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

Large-scale differential proteome analysis in Plasmodium falciparum under drug treatment

Proteome studies contribute markedly to our understanding of parasite biology, host-parasite interactions, and mechanisms of drug action. For most antimalarial drugs neither mode of action nor mechanisms of resistance development are fully elucidated although this would be important prerequisites for successfully developing urgently required novel antimalarials. Here, we establish a large-scale quantitative proteomic approach to examine protein expression changes in trophozoite stages of the malarial parasite Plasmodium falciparum following chloroquine and artemisinin treatment. For this purpose SIL (stable isotope labeling) using (14)N-isoleucine and (13)C(6),(15)N(1)-isoleucine was optimized to obtain 99% atomic percent enrichment. Proteome fractionation with anion exchange chromatography was used to reduce sample complexity and increase quantitative coverage of protein expression. Tryptic peptides of subfractions were subjected to SCX/RP separation, measured by LC-MS/MS and quantified using the novel software tool Census. In drug treated parasites, we identified a total number of 1,253 proteins, thus increasing the overall number of proteins identified in the trophozoite stage by 30%. A relative quantification was obtained for more than 800 proteins. Under artemisinin and chloroquine treatment 41 and 38 proteins respectively were upregulated (>1.5) whereas 14 and 8 proteins were down-regulated (<0.5). Apart from specifically regulated proteins we also identified sets of proteins which were regulated as a general response to drug treatment. The proteomic data was confirmed by Western blotting. The methodology described here allows for the efficient large-scale differential proteome analysis of P. falciparum to study the response to drug treatment or environmental changes. Only 100 microg of protein is required for the analysis suggesting that the method can also be transferred to other apicomplexan parasites.

Nitrosothiol reactivity profiling identifies S-nitrosylated proteins with unexpected stability

Nitric oxide (NO) regulates protein function by S-nitrosylation of cysteine to form nitrosothiols. Nitrosothiols are highly susceptible to nonenzymatic degradation by cytosolic reducing agents. Here we show that although most protein nitrosothiols are rapidly degraded by cytosolic reductants, a small subset form unusually stable S-nitrosylated proteins. Our findings suggest that stable S-nitrosylation reflects a protein conformation change that shields the nitrosothiol. To identify stable protein nitrosothiols, we developed a proteomic method for profiling S-nitrosylation. We examined the stability of over 100 S-nitrosylated proteins, and identified 10 stable nitrosothiols. These proteins remained S-nitrosylated in cells after NO synthesis was inhibited, unlike most S-nitrosylated proteins. Taken together, our data identify a class of NO targets that form stable nitrosothiols in the cell and are likely to mediate the persistent cellular effects of NO.

Quantitative phosphoproteomics--an emerging key technology in signal-transduction research

Protein phosphorylation is the most important type of reversible post-translational modification involved in the regulation of cellular signal-transduction processes. In addition to controlling normal cellular physiology on the molecular level, perturbations of phosphorylation-based signaling networks and cascades have been implicated in the onset and progression of various human diseases. Recent advances in mass spectrometry-based proteomics helped to overcome many of the previous limitations in protein phosphorylation analysis. Improved isotope labeling and phosphopeptide enrichment strategies in conjunction with more powerful mass spectrometers and advances in data analysis have been integrated in highly efficient phosphoproteomics workflows, which are capable of monitoring up to several thousands of site-specific phosphorylation events within one large-scale analysis. Combined with ongoing efforts to define kinase-substrate relationships in intact cells, these major achievements have considerable potential to assess phosphorylation-based signaling networks on a system-wide scale. Here, we provide an overview of these exciting developments and their potential to transform signal-transduction research into a technology-driven, high-throughput science.

Identification and characterization of neuronal mitogen-activated protein kinase substrates using a specific phosphomotif antibody

Mitogen-activated protein kinases (MAPKs) control neuronal synaptic function; however, little is known about the synaptic substrates regulated by MAPKs. A phosphopeptide library incorporating the MAPK consensus motif (PX(pS/pT)P where pS is phosphoserine and pT is phosphothreonine) was used to raise a phosphospecific antibody that detected MAPK-mediated phosphorylation. The antibody (termed “5557”) recognized a variety of phosphoproteins in the brain, many of which were enriched in postsynaptic density fractions. The immunoblot pattern changed rapidly in response to altered synaptic activity and with the inhibition of specific MAPKs and protein phosphatases. By immunoaffinity purification with 5557 antibody followed by mass spectrometry, we identified 449 putative MAPK substrates of which many appeared dynamically regulated in neuron cultures. Several of the novel candidate MAPK substrates were validated by in vitro phosphorylation assays. Additionally 82 specific phosphorylation sites were identified in 34 proteins, including Ser-447 in δ-catenin, a component of the cadherin adhesion complex. We further raised another phosphospecific antibody to confirm that δ-catenin Ser-447 is modified in neurons by the MAPK JNK in a synaptic activity-dependent manner. Ser-447 phosphorylation by JNK appears to be correlated with δ-catenin degradation, and a δ-catenin mutant defective in Ser-447 phosphorylation showed enhanced ability to promote dendrite branching in cultured neurons. Thus, phosphomotif-based affinity purification is a powerful approach to identify novel substrates of MAPKs in vivo and to reveal functionally significant phosphorylation events.

