Trade-off between high sensitivity and increased potential for false positive peptide sequence matches using a two-dimensional linear ion trap for tandem mass spectrometry-based proteomics

Proteomic identification and validation of novel interactions of the putative tumor suppressor PRELP with membrane proteins including IGFI-R and p75NTR

Proline and arginine-rich end leucine-rich repeat protein (PRELP) is a member of the small leucine-rich repeat proteoglycans (SLRPs) family. Levels of PRELP mRNA are downregulated in many types of cancer, and PRELP has been reported to have suppressive effects on tumor cell growth, although the molecular mechanism has yet to be fully elucidated. Given that other SLRPs regulate signaling pathways through interactions with various membrane proteins, we reasoned that PRELP likely interacts with membrane proteins to maintain cellular homeostasis. To identify membrane proteins that interact with PRELP, we carried out coimmunoprecipitation coupled with mass spectrometry (CoIP-MS). We prepared membrane fractions from Expi293 cells transfected to overexpress FLAG-tagged PRELP or control cells and analyzed samples precipitated with anti-FLAG antibody by mass spectrometry. Comparison of membrane proteins in each sample identified several that seem to interact with PRELP; among them, we noted two growth factor receptors, insulin-like growth factor I receptor (IGFI-R) and low-affinity nerve growth factor receptor (p75NTR), interactions with which might help to explain PRELP's links to cancer. We demonstrated that PRELP directly binds to extracellular domains of these two growth factor receptors with low micromolar affinities by surface plasmon resonance analysis using recombinant proteins. Furthermore, cell-based analysis using recombinant PRELP protein showed that PRELP suppressed cell growth and affected cell morphology of A549 lung carcinoma cells, also at micromolar concentration. These results suggest that PRELP regulates cellular functions through interactions with IGFI-R and p75NTR and provide a broader set of candidate partners for further exploration.

Shotgun Proteomic Profiling of Bloodborne Nanoscale Extracellular Vesicles

Analyses of bloodborne nanoscale extracellular vesicles (nsEVs) have shown tremendous promise in enabling the development of noninvasive blood-based clinical diagnostic tests, predicting and monitoring the efficacy of treatment programs, and identifying new drug targets in the context of health conditions such as cancer and Alzheimer's disease. In this chapter we present a protocol for generating global nsEV proteomic profiles that can further the utility of nsEV analysis for the above biomedical applications by enlightening us of differences in protein abundance across normal and disease state nsEVs. This protocol features the use of magnetic particle-based immunoprecipitation to enrich highly purified populations of nsEVs directly from plasma or serum samples. The constituent proteins of these vesicles are subsequently characterized using a comparative shotgun proteomics approach that entails bottom-up, tandem mass spectrometric analysis of peptides generated by proteolytic digestion of nsEV-derived proteins. The methods described here are compatible with parallel processing of dozens of plasma or serum samples and can be valuable tools in enabling nsEV biomarker discoveries that have high translational relevance in the development of both novel therapeutics and blood sample diagnostic assays.

Magnetic Particle-Based Immunoprecipitation of Nanoscale Extracellular Vesicles from Biofluids

Analysis of nanoscale extracellular vesicles (nsEVs) present in blood, cell culture media, and other biofluids has shown tremendous promise in enabling the development of noninvasive blood-based clinical diagnostic tests, predicting and monitoring the efficacy of treatment programs, and providing molecular level insights into pathology that can enlighten new drug targets in the contexts of health conditions such as cancer and Alzheimer's Disease (AD). In this chapter, we present methods for using magnetic particle-based immunoprecipitation to enrich highly purified populations of nsEVs directly from plasma, serum, and other biofluids. These methods enable downstream analysis of nsEV protein and nucleic acid constituents in the contexts of both global omics profiling and quantification of individual protein or nucleic acid species of interest. Additionally, these methods allow the researcher to either enrich total nsEV populations or enrich nsEVs derived from a particular tissue type from the overall nsEV population. The methods described here are compatible with parallel processing of dozens of biofluid samples and can be valuable tools for enabling nsEV analyses that have high translational relevance in the development of both novel therapeutics and noninvasive diagnostic assays.

