Integrating 'top-down" and "bottom-up" mass spectrometric approaches for proteomic analysis of Shewanella oneidensis

Here we present a comprehensive method for proteome analysis that integrates both intact protein measurement ("top-down") and proteolytic fragment characterization ("bottom-up") mass spectrometric approaches, capitalizing on the unique capabilities of each method. This integrated approach was applied in a preliminary proteomic analysis of Shewanella oneidensis, a metal-reducing microbe of potential importance to the field of bioremediation. Cellular lysates were examined directly by the "bottom-up" approach as well as fractionated via anion-exchange liquid chromatography for integrated studies. A portion of each fraction was proteolytically digested, with the resulting peptides characterized by on-line liquid chromatography/tandem mass spectrometry. The remaining portion of each fraction containing the intact proteins was examined by high-resolution Fourier transform mass spectrometry. This "top-down" technique provided direct measurement of the molecular masses for the intact proteins and thereby enabled confirmation of post-translational modifications, signal peptides, and gene start sites of proteins detected in the "bottom-up" experiments. A total of 868 proteins from virtually every functional class, including hypotheticals, were identified from this organism.     

Characterization of the 70S Ribosome from Rhodopseudomonas palustris using an integrated "top-down" and "bottom-up" mass spectrometric approach

We present a comprehensive mass spectrometric approach that integrates intact protein molecular mass measurement ("top-down") and proteolytic fragment identification ("bottom-up") to characterize the 70S ribosome from Rhodopseudomonas palustris. Forty-two intact protein identifications were obtained by the top-down approach and 53 out of the 54 orthologs to Escherichia coli ribosomal proteins were identified from bottom-up analysis. This integrated approach simplified the assignment of post-translational modifications by increasing the confidence of identifications, distinguishing between isoforms, and identifying the amino acid positions at which particular post-translational modifications occurred. Our combined mass spectrometry data also allowed us to check and validate the gene annotations for three ribosomal proteins predicted to possess extended C-termini. In particular, we identified a highly repetitive C-terminal "alanine tail" on L25. This type of low complexity sequence, common to eukaryotic proteins, has previously not been reported in prokaryotic proteins. To our knowledge, this is the most comprehensive protein complex analysis to date that integrates two MS techniques.     

Intact glycopeptide characterization using mass spectrometry

Glycosylation is one of the most prominent and extensively studied protein post-translational modifications. However, traditional proteomic studies at the peptide level (bottom-up) rarely characterize intact glycopeptides (glycosylated peptides without removing glycans), so no glycoprotein heterogeneity information is retained. Intact glycopeptide characterization, on the other hand, provides opportunities to simultaneously elucidate the glycan structure and the glycosylation site needed to reveal the actual biological function of protein glycosylation. Recently, significant improvements have been made in the characterization of intact glycopeptides, ranging from enrichment and separation, mass spectroscopy (MS) detection, to bioinformatics analysis. In this review, we recapitulated currently available intact glycopeptide characterization methods with respect to their advantages and limitations as well as their potential applications.     

Identification of protein fragments as pattern features in MALDI-MS analyses of serum

The use of matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) to acquire spectral profiles has become a common approach to detect proteomic biomarkers of disease. MALDI-MS signals may represent both intact proteins as well as proteolysis products. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis can tentatively identify the corresponding proteins Here, we describe the application of a data analysis utility called FragMint, which combines MALDI-MS spectral data with LC-MS/MS based protein identifications to generate candidate protein fragments consistent with both types of data. This approach was used to identify protein fragments corresponding to spectral signals in MALDI-MS analyses of unfractionated human serum. The serum also was analyzed by one-dimensional SDS-PAGE and bands corresponding to the MALDI-MS signal masses were excised and subjected to in-gel digestion and LC-MS/MS analysis. Database searches mapped all of the identified peptides to abundant blood proteins larger than the observed MALDI-MS signals. FragMint identified fragments of these proteins that contained the MS/MS identified sequences and were consistent with the observed MALDI-MS signals. This approach should be generally applicable to identify protein species corresponding to MALDI-MS signals.     