Quantitative mass spectrometry identifies drug targets in cancer stem cell-containing side population

A multifaceted approach is presented as a general strategy to identify new drug targets in a breast cancer stem cell-containing side population. The approach we have utilized combines side population cell sorting and stable isotope labeling by amino acids in cell culture with mass spectrometry to compare and identify proteins with differential expression profiles between side population cells, know to be enriched in cancer stem cells, and nonside population cells, which are depleted in cancer stem cells, for two breast cancer cell lines, MCF7 and MDA-MB231. Almost 900 proteins were quantified, and several important proteins in cell cycle control and differentiation were found to be upregulated in the cancer stem cell-containing side population. Most interestingly, a splice isoform of pyruvate kinase M2 as well as peroxiredoxin 6 were found to be downregulated. The differential levels of three of these proteins, thymosin beta4 (TB4), proliferation-associated protein 2G4, and SIAH-interacting protein, were validated using Western blot. Furthermore, functional validation provided clear evidence that elevated TB4 expression contributes to drug resistance in the stem cell population. Small interfering RNA silencing of TB4 led to a loss of chemoresistance in two separate breast cancer populations. These proteins likely contribute to resistance in the cancer stem cell-containing side population, and their altered expression in a tumor causes clinical resistance to chemotherapy. The ability to perform quantitative mass spectrometry has enabled the identification of a series of proteins that could serve as future therapeutic targets.

Global liver proteomics of rats exposed for 5 days to phenobarbital identifies changes associated with cancer and with CYP metabolism

A global proteomics approach was applied to model the hepatic response elicited by the toxicologically well-characterized xenobiotic phenobarbital (PB), a prototypical inducer of hepatic xenobiotic metabolizing enzymes and a well-known nongenotoxic liver carcinogen in rats. Differential detergent fractionation two-dimensional liquid chromatography electrospray ionization tandem mass spectrometry and systems biology modeling were used to identify alterations in toxicologically relevant hepatic molecular functions and biological processes in the livers of rats following a 5-day exposure to PB at 80 mg/kg/day or a vehicle control. Of the 3342 proteins identified, expression of 121 (3.6% of the total proteins) was significantly increased and 127 (3.8%) significantly decreased in the PB group compared to controls. The greatest increase was seen for cytochrome P450 (CYP) 2B2 (167-fold). All proteins with statistically significant differences from control were then analyzed using both Gene Ontology (GO) and Ingenuity Pathways Analysis (IPA, 5.0 IPA-Tox) for cellular location, function, network connectivity, and possible disease processes, especially as they relate to CYP-mediated metabolism and nongenotoxic carcinogenesis mechanisms. The GO results suggested that PB's mechanism of nongenotoxic carcinogenesis involves both increased xenobiotic metabolism, especially induction of the 2B subfamily of CYP enzymes, and increased cell cycle activity. Apoptosis, however, also increased, perhaps, as an attempt to counter the rising cancer threat. Of the IPA-mapped proteins, 41 have functions which are procarcinogenic and 14 anticarcinogenic according to the hypothesized nongenotoxic mechanism of imbalance between apoptosis and cellular proliferation. Twenty-two additional IPA nodes can be classified as procarcinogenic by the competing theory of increased metabolism resulting in the formation of reactive oxygen species. Since the systems biology modeling corresponded well to PB effects previously elucidated via more traditional methods, the global proteomic approach is proposed as a new screening methodology that can be incorporated into future toxicological studies.

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

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

Characterization of strategies for obtaining confident identifications in bottom-up proteomics measurements using hybrid FTMS instruments

Hybrid FTMS instruments, such as the LTQ-FT and LTQ-Orbitrap, are capable of generating high duty cycle linear ion trap MS/MS data along with high resolution information without compromising the overall throughput of measurements. Combined with online LC separations, these instruments provide powerful capabilities for proteomics research. In the present work, we explore three alternative strategies for high throughput proteomics measurements using hybrid FTMS instruments. Our accurate mass and time tag (AMT tag) strategy enables identification of thousands of peptides in a single LC-FTMS analysis by comparing accurate molecular mass and LC elution time information from the analysis to a reference database. An alternative strategy considered here, termed accurate precursor mass filter (APMF), employs linear ion trap (low resolution) MS/MS identifications generated by an appropriate search engine, such as SEQUEST, refined with high resolution precursor ion data obtained from FTMS mass spectra. The APMF results can be additionally filtered using the LC elution time information from the AMT tag database, which constitutes a precursor mass and time filter (PMTF), the third approach implemented in this study. Both the APMF and the PMTF approaches are evaluated for coverage and confidence of peptide identifications and contrasted with the AMT tag strategy. The commonly used decoy database method and an alternative method based on mass accuracy histograms were used to reliably quantify identification confidence, revealing that both methods yielded similar results. Comparison of the AMT, APMF and PMTF approaches indicates that the AMT tag approach is preferential for studies desiring a highest achievable number of identified peptides. In contrast, the APMF approach does not require an AMT tag database and provides a moderate level of peptide coverage combined with acceptable confidence values of approximately 99%. The PMTF approach yielded a significantly better peptide identification confidence, >99.9%, that essentially excluded any false peptide identifications. Since AMT tag databases that exclude incorrect identifications are desirable, this study points to the value of a multipass APMF approach to generate AMT tag databases, which are then validated using the PMTF approach. The resulting compact, high quality databases can then be used for subsequent high-throughput, high peptide coverage AMT tag studies.

