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

Allergy, asthma, and proteomics: opportunities with immediate impact

Allergy is widely discussed by researchers due to its complex mechanism that leads to disorders and injuries, but the reason behind the allergic status remains unclear. Current treatments are insufficient to improve the patient's quality of life significantly. New technologies in scientific and technological development are emerging. For instance, the union between allergy and peptidomics and bioinformatics tools may help fill the gaps in this field, diagnosis, and treatment. In this review, we look at peptidomics and address some findings, such as target proteins or biomarkers that help better understand mechanisms that lead to inflammation, organ damage, and, consequently, poor quality of life or even death.

An improved scoring method for the identification of endogenous peptides based on the Mascot MS/MS ion search

To identify endogenous peptides using MS/MS analysis and searching against a polypeptide sequence database, a non-enzyme specific (NES) search considering all of the possible proteolytic cleavages is required. However, the use of a NES search generates more false positive hits than an enzyme specific search, and therefore shows lower identification performance. In this study, the use of the sub-ranked matches for improving the identification performance of the Mascot NES search was investigated and a new scoring method was developed that considered the contribution of all sub-ranked random match probabilities, named the contribution score (CS). The CS showed the highest identification sensitivity using the Mascot NES search with a full protein database when compared to the use of the Mascot first ranked score and the delta score (DS). The confident peptides identified by DS and CS were shown to be complementary. When applied to plant endogenous peptide identification, the identification numbers of tomato endogenous peptides using DS and CS were 176.3% and 184.2%, respectively, higher than the use of the first ranked score of Mascot. The combination of DS and CS identified 200.0% and 8.6% more tomato endogenous peptides compared to the use of Mascot and DS, respectively. This method by combining the CS and DS can significantly improve the identification performance of endogenous peptides without complex computational steps and is also able to improve the identification performance of the enzyme specific search. In addition to the application in the plant peptidomics analysis, this method may be applied to the improvement of peptidomics studies in different species. A web interface for calculating the DS and CS based on Mascot search results was developed herein.

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.

Critical Role of COI1-Dependent Jasmonate Pathway in AAL toxin induced PCD in Tomato Revealed by Comparative Proteomics

Alternaria alternata f.sp. Lycopersici (AAL) toxin induces programmed cell death (PCD) in susceptible tomato (Solanum lycopersicum) leaves. Jasmonate (JA) promotes AAL toxin induced PCD in a COI1 (coronatine insensitive 1, JA receptor)-dependent manner by enhancement of reactive oxygen species (ROS) production. To further elucidate the underlying mechanisms of this process, we performed a comparative proteomic analysis using tomato jasmonic acid insensitive1 ( jai1), the receptor mutant of JA, and its wild type (WT) after AAL toxin treatment with or without JA treatment. A total of 10367 proteins were identified in tomato leaves using isobaric tags for relative and absolute quantitation (iTRAQ) quantitative proteomics approach. 2670 proteins were determined to be differentially expressed in response to AAL toxin and JA. Comparison between AAL toxin treated jai1 and its WT revealed the COI1-dependent JA pathway regulated proteins, including pathways related to redox response, ceramide synthesis, JA, ethylene (ET), salicylic acid (SA) and abscisic acid (ABA) signaling. Autophagy, PCD and DNA damage related proteins were also identified. Our data suggest that COI1-dependent JA pathway enhances AAL toxin induced PCD through regulating the redox status of the leaves, other phytohormone pathways and/or important PCD components.

Seasonal Variation of Carbon Metabolism in the Cambial Zone of Eucalyptus grandis

Eucalyptus species are the most widely hardwood planted in the world. It is one of the successful examples of commercial forestry plantation in Brazil and other tropical and subtropical countries. The tree is valued for its rapid growth, adaptability and wood quality. Wood formation is the result of cumulative annual activity of the vascular cambium. This cambial activity is generally related to the alternation of cold and warm, and/or dry and rainy seasons. Efforts have focused on analysis of cambial zone in response to seasonal variations in trees from temperate zones. However, little is known about the molecular changes triggered by seasonal variations in trees from tropical countries. In this work we attempted to establish a global view of seasonal alterations in the cambial zone of Eucalyptus grandis Hill ex Maiden, emphasizing changes occurring in the carbon metabolism. Using transcripts, proteomics and metabolomics we analyzed the tissues harvested in summer-wet and winter-dry seasons. Based on proteomics analysis, 70 proteins that changed in abundance were successfully identified. Transcripts for some of these proteins were analyzed and similar expression patterns were observed. We identified 19 metabolites differentially abundant. Our results suggest a differential reconfiguration of carbon partioning in E. grandis cambial zone. During summer, pyruvate is primarily metabolized via ethanolic fermentation, possibly to regenerate NAD(+) for glycolytic ATP production and cellular maintenance. However, in winter there seems to be a metabolic change and we found that some sugars were highly abundant. Our results revealed a dynamic change in E. grandis cambial zone due to seasonality and highlight the importance of glycolysis and ethanolic fermentation for energy generation and maintenance in Eucalyptus, a fast growing tree.