Detection and confirmation of α-cobratoxin in equine plasma by solid-phase extraction and liquid chromatography coupled to mass spectrometry

α-Cobratoxin (CTX) is a large peptide (71 amino acids) with strong analgesic effect and may be misused in sports such as horse racing. To prevent such misuse, a sensitive method is required for detection and confirmation of the toxin in equine samples. CTX was extracted from equine plasma using an optimized mixed-mode solid-phase extraction (SPE) procedure. Extracted CTX was reduced with dithiothreitol and alkylated with iodoacetamide, and then was digested by trypsin at 56°C for 30min. The digest was analysed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and tryptic peptides T2 (³CFITPDITSK¹²) and T4 (²⁴TWCDAFCSIR³³) were monitored for detection and confirmation of CTX. The limit of detection (LOD) was 0.05ng/mL for CTX in plasma, and the limit of confirmation (LOC) 0.2ng/mL. Unlike small peptides consisting of the 20 canonical amino acids, CTX was stable in equine plasma at ambient temperature for at least 24h. The developed analytical method was successfully applied to analysis of incurred plasma samples; CTX was detected in plasma collected 15min through 36h post subcutaneous administration of CTX (2.0mg dose) to a research horse, and confirmed 30min through 24h. Additionally, an approach named "reliable targeted SEQUEST search" has been proposed for assessing the specificity of T2 at product ion spectrum level for confirmation of CTX. T2 is uniquely specific for CTX, as evaluated with this approach and BLAST search. Furthermore, the effect of dimethyl sulfoxide (DMSO) as a mobile phase additive on electrospray (ESI) response of T2 and T4, background noise level and signal to noise ratio (S/N) was examined; DMSO increased signal intensity of T2 and T4 by a factor of less than 2. It is the first report that DMSO raised background noise level and did not improve S/N for the peptides, to the authors' knowledge. The developed analytical method may be applicable for analysis of CTX in plasma from other species such as greyhound dogs or even human beings.

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

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

Identification of outer membrane proteins from an Antarctic bacterium Pseudomonas syringae Lz4W

Subcellular fractionation of proteins is a preferred method of choice for detection and identification of proteins from complex mixtures such as bacterial cells. To characterize the membrane proteins of the Antarctic bacterium Pseudomonas syringae Lz4W, the membrane fractions were prepared using three different methods, namely Triton X-100 solubilization, sucrose density gradient, and carbonate extraction methods. The proteins were separated on one-dimensional polyacrylamide gels and analyzed using a combination of liquid chromatography-coupled electrospray ionization-MS. The membrane proteins that were prepared by carbonate extraction were separated on two-dimensional PAGE in different pI ranges using the detergent 2% amidosulfobetaine (ASB). The proteins were then subjected to matrix-assisted laser desorption ionization-time-of-flight/time-of-flight for analysis and identification. Because the genome sequence of P. syringae Lz4W is not known, the proteins were identified by using the relevant sequence databases of the Pseudomonas sp available at National Centre for Biotechnology Information (NCBI). The sequence identification of some tryptic peptides were validated by de novo sequencing and others by chemical modification and mass spectrometry. The peptide sequences of P. syringae Lz4W were then matched with the sequences of the peptides from different Pseudomonas sp. by similarity search of the proteins from different species using clustal W2 program. Thus by using a combination of the methods, we have been able to identify large number of proteins of this bacterial strain, which include most of the outer membrane proteins.

Fractionation of phosphopeptides on strong anion-exchange capillary trap column for large-scale phosphoproteome analysis of microgram samples

It is one of the key issues to develop powerful fractionating method to increase the identification of the low-abundance phosphopeptides. In this study, a semi-online 2-D LC separation strategy based on three-step fractionation of the enriched peptides on strong anion-exchange trap column was developed. It was demonstrated that the sensitivity and phosphoproteome coverage obtained by this fractionating method with strong anion-exchange trap column is much higher than those by the conventional methods based on C18 trap column. In addition, when the same amount of sample was loaded, the number of identified phosphopeptides had increased 108%. Combination of this three-step fractionation method with RPLC-MS/MS analysis by 300 min RP-gradient separation was applied to phosphoproteome analysis of human liver proteins, and 853 unique phosphopeptides was positively identified from 500 microg tryptic digest of human liver proteins. After three cycles' consecutive analyses, 1554 unique phosphopeptides and 1566 phosphorylated sites were totally identified from 735 phosphorylated proteins at a false discovery rate of <1% in about 54 h of analysis time.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

Techniques for accurate protein identification in shotgun proteomic studies of human, mouse, bovine, and chicken lenses