Comparisons of mass spectrometry compatible surfactants for global analysis of the mammalian brain proteome

Methods for the global analysis of protein expression offer an approach to study the molecular basis of disease. Studies of protein expression in tissue, such as brain, are complicated by the need for efficient and unbiased digestion of proteins that permit identification of peptides by shotgun proteomic methods. In particular, identification and characterization of less abundant membrane proteins has been of great interest for studies of brain physiology, but often proteins of interest are of low abundance or exist in multiple isoforms. Parsing protein isoforms as a function of disease will be essential. In this study, we develop a digestion scheme using detergents compatible with mass spectrometry that improves membrane protein identification from brain tissue. We show the modified procedure yields close to 5,000 protein identifications from 1.8 mg of rat brain homogenate with an average of 25% protein sequence coverage. This procedure achieves a remarkable reduction in the amount of starting material required to observe a broad spectrum of membrane proteins. Among the proteins identified from a mammalian brain homogenate, 1897 (35%) proteins are annotated by Gene Ontology as membrane proteins, and 1225 (22.6%) proteins are predicted to contain at least one transmembrane domain. Membrane proteins identified included neurotransmitter receptors and ion channels implicated in important physiological functions and disease.

Bayesian nonparametric model for the validation of peptide identification in shotgun proteomics

Tandem mass spectrometry combined with database searching allows high throughput identification of peptides in shotgun proteomics. However, validating database search results, a problem with a lot of solutions proposed, is still advancing in some aspects, such as the sensitivity, specificity, and generalizability of the validation algorithms. Here a Bayesian nonparametric (BNP) model for the validation of database search results was developed that incorporates several popular techniques in statistical learning, including the compression of feature space with a linear discriminant function, the flexible nonparametric probability density function estimation for the variable probability structure in complex problem, and the Bayesian method to calculate the posterior probability. Importantly the BNP model is compatible with the popular target-decoy database search strategy naturally. We tested the BNP model on standard proteins and real, complex sample data sets from multiple MS platforms and compared it with Peptide-Prophet, the cutoff-based method, and a simple nonparametric method (proposed by us previously). The performance of the BNP model was shown to be superior for all data sets searched on sensitivity and generalizability. Some high quality matches that had been filtered out by other methods were detected and assigned with high probability by the BNP model. Thus, the BNP model could be able to validate the database search results effectively and extract more information from MS/MS data.

Brain-specific proteins decline in the cerebrospinal fluid of humans with Huntington disease

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

Evaluation of the utility of neutral-loss-dependent MS3 strategies in large-scale phosphorylation analysis

Phosphopeptide identification and site determination are major challenges in biomedical MS. Both are affected by frequent and often overwhelming losses of phosphoric acid in ion trap CID fragmentation spectra. These losses are thought to translate into reduced intensities of sequence informative ions and a general decline in the quality of MS/MS spectra. To address this issue, several methods have been proposed, which rely on extended fragmentation schemes including collecting MS3 scans from neutral loss-containing ions and multi-stage activation to further fragment these same ions. Here, we have evaluated the utility of these methods in the context of a large-scale phosphopeptide analysis strategy with current instrumentation capable of accurate precursor mass determination. Remarkably, we found that MS3-based schemes did not increase the overall number of confidently identified peptides and had only limited value in site localization. We conclude that the collection of MS3 or pseudo-MS3 scans in large-scale proteomics studies is not worthwhile when high-mass accuracy instrumentation is used.

Quantitative analysis of brain nuclear phosphoproteins identifies developmentally regulated phosphorylation events

Protein phosphorylation is a globally adopted and tightly controlled post-translational modification, and represents one of the most important molecular switching mechanisms that govern the entire spectrum of biological processes. In the central nervous system, it has been demonstrated that phosphorylation of key proteins mediating chromatin remodeling and gene transcription plays an important role controlling brain development, synaptogenesis, learning and memory. Many studies have focused on large scale identification of phosphopeptides in brain tissue. These studies have identified phosphorylation site specific motifs useful for predicting protein kinase substrates. In this study, we applied a previously developed quantitative approach, stable isotope labeling of amino acids in mammals (SILAM), to quantify changes in the phosphorylation of nuclear proteins between a postnatal day one (p1) and a p45 rat brain cortex. Using a 15N labeled rat brain as an internal standard, we quantified 705 phosphopeptides in the p1 cortex and 1477 phosphopeptides in the p45 cortex, which translates to 380 and 585 phosphoproteins in p1 and p45 cortex, respectively. Bioinformatic analysis of the differentially modified phosphoproteins revealed that phosphorylation is upregulated on multiple components of chromatin remodeling complexes in the p1 cortex. Taken together, we demonstrated for the first time the usefulness of employing stable isotope labeled rat tissue for global quantitative phosphorylation analysis.

Analysis of environmental stress response on the proteome level

Thousands of man-made chemicals are annually released into the environment by agriculture, transport, industries, and other human activities. In general, chemical analysis of environmental samples used to assess the pollution status of a specific ecosystem is complicated by the complexity of the mixture, and in some cases by the very low toxicity thresholds of chemicals present. In that sense, a proteomics approach, capable of detecting subtle changes in the level and structure of individual proteins within the whole proteome in response to the altered surroundings, has obvious applications in the field of ecotoxicology. In addition to identifying new protein biomarkers, it can also help to provide an insight into underlying mechanisms of toxicity. Despite being a comparatively new field with a number of caveats, proteomics applications have spread from microorganisms and plants to invertebrates and vertebrates, gradually becoming an established technology used in environmental research. This review article highlights recent advances in the field of environmental proteomics, mainly focusing on experimental approaches with a potential to understand toxic modes of action and to identify novel ecotoxicological biomarkers.