Dynamics of the lipid droplet proteome of the Oleaginous yeast rhodosporidium toruloides

Lipid droplets (LDs) are ubiquitous organelles that serve as a neutral lipid reservoir and a hub for lipid metabolism. Manipulating LD formation, evolution, and mobilization in oleaginous species may lead to the production of fatty acid-derived biofuels and chemicals. However, key factors regulating LD dynamics remain poorly characterized. Here we purified the LDs and identified LD-associated proteins from cells of the lipid-producing yeast Rhodosporidium toruloides cultured under nutrient-rich, nitrogen-limited, and phosphorus-limited conditions. The LD proteome consisted of 226 proteins, many of which are involved in lipid metabolism and LD formation and evolution. Further analysis of our previous comparative transcriptome and proteome data sets indicated that the transcription level of 85 genes and protein abundance of 77 proteins changed under nutrient-limited conditions. Such changes were highly relevant to lipid accumulation and partially confirmed by reverse transcription-quantitative PCR. We demonstrated that the major LD structure protein Ldp1 is an LD marker protein being upregulated in lipid-rich cells. When overexpressed in Saccharomyces cerevisiae, Ldp1 localized on the LD surface and facilitated giant LD formation, suggesting that Ldp1 plays an important role in controlling LD dynamics. Our results significantly advance the understanding of the molecular basis of lipid overproduction and storage in oleaginous yeasts and will be valuable for the development of superior lipid producers.

Proteome-wide profiling of carbonylated proteins and carbonylation sites in HeLa cells under mild oxidative stress conditions

A number of oxidative protein modifications have been well characterized during the past decade. Presumably, reversible oxidative posttranslational modifications (PTMs) play a significant role in redox signaling pathways, whereas irreversible modifications including reactive protein carbonyl groups are harmful, as their levels are typically increased during aging and in certain diseases. Despite compelling evidence linking protein carbonylation to numerous disorders, the underlying molecular mechanisms at the proteome remain to be identified. Recent advancements in analysis of PTMs by mass spectrometry provided new insights into the mechanisms of protein carbonylation, such as protein susceptibility and exact modification sites, but only for a limited number of proteins. Here we report the first proteome-wide study of carbonylated proteins including modification sites in HeLa cells for mild oxidative stress conditions. The analysis relied on our recent strategy utilizing mass spectrometry-based enrichment of carbonylated peptides after DNPH derivatization. Thus a total of 210 carbonylated proteins containing 643 carbonylation sites were consistently identified in three replicates. Most carbonylation sites (284, 44.2%) resulted from oxidation of lysine residues (aminoadipic semialdehyde). Additionally, 121 arginine (18.8%), 121 threonine (18.8%), and 117 proline residues (18.2%) were oxidized to reactive carbonyls. The sequence motifs were significantly enriched for lysine and arginine residues near carbonylation sites (±10 residues). Gene Ontology analysis revealed that 80% of the carbonylated proteins originated from organelles, 50% enrichment of which was demonstrated for the nucleus. Moreover, functional interactions between carbonylated proteins of kinetochore/spindle machinery and centrosome organization were significantly enriched. One-third of the 210 carbonylated proteins identified here are regulated during apoptosis.