Analysis of shotgun proteomics datasets requires techniques to distinguish correct peptide identifications from incorrect identifications, such as linear discriminant functions and target/decoy protein databases. We report an efficient, flexible proteomic analysis workflow pipeline that implements these techniques to control both peptide and protein false discovery rates. We demonstrate its performance by analyzing two-dimensional liquid chromatography separations of lens proteins from human, mouse, bovine, and chicken lenses. We compared the use of International Protein Index databases to UniProt databases and no-enzyme SEQUEST searches to tryptic searches. Sequences present in the International Protein Index databases allowed detection of several novel crystallins. An alternate start codon isoform of betaA4 was found in human lens. The minor crystallin gammaN was detected for the first time in bovine and chicken lenses. Chicken gammaS was identified and is the first member of the gamma-crystallin family observed in avian lenses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12177-009-9042-6) contains supplementary material, which is available to authorized users.

Proteomic analysis of laser-microdissected paraffin-embedded tissues: (1) Stage-related protein candidates upon non-metastatic lung adenocarcinoma

We used formalin-fixed paraffin-embedded (FFPE) materials for biomarker discovery in cases of lung cancer using proteomic analysis. We conducted a retrospective global proteomic study in order to characterize protein expression reflecting clinical stages of individual patients with stage I lung adenocarcinoma without lymph node involvement (n=7). In addition, we studied more advanced stage IIIA with spread to lymph nodes (n=6), because the degree of lymph node involvement is the most important factor for staging. FFPE sections of cancerous lesions resected surgically from patients with well-characterized clinical history were subjected to laser microdissection (LMD) followed by Liquid Tissue solubilization and digestion trypsin. Spectral counting was used to measure the amounts of proteins identified by shotgun liquid chromatography (LC)/tandem mass spectrometry (MS/MS). More than 500 proteins were identified from IA and IIIA cases, and non-parametric statistics showed that 81 proteins correlated significantly with stage IA or IIIA. A subset of those proteins were verified by multiple-reaction monitoring mass spectrometric quantitation (MRM assay), described in other paper in this issue. These results demonstrated the technical feasibility of a global proteomic study using clinically well documented FFPE sections, and its possible utility for detailed retrospective disease analyses in order to improve therapeutic strategy.

Experimental approach for deep proteome measurements from small-scale microbial biomass samples

Many methods of microbial proteome characterizations require large quantities of cellular biomass (>1-2 g) for sample preparation and protein identification. Our experimental approach differs from traditional techniques by providing the ability to identify the proteomic state of a microbe from a few milligrams of starting cellular material. The small-scale, guanidine lysis method minimizes sample loss by achieving cellular lysis and protein digestion in a single-tube experiment. For this experimental approach, the freshwater microbe Shewanella oneidensis MR-1 and the purple non-sulfur bacterium Rhodopseudomonas palustris CGA0010 were used as model organisms for technology development and evaluation. A 2-D LC-MS/MS comparison between a standard sonication lysis method and the small-scale guanidine lysis techniques demonstrates that the guanidine lysis method is more efficient with smaller sample amounts of cell pellet (i.e., down to 1 mg). The described methodology enables deeper proteome measurements from a few milliliters of confluent bacterial cultures. We also report a new protocol for efficient lysis from small amounts of natural biofilm samples for deep proteome measurements, which should greatly enhance the emerging field of environmental microbial community proteomics. This straightforward sample boiling protocol is complementary to the small-scale guanidine lysis technique, is amenable for small sample quantities, and requires no special reagents that might complicate the MS measurements.

Preparation of monodisperse immobilized Ti(4+) affinity chromatography microspheres for specific enrichment of phosphopeptides