The impact of peptide abundance and dynamic range on stable-isotope-based quantitative proteomic analyses

Recently, mass spectrometry has been employed in many studies to provide unbiased, reproducible, and quantitative protein abundance information on a proteome-wide scale. However, how instruments' limited dynamic ranges impact the accuracy of such measurements has remained largely unexplored, especially in the context of complex mixtures. Here, we examined the distribution of peptide signal versus background noise (S/N) and its correlation with quantitative accuracy. With the use of metabolically labeled Jurkat cell lysate, over half of all confidently identified peptides had S/N ratios less than 10 when examined using both hybrid linear ion trap-Fourier transform ion cyclotron resonance and Orbitrap mass spectrometers. Quantification accuracy was also highly correlated with S/N. We developed a mass precision algorithm that significantly reduced measurement variance at low S/N beyond the use of highly accurate mass information alone and expanded it into a new software suite, Vista. We also evaluated the interplay between mass measurement accuracy and S/N; finding a balance between both parameters produced the greatest identification and quantification rates. Finally, we demonstrate that S/N can be a useful surrogate for relative abundance ratios when only a single species is detected.

An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.

Functional and stoichiometric analysis of subunit e in bovine heart mitochondrial F(0)F(1)ATP synthase

The role of the integral inner membrane subunit e in self-association of F(0)F(1)ATP synthase from bovine heart mitochondria was analyzed by in situ limited proteolysis, blue native PAGE/iterative SDS-PAGE, and LC-MS/MS. Selective degradation of subunit e, without disrupting membrane integrity or ATPase capacity, altered the oligomeric distribution of F(0)F(1)ATP synthase, by eliminating oligomers and reducing dimers in favor of monomers. The stoichiometry of subunit e was determined by a quantitative MS-based proteomics approach, using synthetic isotope-labelled reference peptides IAQL*EEVK, VYGVGSL*ALYEK, and ELAEAQEDTIL*K to quantify the b, gamma and e subunits, respectively. Accuracy of the method was demonstrated by confirming the 1:1 stoichiometry of subunits gamma and b. Altogether, the results indicate that the integrity of a unique copy of subunit e is essential for self-association of mammalian F(0)F(1)ATP synthase.

Ortho-proteogenomics: multiple proteomes investigation through orthology and a new MS-based protocol

The progress in sequencing technologies irrigates biology with an ever-increasing number of genome sequences. In most cases, the gene repertoire is predicted in silico and conceptually translated into proteins. As recently highlighted, the predicted genes exhibit frequent errors, particularly in start codons, with a serious impact on subsequent biological studies. A new "ortho-proteogenomic" approach is presented here for the annotation refinement of multiple genomes at once. It combines comparative genomics with an original proteomic protocol that allows the characterization of both N-terminal and internal peptides in a single experiment. This strategy was applied to the Mycobacterium genus with Mycobacterium smegmatis as the reference, and identified 946 distinct proteins, including 443 characterized N termini. These experimental data allowed the correction of 19% of the characterized start codons, the identification of 29 proteins missed during the annotation process, and the curation, thanks to comparative genomics, of 4328 sequences of 16 other Mycobacterium proteomes.

Hybridization of pulsed-Q dissociation and collision-activated dissociation in linear ion trap mass spectrometer for iTRAQ quantitation

Coupling of multiplex isobaric tags for relative and absolute quantitation (iTRAQ) to a sensitive linear ion trap (LTQ) mass spectrometer (MS) is a challenging, but highly promising approach for quantitative high-throughput proteomic profiling. Integration of the advantages of pulsed-Q dissociation (PQD) and collision-activated dissociation (CAD) fragmentation methods into a PQD-CAD hybrid mode, together with PQD optimization and data manipulation with a bioinformatics algorithm, resulted in a robust, sensitive and accurate iTRAQ quantitative proteomic workflow. The workflow was superior to the default PQD setting when profiling the proteome of a gastric cancer cell line, SNU5. Taken together, we established an optimized PQD-CAD hybrid workflow in LTQ-MS for iTRAQ quantitative proteomic profiling that may have wide applications in biological and biomedical research.

A comparative study of electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) versus SCX-IMAC-based methods for phosphopeptide isolation/enrichment

Electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) has been introduced recently for phosphopeptide enrichment. Here we compared ERLIC with the well-established SCX-IMAC for identifying phosphopeptides in EGF-treated A431 cells. The ERLIC approach detected a higher number of phosphopeptides (17 311) than SCX-IMAC (4850), but it only detected 926 unique phosphopeptides compared to 1315 in SCX-IMAC. Only 12% unique phosphopeptides were common to both approaches, suggesting that more comprehensive phosphoproteomes could be generated by complementing SCX-IMAC with ERLIC.

Simplified validation of borderline hits of database searches

Along with unequivocal hits produced by matching multiple MS/MS spectra to database sequences, LC-MS/MS analysis often yields a large number of hits of borderline statistical confidence. To simplify their validation, we propose to use rapid de novo interpretation of all acquired MS/MS spectra and, with the help of a simple software tool, display the candidate sequences together with each database search hit. We demonstrate that comparing hit database sequences and independent de novo interpretations of the same MS/MS spectra assists in rapid examination of ambiguous matches.