"Top Down" characterization is a complementary technique to peptide sequencing for identifying protein species in complex mixtures

At present, mass spectrometry provides a rapid and sensitive means for making conclusive protein identifications from complex mixtures. Sequencing tryptic peptides derived from proteolyzed protein samples, also known as the "Bottom Up" approach, is the mass spectrometric gold standard for identifying unknowns. An alternative technology, "Top Down" characterization, is emerging as a viable option for protein identifications, which involves analyzing the intact unknowns for accurate mass and amino acid sequence tags. In this paper, both characterization methods were employed to more comprehensively differentiate two early-eluting peaks in a process-scale size-exclusion chromatography (SEC) step for a recombinant, immunoglobulin gamma-1 (IgG-1) fusion protein. The contents of each SEC peak were enzymatically digested, and the resulting peptides were mapped using reversed-phase (RP) HPLC-ion trap MS. Many low-level UV signals were observed among the fusion protein-related peptide peaks. These unknowns were collected, concentrated, and analyzed using nanoelectrospray (nanoES) collision-induced dissociation (CID) tandem (MS/MS) mass spectrometry for identification. The peptide sequencing experiments resulted in the identification of twenty host cell-related proteins. Following peptide mapping, the contents of the two SEC peaks were protein mass profiled using on-line RP HPLC coupled to a high-resolution, quadrupole time-of-flight (Qq/TOF) MS. Unknown proteins were also collected, concentrated, and dissociated using nanoES CID MS/MS. Intact protein CID experiments and accurate molecular weight information allowed for the identification of three full length host cell-derived proteins and numerous clips from these and additional proteins. The accurate molecular weight values allowed for the assignment of N- and C-terminal processing, which is difficult to conclusively access from peptide mapping data. The peptide-mapping experiments proved to be far more effective for making protein identifications from complex mixtures, whereas the protein mass profiling was useful for assessing modifications and distinguishing protein clips from full length species.     

Scientific workflow management in proteomics

Data processing in proteomics can be a challenging endeavor, requiring extensive knowledge of many different software packages, all with different algorithms, data format requirements, and user interfaces. In this article we describe the integration of a number of existing programs and tools in Taverna Workbench, a scientific workflow manager currently being developed in the bioinformatics community. We demonstrate how a workflow manager provides a single, visually clear and intuitive interface to complex data analysis tasks in proteomics, from raw mass spectrometry data to protein identifications and beyond.     

Proteomic profile of reversible protein oxidation using PROP, purification of reversibly oxidized proteins

Signal transduction pathways that are modulated by thiol oxidation events are beginning to be uncovered, but these discoveries are limited by the availability of relatively few analytical methods to examine protein oxidation compared to other signaling events such as protein phosphorylation. We report here the coupling of PROP, a method to purify reversibly oxidized proteins, with the proteomic identification of the purified mixture using mass spectrometry. A gene ontology (GO), KEGG enrichment and Wikipathways analysis of the identified proteins indicated a significant enrichment in proteins associated with both translation and mRNA splicing. This methodology also enabled the identification of some of the specific cysteine residue targets within identified proteins that are reversibly oxidized by hydrogen peroxide treatment of intact cells. From these identifications, we determined a potential consensus sequence motif associated with oxidized cysteine residues. Furthermore, because we identified proteins and specific sites of oxidation from both abundant proteins and from far less abundant signaling proteins (e.g. hepatoma derived growth factor, prostaglandin E synthase 3), the results suggest that the PROP procedure was efficient. Thus, this PROP-proteomics methodology offers a sensitive means to identify biologically relevant redox signaling events that occur within intact cells.     