This study presented an approach to prepare monodisperse immobilized Ti(4+) affinity chromatography (Ti(4+)-IMAC) microspheres for specific enrichment of phosphopeptides in phosphoproteome analysis. Monodisperse polystyrene seed microspheres with a diameter of ca. 4.8mum were first prepared by a dispersion polymerization method. Monodisperse microspheres with a diameter of ca. 13mum were prepared using the seed microspheres by a single-step swelling and polymerization method. Ti(4+) ion was immobilized after chemical modification of the microspheres with phosphonate groups. The specificity of the Ti(4+)-IMAC microspheres to phosphopeptides was demonstrated by selective enrichment of phosphopeptides from mixture of tryptic digests of alpha-casein and bovine serum albumin (BSA) at molar ratio of 1 to 500 by MALDI-TOF MS analysis. The sensitivity of detection for phosphopeptides determined by MALDI-TOF MS was as low as 5fmol for standard tryptic digest of beta-casein. The Ti(4+)-IMAC microspheres were compared with commercial Fe(3+)-IMAC adsorbent and homemade Zr(4+)-IMAC microspheres for enrichment of phosphopeptides. The phosphopeptides and non-phosphopeptides identified by Fe(3+)-IMAC, Zr(4+)-IMAC and Ti(4+)-IMAC methods were 26, 114, 127 and 181, 11, 11 respectively for the same tryptic digest samples. The results indicated that the Ti(4+)-IMAC had the best performance for enrichment of phosphopeptides.

Generation and analysis of multidimensional protein identification technology datasets

Systems that couple two dimensional liquid chromatography (LC/LC) with tandem mass spectrometry are widely used in modern proteomics. One such system, multidimensional protein identification technology (MudPIT), couples strong cation exchange chromatography and reversed phase chromatography to tandem mass spectrometry in a single microcapillary column. Using database searching algorithms like SEQUEST and additional computational tools, researchers are able to analyze in great detail complex peptide mixtures generated from biofluids, tissues, cells, organelles, or protein complexes. This chapter describes the use of MudPIT on modern mass spectrometry instrumentation and describes a data analysis pipeline designed to provide low false positive rates and quantitative datasets.

Systematic comparison of the human saliva and plasma proteomes

The proteome of human salivary fluid has the potential to open new doors for disease biomarker discovery. A recent study to comprehensively identify and catalog the human ductal salivary proteome led to the compilation of 1166 proteins. The protein complexity of both saliva and plasma is large, suggesting that a comparison of these two proteomes will provide valuable insight into their physiological significance and an understanding of the unique and overlapping disease diagnostic potential that each fluid provides. To create a more comprehensive catalog of human salivary proteins, we have first compiled an extensive list of proteins from whole saliva (WS) identified through MS experiments. The WS list is thereafter combined with the proteins identified from the ductal parotid, and submandibular and sublingual (parotid/SMSL) salivas. In parallel, a core dataset of the human plasma proteome with 3020 protein identifications was recently released. A total of 1939 nonredundant salivary proteins were compiled from a total of 19 474 unique peptide sequences identified from whole and ductal salivas; 740 out of the total 1939 salivary proteins were identified in both whole and ductal saliva. A total of 597 of the salivary proteins have been observed in plasma. Gene ontology (GO) analysis showed similarities in the distributions of the saliva and plasma proteomes with regard to cellular localization, biological processes, and molecular function, but revealed differences which may be related to the different physiological functions of saliva and plasma. The comprehensive catalog of the salivary proteome and its comparison to the plasma proteome provides insights useful for future study, such as exploration of potential biomarkers for disease diagnostics.

Quantitative proteomic profiling of muscle type-dependent and age-dependent protein carbonylation in rat skeletal muscle mitochondria

Carbonylation is a highly prevalent protein modification in skeletal muscle mitochondria, possibly contributing to its functional decline with age. Using quantitative proteomics, we identified mitochondrial proteins susceptible to carbonylation in a muscle type (slow- vs fast-twitch)-dependent and age-dependent manner from Fischer 344 rat skeletal muscle. Fast-twitch muscle contained twice as many carbonylated mitochondrial proteins than did slow-twitch muscle, with 22 proteins showing significant changes in carbonylation state with age, the majority of these increasing in their amount of carbonylation. Ingenuity pathway analysis revealed that these proteins belong to functional classes and pathways known to be impaired in muscle aging, including cellular function and maintenance, fatty acid metabolism, and citrate cycle. Although our studies do not conclusively link protein carbonylation to these functional changes in aging muscle, they provide a unique catalogue of promising protein targets deserving further investigation because of their potential role in aging muscle decline.