A novel algorithm for validating peptide identification from a shotgun proteomics search engine

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has revolutionized the proteomics analysis of complexes, cells, and tissues. In a typical proteomic analysis, the tandem mass spectra from a LC-MS/MS experiment are assigned to a peptide by a search engine that compares the experimental MS/MS peptide data to theoretical peptide sequences in a protein database. The peptide spectra matches are then used to infer a list of identified proteins in the original sample. However, the search engines often fail to distinguish between correct and incorrect peptides assignments. In this study, we designed and implemented a novel algorithm called De-Noise to reduce the number of incorrect peptide matches and maximize the number of correct peptides at a fixed false discovery rate using a minimal number of scoring outputs from the SEQUEST search engine. The novel algorithm uses a three-step process: data cleaning, data refining through a SVM-based decision function, and a final data refining step based on proteolytic peptide patterns. Using proteomics data generated on different types of mass spectrometers, we optimized the De-Noise algorithm on the basis of the resolution and mass accuracy of the mass spectrometer employed in the LC-MS/MS experiment. Our results demonstrate De-Noise improves peptide identification compared to other methods used to process the peptide sequence matches assigned by SEQUEST. Because De-Noise uses a limited number of scoring attributes, it can be easily implemented with other search engines.

A multi-omic map of the lipid-producing yeast Rhodosporidium toruloides

Triacylglycerols are among the most attractive alternative raw materials for biofuel development. Current oil plant-based technologies are limited in terms of triacylglycerol production capacity and rate. These limitations may be circumvented by biotransformation of carbohydrates into lipids; however, our understanding of microbial oleaginicity remains limited. Here we present the results of a multi-omic analysis of Rhodosporidium toruloides, a robust triacylglycerol-producing fungus. The assembly of genome and transcriptome sequencing data reveals a genome of 20.2 Mb containing 8,171 protein-coding genes, the majority of which have multiple introns. Genes including a novel fatty acid synthase are predicted to participate in metabolic pathways absent in non-oleaginous yeasts. Transcriptomic and proteomic data suggest that lipid accumulation under nitrogen-limited conditions correlates with the induction of lipogenesis, nitrogenous compound recycling, macromolecule metabolism and autophagy. The multi-omic map of R. toruloides therefore provides a valuable resource for efforts to rationally engineer lipid-production pathways.

Comprehensive phosphoproteome analysis of INS-1 pancreatic β-cells using various digestion strategies coupled with liquid chromatography-tandem mass spectrometry

Type 2 diabetes results from aberrant regulation of the phosphorylation cascade in beta-cells. Phosphorylation in pancreatic beta-cells has not been examined extensively, except with regard to subcellular phosphoproteomes using mitochondria. Thus, robust, comprehensive analytical strategies are needed to characterize the many phosphorylated proteins that exist, because of their low abundance, the low stoichiometry of phosphorylation, and the dynamic regulation of phosphoproteins. In this study, we attempted to generate data on a large-scale phosphoproteome from the INS-1 rat pancreatic beta-cell line using linear ion trap MS/MS. To profile the phosphoproteome in-depth, we used comprehensive phosphoproteomic strategies, including detergent-based protein extraction (SDS and SDC), differential sample preparation (in-gel, in-solution digestion, and FASP), TiO2 enrichment, and MS replicate analyses (MS2-only and multiple-stage activation). All spectra were processed and validated by stringent multiple filtering using target and decoy databases. We identified 2467 distinct phosphorylation sites on 1419 phosphoproteins using 4 mg of INS-1 cell lysate in 24 LC-MS/MS runs, of which 683 (27.7%) were considered novel phosphorylation sites that have not been characterized in human, mouse, or rat homologues. Our informatics data constitute a rich bioinformatics resource for investigating the function of reversible phosphorylation in pancreatic beta-cells. In particular, novel phosphorylation sites on proteins that mediate the pathology of type 2 diabetes, such as Pdx-1, Nkx.2, and Srebf1, will be valuable targets in ongoing phosphoproteomics studies.

Ubiquitinated proteome: ready for global?

Ubiquitin (Ub) is a small and highly conserved protein that can covalently modify protein substrates. Ubiquitination is one of the major post-translational modifications that regulate a broad spectrum of cellular functions. The advancement of mass spectrometers as well as the development of new affinity purification tools has greatly expedited proteome-wide analysis of several post-translational modifications (e.g. phosphorylation, glycosylation, and acetylation). In contrast, large-scale profiling of lysine ubiquitination remains a challenge. Most recently, new Ub affinity reagents such as Ub remnant antibody and tandem Ub binding domains have been developed, allowing for relatively large-scale detection of several hundreds of lysine ubiquitination events in human cells. Here we review different strategies for the identification of ubiquitination site and discuss several issues associated with data analysis. We suggest that careful interpretation and orthogonal confirmation of MS spectra is necessary to minimize false positive assignments by automatic searching algorithms.

