Ybl107c (Mic23), as substrates of the MIA import pathway that have unusual cysteine motifs and found the protein phosphatase Ptc5 to be a novel substrate of the inner membrane protease (IMP). For Coa6 we discovered a role as a novel assembly factor of the cytochrome c oxidase complex. We present here the first and comprehensive proteome of IMS proteins of yeast mitochondria with 51 proteins in total. The IMS proteome will serve as a valuable source for further studies on the role of the IMS in cell life and death.

The effect of peptide identification search algorithms on MS2-based label-free protein quantification

Several approaches exist for the quantification of proteins in complex samples processed by liquid chromatography-mass spectrometry followed by fragmentation analysis (MS2). One of these approaches is label-free MS2-based quantification, which takes advantage of the information computed from MS2 spectrum observations to estimate the abundance of a protein in a sample. As a first step in this approach, fragmentation spectra are typically matched to the peptides that generated them by a search algorithm. Because different search algorithms identify overlapping but non-identical sets of peptides, here we investigate whether these differences in peptide identification have an impact on the quantification of the proteins in the sample. We therefore evaluated the effect of using different search algorithms by examining the reproducibility of protein quantification in technical repeat measurements of the same sample. From our results, it is clear that a search engine effect does exist for MS2-based label-free protein quantification methods. As a general conclusion, it is recommended to address the overall possibility of search engine-induced bias in the protein quantification results of label-free MS2-based methods by performing the analysis with two or more distinct search engines.

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.

The good, the bad, the ugly: validating the mass spectrometric analysis of modified peptides

Mass spectrometric characterization of protein modifications is usually based on single peptides. With the advent of large-scale PTM-focussed MS studies, vast amounts of data are generated continuously, providing biologists extremely valuable and virtually never-ending sources for targeted functional research. However, even more than for proteomics in general, appropriate strategies for quality control of the different steps of the analytical strategy are imperative to prevent functional researchers from doing Sisyphos work on false-positive and unconfident PTM assignments. Here, we describe strategies to address the important issue of quality control for PTM analysis on various levels of the analytical pipeline: sample preparation/processing, analysis/identification and finally data interpretation, for qualitative as well as quantitative studies.