Novel ion traps using planar resistive electrodes: implications for miniaturized mass analyzers

In radiofrequency ion traps, electric fields are produced by applying time-varying potentials between machined metal electrodes. The electrode shape constitutes a boundary condition and defines the field shape. This paper presents a new approach to making ion traps in which the electrodes consist of two ceramic discs, the facing surfaces of which are lithographically imprinted with sets of concentric metal rings and overlaid with a resistive material. A radial potential function can be applied to the resistive material such that the potential between the plates is quadrupolar, and ions are trapped between the plates. The electric field is independent of geometry and can be optimized electronically. The trap can produce any trapping field geometry, including both a toroidal trapping geometry and the traditional Paul-trap field. Dimensionally smaller ion trajectories, as would be produced in a miniaturized ion trap, can be achieved by increasing the potential gradient on the resistive material and operating the trap at higher frequency, rather than by making any physical changes to the trap or the electrodes. Obstacles to miniaturization of ion traps, such as fabrication tolerances, surface smoothness, electrode alignment, limited access for ionization or ion injection, and small trapping volume are addressed using this design.

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.

Automatic validation of phosphopeptide identifications by the MS2/MS3 target-decoy search strategy

Manual checking is commonly employed to validate the phosphopeptide identifications from database searching of tandem mass spectra. It is very time-consuming and labor intensive as the number of phosphopeptide identifications increases greatly. In this study, a simple automatic validation approach was developed for phosphopeptide identification by combining consecutive stage mass spectrometry data and the target-decoy database searching strategy. Only phosphopeptides identified from both MS2 and its corresponding MS3 were accepted for further filtering, which greatly improved the reliability in phosphopeptide identification. Before database searching, the spectra were validated for charge state and neutral loss peak intensity, and then the invalid MS2/MS3 spectra were removed, which greatly reduced the database searching time. It was found that the sensitivity was significantly improved in MS2/MS3 strategy as the number of identified phosphopeptides was 2.5 times that obtained by the conventional filter-based MS2 approach. Because of the use of the target-decoy database, the false-discovery rate (FDR) of the identified phosphopeptides could be easily determined, and it was demonstrated that the determined FDR can precisely reflect the actual FDR without any manual validation stage.

Proteomics analysis of cells in whole saliva from oral cancer patients via value-added three-dimensional peptide fractionation and tandem mass spectrometry

Whole human saliva possesses tremendous potential in clinical diagnostics, particularly for conditions within the oral cavity such as oral cancer. Although many have studied the soluble fraction of whole saliva, few have taken advantage of the diagnostic potential of the cells present in saliva, and none have taken advantage of proteomics capabilities for their study. We report on a novel proteomics method with which we characterized for the first time cells contained in whole saliva from patients diagnosed with oral squamous cell carcinoma. Our method uses three dimensions of peptide fractionation, combining the following steps: preparative IEF using free flow electrophoresis, strong cation exchange step gradient chromatography, and microcapillary reverse-phase liquid chromatography. We determined that the whole saliva samples contained enough cells, mostly exfoliated epithelial cells, providing adequate amounts of total protein for proteomics analysis. From a mixture of four oral cancer patient samples, the analysis resulted in a catalogue of over 1000 human proteins, each identified from at least two peptides, including numerous proteins with a role in oral squamous cell carcinoma signaling and tumorigenesis pathways. Additionally proteins from over 30 different bacteria were identified, some of which putatively contribute to cancer development. The combination of preparative IEF followed by strong cation exchange chromatography effectively fractionated the complex peptide mixtures despite the closely related physiochemical peptide properties of these separations (pI and solution phase charge, respectively). Furthermore compared with our two-step method combining preparative IEF and reverse-phase liquid chromatography, our three-step method identified significantly more cellular proteins while retaining higher confidence protein identification enabled by peptide pI information gained through IEF. Thus, for detecting salivary markers of oral cancer and possibly other conditions of the oral cavity, the results confirm both the potential of analyzing the cells in whole saliva and doing so with our proteomics method.

Identification of carbonylated proteins from enriched rat skeletal muscle mitochondria using affinity chromatography-stable isotope labeling and tandem mass spectrometry

We describe a strategy for the identification of carbonylated proteins from complex protein mixtures that combines biotin hydrazide labeling of protein carbonyl groups, avidin affinity chromatography, multiplexed iTRAQ reagent stable isotope labeling, and analysis using pulsed Q dissociation (PQD) operation on an LTQ linear ion trap mass spectrometer. This strategy provided the ability to distinguish biotin hydrazide labeled, avidin purified, carbonylated proteins from non-carbonylated background proteins with affinity for the avidin column, derived from a control sample. Applying this strategy to the identification of crudely enriched rat skeletal muscle mitochondrial protein isolates, we generated a catalogue of over 200 carbonylated proteins by virtue of their quantitative enrichment compared to the control sample. The catalogue contains many mitochondrial localized proteins shown to be susceptible to carbonyl modification for the first time, including numerous transmembrane proteins involved in oxidative phosphorylation. Other oxidative modifications (e.g. nitrosylation, hydroxylation) were also identified on many of the carbonylated proteins, providing further evidence of the susceptibility of these proteins to oxidative damage. The results also demonstrate the utility of PQD operation on the LTQ instrument for quantitative analysis of iTRAQ reagent-labeled peptide mixtures, as well as the quantitative reproducibility of the avidin-affinity enrichment method.