Increasing information from shotgun proteomic data by accounting for misassigned precursor ion masses

Although mass spectrometers are capable of providing high mass accuracy data, assignment of true monoisotopic precursor ion mass is complicated during data-dependent ion selection for LC-MS/MS analysis of complex mixtures. The complication arises when chromatographic peak widths for a given analyte exceed the time required to acquire a precursor ion mass spectrum. The result is that many measured monoisotopic masses are misassigned due to calculation from a single mass spectrum with poor ion statistics based on only a fraction of the total available ions for a given analyte. Such data in turn produces errors in automated database searches, where precursor m/z value is one search parameter. We propose here a postacquisition approach to correct misassigned monoisotopic m/z values that involves peak detection over the entire elution profile and correction of the precursor ion monoisotopic mass. As a result of using this approach to reprocess shotgun proteomic data we increased peptide sequence assignments by 10% while reducing the estimated false positive ratio from 1 to 0.2%. We also show that 4% of the salvaged identifications may be accounted for by correction of mixed tandem mass spectra resulting from fragmentation of multiple peptides simultaneously, a situation which we refer to as accidental CID.

Importance of systems biology in engineering microbes for biofuel production

Microorganisms have been rich sources for natural products, some of which have found use as fuels, commodity chemicals, specialty chemicals, polymers, and drugs, to name a few. The recent interest in production of transportation fuels from renewable resources has catalyzed numerous research endeavors that focus on developing microbial systems for production of such natural products. Eliminating bottlenecks in microbial metabolic pathways and alleviating the stresses due to production of these chemicals are crucial in the generation of robust and efficient production hosts. The use of systems-level studies makes it possible to comprehensively understand the impact of pathway engineering within the context of the entire host metabolism, to diagnose stresses due to product synthesis, and provides the rationale to cost-effectively engineer optimal industrial microorganisms.

Adaptive discriminant function analysis and reranking of MS/MS database search results for improved peptide identification in shotgun proteomics

Robust statistical validation of peptide identifications obtained by tandem mass spectrometry and sequence database searching is an important task in shotgun proteomics. PeptideProphet is a commonly used computational tool that computes confidence measures for peptide identifications. In this paper, we investigate several limitations of the PeptideProphet modeling approach, including the use of fixed coefficients in computing the discriminant search score and selection of the top scoring peptide assignment per spectrum only. To address these limitations, we describe an adaptive method in which a new discriminant function is learned from the data in an iterative fashion. We extend the modeling framework to go beyond the top scoring peptide assignment per spectrum. We also investigate the effect of clustering the spectra according to their spectrum quality score followed by cluster-specific mixture modeling. The analysis is carried out using data acquired from a mixture of purified proteins on four different types of mass spectrometers, as well as using a complex human serum data set. A special emphasis is placed on the analysis of data generated on high mass accuracy instruments.

A fast SEQUEST cross correlation algorithm

The SEQUEST program was the first and remains one of the most widely used tools for assigning a peptide sequence within a database to a tandem mass spectrum. The cross correlation score is the primary score function implemented within SEQUEST and it is this score that makes the tool particularly sensitive. Unfortunately, this score is computationally expensive to calculate, and thus, to make the score manageable, SEQUEST uses a less sensitive but fast preliminary score and restricts the cross correlation to just the top 500 peptides returned by the preliminary score. Classically, the cross correlation score has been calculated using Fast Fourier Transforms (FFT) to generate the full correlation function. We describe an alternate method of calculating the cross correlation score that does not require FFTs and can be computed efficiently in a fraction of the time. The fast calculation allows all candidate peptides to be scored by the cross correlation function, potentially mitigating the need for the preliminary score, and enables an E-value significance calculation based on the cross correlation score distribution calculated on all candidate peptide sequences obtained from a sequence database.

Application of capillary isotachophoresis-based multidimensional separations coupled with electrospray ionization-tandem mass spectrometry for characterization of mouse brain mitochondrial proteome

By employing a capillary ITP (CITP)/CZE-based proteomic technology, a total of 1795 distinct mouse Swiss-Prot protein entries (or 1705 nonredundant proteins) are identified from synaptic mitochondria isolated from mouse brain. 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. The degree of peptide overlapping among CITP fractions is even lower than that achieved using combined CIEF/nano-RP LC separations for the analysis of the same mitochondrial sample. When evaluating the protein sequence coverage by the number of distinct peptides mapping to each mitochondrial protein identification, CITP/CZE similarly achieves superior performance with 1041 proteins (58%) having 3 or more distinct peptides, 233 (13%) having 2 distinct peptides, and 521 (29%) having a single distinct peptide. The reproducibility of protein identifications is found to be around 86% by comparing proteins identified from repeated runs of the same mitochondrial sample. The analysis of the mouse mitochondrial proteome by two CITP/CZE runs results in the detection of 2095 distinct mouse Swiss-Prot protein entries (or 1992 nonredundant proteins), corresponding to 59% coverage of the updated Maestro mitochondrial reference set. The collective analysis from combined CITP/CZE and CIEF-based proteomic studies yields the identification of 2191 distinct mitochondrial protein entries (or 2082 nonredundant proteins), corresponding to 76% coverage of the MitoP2-database reference set.