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.

Estimating the confidence of peptide identifications without decoy databases

Using decoy databases to compute the confidence of peptide identifications has become the standard procedure for mass spectrometry driven proteomics. While decoy databases have numerous advantages, they double the run time and are not applicable to all peptide identification problems such as error-tolerant or de novo searches or the large-scale identification of cross-linked peptides. Instead, we propose a fast, simple and robust mixture modeling approach to estimate the confidence of peptide identifications without the need for decoy database searches, which automatically checks whether its underlying assumptions are fulfilled. This approach is then evaluated on 41 LC/MS data sets of varying complexity and origin. The results are very similar to those of the decoy database strategy at a negligible computational cost. Our approach is applicable not only to standard protein identification workflows, but also to proteomics problems for which meaningful decoy databases cannot be constructed.

Target-decoy search strategy for mass spectrometry-based proteomics

Accurate and precise methods for estimating incorrect peptide and protein identifications are crucial for effective large-scale proteome analyses by tandem mass spectrometry. The target-decoy search strategy has emerged as a simple, effective tool for generating such estimations. This strategy is based on the premise that obvious, necessarily incorrect "decoy" sequences added to the search space will correspond with incorrect search results that might otherwise be deemed to be correct. With this knowledge, it is possible not only to estimate how many incorrect results are in a final data set but also to use decoy hits to guide the design of filtering criteria that sensitively partition a data set into correct and incorrect identifications.

Comprehensive and reliable phosphorylation site mapping of individual phosphoproteins by combination of multiple stage mass spectrometric analysis with a target-decoy database search

Since the emergence of proteomics, much attention has been paid to the development of new technologies for phosphoproteomcis analysis. Compared with large scale phosphorylation analysis at the proteome level, comprehensive and reliable phosphorylation site mapping of individual phosphoprotein is equally important. Here, we present a modified target-decoy database search strategy for confident phosphorylation site analysis of individual phosphoproteins without manual interpretation of spectra. Instead of using all protein sequences in a proteome database of an organism for the construction of a target-decoy database for phosphoproteome analysis, the composite database constructed for phosphorylation site analysis of individual phosphoproteins only included the sequences of the individual target proteins and a decoy version of a small inhomogeneous protein database. It was found that the confidence of phosphopeptide identifications could be effectively controlled when the acquired MS2 and MS3 spectra were searched against the above composite database followed with data processing. Because of the small size of the composite database, the computation time for the database search is very short, which allows the adoption of low-specificity proteases for protein digestion to increase the coverage of phosphorylation site mapping. The sensitivity and comprehensive phosphorylation site mapping of this approach was demonstrated by using two standard phosphoprotein samples of alpha-casein and beta-casein, and this approach was further applied to analyze the phosphorylation of the cyclic AMP-dependent protein kinase (PKA), which resulted in the identification of 17 phosphorylation sites, including five novel sites on four PKA subunits.

Optimization of the Use of Consensus Methods for the Detection and Putative Identification of Peptides via Mass Spectrometry Using Protein Standard Mixtures

Correct identification of peptides and proteins in complex biological samples from proteomic mass-spectra is a challenging problem in bioinformatics. The sensitivity and specificity of identification algorithms depend on underlying scoring methods, some being more sensitive, and others more specific. For high-throughput, automated peptide identification, control over the algorithms' performance in terms of trade-off between sensitivity and specificity is desirable. Combinations of algorithms, called 'consensus methods', have been shown to provide more accurate results than individual algorithms. However, due to the proliferation of algorithms and their varied internal settings, a systematic understanding of relative performance of individual and consensus methods are lacking. We performed an in-depth analysis of various approaches to consensus scoring using known protein mixtures, and evaluated the performance of 2310 settings generated from consensus of three different search algorithms: Mascot, Sequest, and X!Tandem. Our findings indicate that the union of Mascot, Sequest, and X!Tandem performed well (considering overall accuracy), and methods using 80-99.9% protein probability and/or minimum 2 peptides and/or 0-50% minimum peptide probability for protein identification performed better (on average) among all consensus methods tested in terms of overall accuracy. The results also suggest method selection strategies to provide direct control over sensitivity and specificity.

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.