Systematic characterization of high mass accuracy influence on false discovery and probability scoring in peptide mass fingerprinting

Whereas the bearing of mass measurement error on protein identification is sometimes underestimated, uncertainty in observed peptide masses unavoidably translates to ambiguity in subsequent protein identifications. Although ongoing instrumental advances continue to make high accuracy mass spectrometry (MS) increasingly accessible, many proteomics experiments are still conducted with rather large mass error tolerances. In addition, the ranking schemes of most protein identification algorithms do not include a meaningful incorporation of mass measurement error. This article provides a critical evaluation of mass error tolerance as it pertains to false positive peptide and protein associations resulting from peptide mass fingerprint (PMF) database searching. High accuracy, high resolution PMFs of several model proteins were obtained using matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR-MS). Varying levels of mass accuracy were simulated by systematically modulating the mass error tolerance of the PMF query and monitoring the effect on figures of merit indicating the PMF quality. Importantly, the benefits of decreased mass error tolerance are not manifest in Mowse scores when operating at tolerances in the low parts-per-million range but become apparent with the consideration of additional metrics that are often overlooked. Furthermore, the outcomes of these experiments support the concept that false discovery is closely tied to mass measurement error in PMF analysis. Clear establishment of this relation demonstrates the need for mass error-aware protein identification routines and argues for a more prominent contribution of high accuracy mass measurement to proteomic science.

Analysis and validation of proteomic data generated by tandem mass spectrometry

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

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

Background

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

Results

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

Conclusion

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

Informatics strategies for large-scale novel cross-linking analysis

The detection of protein interactions in biological systems represents a significant challenge for today's technology. Chemical cross-linking provides the potential to impart new chemical bonds in a complex system that result in mass changes in a set of tryptic peptides detected by mass spectrometry. However, system complexity and cross-linking product heterogeneity have precluded widespread chemical cross-linking use for large-scale identification of protein-protein interactions. The development of mass spectrometry identifiable cross-linkers called protein interaction reporters (PIRs) has enabled on-cell chemical cross-linking experiments with product type differentiation. However, the complex datasets resultant from PIR experiments demand new informatics capabilities to allow interpretation. This manuscript details our efforts to develop such capabilities and describes the program X-links, which allows PIR product type differentiation. Furthermore, we also present the results from Monte Carlo simulation of PIR-type experiments to provide false discovery rate estimates for the PIR product type identification through observed precursor and released peptide masses. Our simulations also provide peptide identification calculations based on accurate masses and database complexity that can provide an estimation of false discovery rates for peptide identification. Overall, the calculations show a low rate of false discovery of PIR product types due to random mass matching of approximately 12% with 10 ppm mass measurement accuracy and spectral complexity resulting from 100 peptides. In addition, consideration of a reduced database resulting from stage 1 analysis of Shewanella oneidensis MR-1 containing 367 proteins resulted in a significant reduction of expected identification false discovery rate estimation compared to that from the entire Shewanella oneidensis MR-1 proteome.

Quantitative shotgun proteomics using a protease with broad specificity and normalized spectral abundance factors

Non-specific proteases are rarely used in quantitative shotgun proteomics due to potentially high false discovery rates. Yet, there are instances when application of a non-specific protease is desirable to obtain sufficient sequence coverage of otherwise poorly accessible proteins or structural domains. Using the non-specific protease, proteinase K, we analyzed Saccharomyces cerevisiae preparations grown in (14)N rich media and (15)N minimal media and obtained relative quantitation from the dataset using normalized spectral abundance factors (NSAFs). A critical step in using a spectral counting based approach for quantitative proteomics is ensuring the inclusion of high quality spectra in the dataset. One way to do this is to minimize the false discovery rate, which can be accomplished by applying different filters to a searched dataset. Natural log transformation of proteinase K derived NSAF values followed a normal distribution and allowed for statistical analysis by the t-test. Using this approach, we generated a dataset of 719 unique proteins found in each of the three independent biological replicates, of which 84 showed a statistically significant difference in expression levels between the two growth conditions.

Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors

Mass spectrometry-based approaches are commonly used to identify proteins from multiprotein complexes, typically with the goal of identifying new complex members or identifying post-translational modifications. However, with the recent demonstration that spectral counting is a powerful quantitative proteomic approach, the analysis of multiprotein complexes by mass spectrometry can be reconsidered in certain cases. Using the chromatography-based approach named multidimensional protein identification technology, multiprotein complexes may be analyzed quantitatively using the normalized spectral abundance factor that allows comparison of multiple independent analyses of samples. This study describes an approach to visualize multiprotein complex datasets that provides structure function information that is superior to tabular lists of data. In this method review, we describe a reanalysis of the Rpd3/Sin3 small and large histone deacetylase complexes previously described in a tabular form to demonstrate the normalized spectral abundance factor approach.

Carbonylation of adipose proteins in obesity and insulin resistance: identification of adipocyte fatty acid-binding protein as a cellular target of 4-hydroxynonenal

Obesity is a state of mild inflammation correlated with increased oxidative stress. In general, pro-oxidative conditions lead to production of reactive aldehydes such as trans-4-hydroxy-2-nonenal (4-HNE) and trans-4-oxo-2-nonenal implicated in the development of a variety of metabolic diseases. To investigate protein modification by 4-HNE as a consequence of obesity and its potential relationship to the development of insulin resistance, proteomics technologies were utilized to identify aldehyde-modified proteins in adipose tissue. Adipose proteins from lean insulin-sensitive and obese insulin-resistant C57Bl/6J mice were incubated with biotin hydrazide and detected using horseradish peroxidase-conjugated streptavidin. High carbohydrate, high fat feeding of mice resulted in a approximately 2-3-fold increase in total adipose protein carbonylation. Consistent with an increase in oxidative stress in obesity, the abundance of glutathione S-transferase A4 (GSTA4), a key enzyme responsible for metabolizing 4-HNE, was decreased approximately 3-4-fold in adipose tissue of obese mice. To identify specific carbonylated proteins, biotin hydrazide-modified adipose proteins from obese mice were captured using avidin-Sepharose affinity chromatography, proteolytically digested, and subjected to LC-ESI MS/MS. Interestingly enzymes involved in cellular stress response, lipotoxicity, and insulin signaling such as glutathione S-transferase M1, peroxiredoxin 1, glutathione peroxidase 1, eukaryotic elongation factor 1alpha-1 (eEF1alpha1), and filamin A were identified. The adipocyte fatty acid-binding protein, a protein implicated in the regulation of insulin resistance, was found to be carbonylated in vivo with 4-HNE. In vitro modification of adipocyte fatty acid-binding protein with 4-HNE was mapped to Cys-117, occurred equivalently using either the R or S enantiomer of 4-HNE, and reduced the affinity of the protein for fatty acids approximately 10-fold. These results indicate that obesity is accompanied by an increase in the carbonylation of a number of adipose-regulatory proteins that may serve as a mechanistic link between increased oxidative stress and the development of insulin resistance.

Current literature in mass spectrometry

In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of mass spectrometry. Each bibliography is divided into 11 sections: 1 Books, Reviews & Symposia; 2 Instrumental Techniques & Methods; 3 Gas Phase Ion Chemistry; 4 Biology/Biochemistry: Amino Acids, Peptides & Proteins; Carbohydrates; Lipids; Nucleic Acids; 5 Pharmacology/Toxicology; 6 Natural Products; 7 Analysis of Organic Compounds; 8 Analysis of Inorganics/Organometallics; 9 Surface Analysis; 10 Environmental Analysis; 11 Elemental Analysis. Within each section, articles are listed in alphabetical order with respect to author (6 Weeks journals ‐ Search completed at 4th. Oct. 2006)

Peptide retention prediction applied to proteomic data analysis

A retention prediction model was developed for peptides separated in reversed-phase chromatography. The model was utilized to identify and exclude the false positive (FP) peptide identifications obtained via database search. The selected database included human proteins, as well as decoy sequences of random proteins. The FP peptide detection rate was defined either as number of retention time outliers, or random decoy sequence identifications. The FP rate for various MASCOT scores was calculated. The peptides identified in one-dimensional (1D) and two-dimensional (2D) liquid chromatography/mass spectrometry (LC/MS) experiments were validated by prediction models. Multi-dimensional LC was based on two orthogonal reversed-phase chromatography modes; prediction models were successfully applied for data filtering in both separation dimensions.