Peptide and protein quantification using iTRAQ with electron transfer dissociation

Electron transfer dissociation (ETD) has become increasingly used in proteomic analyses due to its complementarity to collision-activated dissociation (CAD) and its ability to sequence peptides with post-translation modifications (PTMs). It was previously unknown, however, whether ETD would be compatible with a commonly employed quantification technique, isobaric tags for relative and absolute quantification (iTRAQ), since the fragmentation mechanisms and pathways of ETD differ significantly from CAD. We demonstrate here that ETD of iTRAQ labeled peptides produces c- and z-type fragment ions as well as reporter ions that are unique from those produced by CAD. Exact molecular formulas of product ions were determined by ETD fragmentation of iTRAQ-labeled synthetic peptides followed by high mass accuracy orbitrap mass analysis. These experiments revealed that ETD cleavage of the N-C(alpha) bond of the iTRAQ tag results in fragment ions that could be used for quantification. Synthetic peptide work demonstrates that these fragment ions provide up to three channels of quantification and that the quality is similar to that provided by beam-type CAD. Protein standards were used to evaluate peptide and protein quantification of iTRAQ labeling in conjunction with ETD, beam-type CAD, and pulsed Q dissociation (PQD) on a hybrid ion trap-orbitrap mass spectrometer. For reporter ion intensities above a certain threshold all three strategies provided reliable peptide quantification (average error <10%). Approximately 36%, 8%, and 16% of scans identified fall below this threshold for ETD, HCD, and PQD, respectively. At the protein level, average errors were 2.3%, 1.7%, and 3.6% for ETD, HCD, and PQD, respectively.

SeMoP: a new computational strategy for the unrestricted search for modified peptides using LC-MS/MS data

A novel computational approach, termed Search for Modified Peptides (SeMoP), for the unrestricted discovery and verification of peptide modifications in shotgun proteomic experiments using low resolution ion trap MS/MS spectra is presented. Various peptide modifications, including post-translational modifications, sequence polymorphisms, as well as sample handling-induced changes, can be identified using this approach. SeMoP utilizes a three-step strategy: (1) a standard database search to identify proteins in a sample; (2) an unrestricted search for modifications using a newly developed algorithm; and (3) a second standard database search targeted to specific modifications found using the unrestricted search. This targeted approach provides verification of discovered modifications and, due to increased sensitivity, a general increase in the number of peptides with the specific modification. The feasibility of the overall strategy has been first demonstrated in the analysis of 65 plasma proteins. Various sample handling induced modifications, such as beta-elimination of disulfide bridges and pyrocarbamidomethylation, as well as biologically induced modifications, such as phosphorylation and methylation, have been detected. A subsequent targeted Sequest search has been used to verify selected modifications, and a 4-fold increase in the number of modified peptides was obtained. In a second application, 1367 proteins of a cervical cancer cell line were processed, leading to detection of several novel amino acid substitutions. By conducting the search against a database of peptides derived from proteins with decoy sequences, a false discovery rate of less than 5% for the unrestricted search resulted. SeMoP is shown to be an effective and easily implemented approach for the discovery and verification of peptide modifications.

A robust linear regression based algorithm for automated evaluation of peptide identifications from shotgun proteomics by use of reversed-phase liquid chromatography retention time

BACKGROUND

Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches.

RESULTS

A robust linear regression based algorithm was developed to automate the evaluation of peptide identifications obtained from shotgun proteomic experiments. The algorithm scores peptides based on their predicted and observed reversed-phase liquid chromatography retention times. The robust algorithm does not require internal or external peptide standards to train or calibrate the linear regression model used for peptide retention time prediction. The algorithm is generic and can be incorporated into any database search program to perform automated evaluation of the candidate peptide matches based on their retention times. It provides a statistical score for each peptide match based on its retention time.

CONCLUSION

Analysis of peptide matches where the retention time score was included resulted in a significant reduction of false positive matches with little effect on the number of true positives. Overall higher sensitivities and specificities were achieved for database searches carried out with MassMatrix, Mascot and X!Tandem after implementation of the retention time based score algorithm.

Precursor-ion mass re-estimation improves peptide identification on hybrid instruments

Mass spectrometry-based proteomics experiments have become an important tool for studying biological systems. Identifying the proteins in complex mixtures by assigning peptide fragmentation spectra to peptide sequences is an important step in the proteomics process. The 1-2 ppm mass-accuracy of hybrid instruments, like the LTQ-FT, has been cited as a key factor in their ability to identify a larger number of peptides with greater confidence than competing instruments. However, in replicate experiments of an 18-protein mixture, we note parent masses deviate 171 ppm, on average, for ion-trap data directed identifications and 8 ppm, on average, for preview Fourier transform (FT) data directed identifications. These deviations are neither caused by poor calibration nor by excessive ion-loading and are most likely due to errors in parent mass estimation. To improve these deviations, we introduce msPrefix, a program to re-estimate a peptide's parent mass from an associated high-accuracy full-scan survey spectrum. In 18-protein mixture experiments, msPrefix parent mass estimates deviate only 1 ppm, on average, from the identified peptides. In a cell lysate experiment searched with a tolerance of 50 ppm, 2295 peptides were confidently identified using native data and 4560 using msPrefixed data. Likewise, in a plasma experiment searched with a tolerance of 50 ppm, 326 peptides were identified using native data and 1216 using msPrefixed data. msPrefix is also able to determine which MS/MS spectra were possibly derived from multiple precursor ions. In complex mixture experiments, we demonstrate that more than 50% of triggered MS/MS may have had multiple precursor ions and note that spectra with multiple candidate ions are less likely to result in an identification using TANDEM. These results demonstrate integration of msPrefix into traditional shotgun proteomics workflows significantly improves identification results.

Unrestrictive identification of multiple post-translational modifications from tandem mass spectrometry using an error-tolerant algorithm based on an extended sequence tag approach

Identification of post-translational modifications (PTMs) is important to understanding the biological functions of proteins. MS/MS is a useful tool to identify PTMs. Most existing search tools are restricted to take only a few types of PTMs as input. Here we describe a new algorithm, called MOD(i) (pronounced "mod eye"), that rapidly searches for all known types of PTMs at once without limiting a multitude of modified sites in a peptide. MOD(i) introduces the notion of a tag chain, a combination structure made from multiple sequence tags, that effectively localizes modified regions within a spectrum and overcomes de novo sequencing errors common in tag-based approaches. MOD(i) showed its performance competence by identifying various types of PTMs in analysis of PTM-rich proteins such as glyceraldehyde-3-phosphate dehydrogenase and lens protein. We demonstrated that MOD(i) innovatively manages the computational complexity of identifying multiple PTMs in a peptide, which may exist in a greater variety than usually expected. In addition, it is suggested that MOD(i) has great potential to discover novel modifications.

Quantitative proteomics reveals GIMAP family proteins 1 and 4 to be differentially regulated during human T helper cell differentiation

T helper (Th) cells differentiate into functionally distinct effector cell subsets of which Th1 and Th2 cells are best characterized. Besides T cell receptor signaling, IL-12-induced STAT4 and T-bet- and IL-4-induced STAT6 and GATA3 signaling pathways are the major players regulating the Th1 and Th2 differentiation process, respectively. However, there are likely to be other yet unknown factors or pathways involved. In this study we used quantitative proteomics exploiting cleavable ICAT labeling and LC-MS/MS to identify IL-4-regulated proteins from the microsomal fractions of CD4(+) cells extracted from umbilical cord blood. We were able to identify 557 proteins of which 304 were also quantified. This study resulted in the identification of the down-regulation of small GTPases GIMAP1 and GIMAP4 by IL-4 during Th2 differentiation. We also showed that both GIMAP1 and GIMAP4 genes are up-regulated by IL-12 and other Th1 differentiation-inducing cytokines in cells induced to differentiate toward Th1 lineage and down-regulated by IL-4 in cells induced to Th2. Our results indicate that the GIMAP (GTPase of the immunity-associated protein) family of proteins is differentially regulated during Th cell differentiation.

Global topology analysis of pancreatic zymogen granule membrane proteins

The zymogen granule is the specialized organelle in pancreatic acinar cells for digestive enzyme storage and regulated secretion and is a classic model for studying secretory granule function. Our long term goal is to develop a comprehensive architectural model for zymogen granule membrane (ZGM) proteins that would direct new hypotheses for subsequent functional studies. Our initial proteomics analysis focused on identification of proteins from purified ZGM (Chen, X., Walker, A. K., Strahler, J. R., Simon, E. S., Tomanicek-Volk, S. L., Nelson, B. B., Hurley, M. C., Ernst, S. A., Williams, J. A., and Andrews, P. C. (2006) Organellar proteomics: analysis of pancreatic zymogen granule membranes. Mol. Cell. Proteomics 5, 306-312). In the current study, a new global topology analysis of ZGM proteins is described that applies isotope enrichment methods to a protease protection protocol. Our results showed that tryptic peptides of ZGM proteins were separated into two distinct clusters according to their isobaric tag for relative and absolute quantification (iTRAQ) ratios for proteinase K-treated versus control zymogen granules. The low iTRAQ ratio cluster included cytoplasm-orientated membrane and membrane-associated proteins including myosin V, vesicle-associated membrane proteins, syntaxins, and all the Rab proteins. The second cluster having unchanged ratios included predominantly luminal proteins. Because quantification is at the peptide level, this technique is also capable of mapping both cytoplasm- and lumen-orientated domains from the same transmembrane protein. To more accurately assign the topology, we developed a statistical mixture model to provide probabilities for identified peptides to be cytoplasmic or luminal based on their iTRAQ ratios. By implementing this approach to global topology analysis of ZGM proteins, we report here an experimentally constrained, comprehensive topology model of identified zymogen granule membrane proteins. This model contributes to a firm foundation for developing a higher order architecture model of the ZGM and for future functional studies of individual ZGM proteins.