The use of proteomic techniques to explore the holistic effects of nutrients in vivo

The availability of ‘omics’ technologies is transforming scientific approaches to physiological problems from a reductionist viewpoint to that of a holistic viewpoint. This is of profound importance in nutrition, since the integration of multiple systems at the level of gene expression on the synthetic side through to metabolic enzyme activity on the degradative side combine to govern nutrient availability to tissues. Protein activity is central to the process of nutrition from the initial absorption of nutrients via uptake carriers in the gut, through to distribution and transport in the blood, metabolism by degradative enzymes in tissues and excretion through renal tubule exchange proteins. Therefore, the global profiling of the proteome, defined as the entire protein complement of the genome expressed in a particular cell or organ, or in plasma or serum at a particular time, offers the potential for identification of important biomarkers of nutritional state that respond to alterations in diet. The present review considers the published evidence of nutritional modulation of the proteome in vivo which has expanded exponentially over the last 3 years. It highlights some of the challenges faced by researchers using proteomic approaches to understand the interactions of diet with genomic and metabolic–phenotypic variables in normal populations.

Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors

Components of multiprotein complexes are routinely determined by using proteomic approaches. However, this information lacks functional content except when new complex members are identified. To analyze quantitatively the abundance of proteins in human Mediator we used normalized spectral abundance factors generated from shotgun proteomics data sets. With this approach we define a common core of mammalian Mediator subunits shared by alternative forms that variably associate with the kinase module and RNA polymerase (pol) II. Although each version of affinity-purified Mediator contained some kinase module and RNA pol II, Mediator purified through F-Med26 contained the most RNA pol II and the least kinase module as demonstrated by the normalized spectral abundance factor approach. The distinct forms of Mediator were functionally characterized by using a transcriptional activity assay, where F-Med26 Mediator/RNA pol II was the most active. This method of protein complex visualization has important implications for the analysis of multiprotein complexes and assembly of protein interaction networks.

Improved validation of peptide MS/MS assignments using spectral intensity prediction

A major limitation in identifying peptides from complex mixtures by shotgun proteomics is the ability of search programs to accurately assign peptide sequences using mass spectrometric fragmentation spectra (MS/MS spectra). Manual analysis is used to assess borderline identifications; however, it is error-prone and time-consuming, and criteria for acceptance or rejection are not well defined. Here we report a Manual Analysis Emulator (MAE) program that evaluates results from search programs by implementing two commonly used criteria: 1) consistency of fragment ion intensities with predicted gas phase chemistry and 2) whether a high proportion of the ion intensity (proportion of ion current (PIC)) in the MS/MS spectra can be derived from the peptide sequence. To evaluate chemical plausibility, MAE utilizes similarity (Sim) scoring against theoretical spectra simulated by MassAnalyzer software (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922) using known gas phase chemical mechanisms. The results show that Sim scores provide significantly greater discrimination between correct and incorrect search results than achieved by Sequest XCorr scoring or Mascot Mowse scoring, allowing reliable automated validation of borderline cases. To evaluate PIC, MAE simplifies the DTA text files summarizing the MS/MS spectra and applies heuristic rules to classify the fragment ions. MAE output also provides data mining functions, which are illustrated by using PIC to identify spectral chimeras, where two or more peptide ions were sequenced together, as well as cases where fragmentation chemistry is not well predicted.

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.

Proteomics technology in systems biology

It has now become apparent that a full understanding of a biological process (e.g. a disease state) is only possible if all biomolecular interactions are taken into account. Systems biology works towards understanding the intricacies of cellular life through the collaborative efforts of biologists, chemists, mathematicians and computer scientists and recently, a number of laboratories around the world have embarked upon such research agendas. The fields of genomics and proteomics are foundational in systems biology studies and a great deal of research is currently being conducted in each worldwide. Moreover, many technological advances (particularly in mass spectrometry) have led to a dramatic rise in the number of proteomic studies over the past two decades. This short review summarizes a selection of technological innovations in proteomics that contribute to systems biology studies.