Label-free quantitative analysis of one-dimensional PAGE LC/MS/MS proteome: application on angiotensin II-stimulated smooth muscle cells secretome

A widely used method for protein identification couples prefractionation of protein samples by one-dimensional (1D) PAGE with LC/MS/MS. We developed a new label-free quantitative algorithm by combining measurements of spectral counting, ion intensity, and peak area on 1D PAGE-based proteomics. This algorithm has several improvements over other label-free quantitative algorithms: (i) Errors in peak detection are reduced because the retention time is based on each LC/MS/MS run and actual precursor m/z. (ii) Detection sensitivity is increased because protein quantification is based on the combination of peptide count, ion intensity, and peak area. (iii) Peak intensity and peak area are calculated in each LC/MS/MS run for all slices from 1D PAGE for every single identified protein and visualized as a Western blot image. The sensitivity and accuracy of this algorithm were demonstrated by using standard curves (17.4 fmol to 8.7 pmol), complex protein mixtures (30 fmol to 1.16 pmol) of known composition, and spiked protein (34.8 fmol to 17.4 pmol) in complex proteins. We studied the feasibility of this approach using the secretome of angiotensin II (Ang II)-stimulated vascular smooth muscle cells (VSMCs). From the VSMC-conditioned medium, 629 proteins were identified including 212 putative secreted proteins. 26 proteins were differently expressed in control and Ang II-stimulated VSMCs, including 18 proteins not previously reported. Proteins related to cell growth (CYR61, protein NOV, and clusterin) were increased, whereas growth arrest-specific 6 (GAS6) and growth/differentiation factor 6 were decreased by Ang II stimulation. Ang II-stimulated changes of plasminogen activator inhibitor-1, GAS6, cathepsin B, and periostin were validated by Western blot. In conclusion, a novel label-free quantitative analysis of 1D PAGE-LC/MS/MS-based proteomics has been successfully applied to the identification of new potential mediators of Ang II action and may provide an alternative to traditional protein staining methods.

The neoplastically transformed (CD30hi) Marek's disease lymphoma cell phenotype most closely resembles T-regulatory cells

INTRODUCTION

Marek's disease (MD), a herpesvirus-induced lymphoma of chickens is a unique natural model of CD30-overexpressing (CD30hi) lymphoma. We have previously proposed that the CD30hi neoplastically transformed CD4+ T cells in MD lymphomas have a phenotype antagonistic to cell mediated immunity. Here were test the hypothesis that the CD30hi neoplastically transformed MD lymphoma cells have a phenotype more closely resembling T-helper (Th)-2 or regulatory T (T-reg) cells.

MATERIALS AND METHODS

We separated ex vivo-derived CD30hi, from the CD30lo/- (non-transformed), MD lymphoma cells and then quantified the relative amounts of mRNA and proteins for cytokines and other genes that define CD4+ Th-1, Th-2 or T-reg phenotypes.

RESULTS AND DISCUSSION

Gene Ontology-based modeling of our data shows that the CD30hi MD lymphoma cells having a phenotype more similar to T-reg. Sequences that could be bound by the MD virus putative oncoprotein Meq in each of these genes' promoters suggests that the MD herpesvirus may play a direct role in maintaining this T-reg-like phenotype.

Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases

A key problem in computational proteomics is distinguishing between correct and false peptide identifications. We argue that evaluating the error rates of peptide identifications is not unlike computing generating functions in combinatorics. We show that the generating functions and their derivatives ( spectral energy and spectral probability) represent new features of tandem mass spectra that, similarly to Delta-scores, significantly improve peptide identifications. Furthermore, the spectral probability provides a rigorous solution to the problem of computing statistical significance of spectral identifications. The spectral energy/probability approach improves the sensitivity-specificity tradeoff of existing MS/MS search tools, addresses the notoriously difficult problem of "one-hit-wonders" in mass spectrometry, and often eliminates the need for decoy database searches. We therefore argue that the generating function approach has the potential to increase the number of peptide identifications in MS/MS searches.

A proteomics grade electron transfer dissociation-enabled hybrid linear ion trap-orbitrap mass spectrometer

Here we detail the modification of a quadrupole linear ion trap-orbitrap hybrid (QLT-orbitrap) mass spectrometer to accommodate a negative chemical ionization (NCI) source. The NCI source is used to produce fluoranthene radical anions for imparting electron transfer dissociation (ETD). The anion beam is stable, robust, and intense so that a sufficient amount of reagents can be injected into the QLT in only 4-8 ms. Following ion/ion reaction in the QLT, ETD product ions are mass-to-charge (m/z) analyzed in either the QLT (for speed and sensitivity) or the orbitrap (for mass resolution and accuracy). Here we describe the physical layout of this device, parametric optimization of anion transport, an evaluation of relevant ETD figures of merit, and the application of this instrument to protein sequence analysis. Described proteomic applications include complex peptide mixture analysis, post-translational modification (PTM) site identification, isotope-encoded quantitation, large peptide characterization, and intact protein analysis. From these experiments, we conclude the ETD-enabled orbitrap will provide the proteomic field with several new opportunities and represents an advance in protein sequence analysis technologies.

Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification

Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.

Fission yeast SWI/SNF and RSC complexes show compositional and functional differences from budding yeast

SWI/SNF chromatin-remodeling complexes have crucial roles in transcription and other chromatin-related processes. The analysis of the two members of this class in Saccharomyces cerevisiae, SWI/SNF and RSC, has heavily contributed to our understanding of these complexes. To understand the in vivo functions of SWI/SNF and RSC in an evolutionarily distant organism, we have characterized these complexes in Schizosaccharomyces pombe. Although core components are conserved between the two yeasts, the compositions of S. pombe SWI/SNF and RSC differ from their S. cerevisiae counterparts and in some ways are more similar to metazoan complexes. Furthermore, several of the conserved proteins, including actin-like proteins, are markedly different between the two yeasts with respect to their requirement for viability. Finally, phenotypic and microarray analyses identified widespread requirements for SWI/SNF and RSC on transcription including strong evidence that SWI/SNF directly represses iron-transport genes.