Proteomic profiling of intact proteins using WAX-RPLC 2-D separations and FTICR mass spectrometry

We investigated the combination of weak anion exchange (WAX) fractionation and on-line reversed-phase liquid chromatography (RPLC) separation using a 12 T FTICR mass spectrometer for the detection of intact proteins from a Shewanella oneidensis MR-1 cell lysate. This work aimed at optimizing intact protein detection for profiling proteins at a level that incorporates their modification state. A total of 715 intact proteins were detected, and the combined results from the WAX fractions and the unfractionated cell lysate were aligned using LC-MS features to facilitate protein abundance measurements. Protein identifications and post-translational modifications were assigned for approximately 10% of the detected proteins by comparing intact protein mass measurements to proteins identified in peptide MS/MS analysis of an aliquot of the same fraction. Intact proteins were also detected for S. oneidensis lysates obtained from cells grown on 13C-, 15N-depleted media under aerobic and sub-oxic conditions. The strategy can be readily applied for measuring differential protein abundances and provides a platform for high-throughput selection of biologically relevant targets for further characterization.     

Protein identification and Peptide expression resolver: harmonizing protein identification with protein expression data

Proteomic discovery platforms generate both peptide expression information and protein identification information. Peptide expression data are used to determine which peptides are differentially expressed between study cohorts, and then these peptides are targeted for protein identification. In this paper, we demonstrate that peptide expression information is also a powerful tool for enhancing confidence in protein identification results. Specifically, we evaluate the following hypothesis: tryptic peptides originating from the same protein have similar expression profiles across samples in the discovery study. Evidence supporting this hypothesis is provided. This hypothesis is integrated into a protein identification tool, PIPER (Protein Identification and Peptide Expression Resolver), that reduces erroneous protein identifications below 5%. PIPER's utility is illustrated by application to a 72-sample biomarker discovery study where it is demonstrated that false positive protein identifications can be reduced below 5%. Consequently, it is recommended that PIPER methodology be incorporated into proteomic studies where both protein expression and identification data are collected.     

The utility of proteases in proteomics,from sequence profiling to structure and function analysis

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.     

Automated reporting from gel-based proteomics experiments using the open source Proteios database application

The assembly of data from different parts of proteomics workflow is often a major bottleneck in proteomics. Furthermore, there is an increasing demand for the publication of details about protein identifications due to the problems with false-positive and false-negative identifications. In this report, we describe how the open-source Proteios software has been expanded to automate the assembly of the different parts of a gel-based proteomics workflow. In Proteios it is possible to generate protein identification reports that contain all the information currently required by proteomics journals. It is also possible for the user to specify maximum allowed false positive ratios, and reports are automatically generated with the corresponding score cut-offs calculated. When protein identification is conducted using multiple search engines, the score thresholds that correlate to the predetermined error rate are also explicitly calculated for proteins that appear on the result lists of more than one search engine.     

2DB: a Proteomics database for storage,analysis, presentation,and retrieval of information from mass spectrometric experiments

Background  

The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed.     

Verification of single-peptide protein identifications by the application of complementary database search algorithms.

Data produced from the MudPIT analysis of yeast (S. cerevisiae) and rice (O. sativa) were used to develop a technique to validate single-peptide protein identifications using complementary database search algorithms. This results in a considerable reduction of overall false-positive rates for protein identifications; the overall false discovery rates in yeast are reduced from near 25% to less than 1%, and the false discovery rate of yeast single-peptide protein identifications becomes negligible. This technique can be employed by laboratories utilizing a SEQUEST-based proteomic analysis platform, incorporating the XTandem algorithm as a complementary tool for verification of single-peptide protein identifications. We have achieved this using open-source software, including several data-manipulation software tools developed in our laboratory, which are freely available to download.     

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 βA4 was found in human lens. The minor crystallin γN was detected for the first time in bovine and chicken lenses. Chicken γS was identified and is the first member of the γ-crystallin family observed in avian lenses.     

RT-SVR+q: a strategy for post-Mascot analysis using retention time and q value metric to improve peptide and protein identifications

Shotgun proteomics commonly utilizes database search like Mascot to identify proteins from tandem MS/MS spectra. False discovery rate (FDR) is often used to assess the confidence of peptide identifications. However, a widely accepted FDR of 1% sacrifices the sensitivity of peptide identification while improving the accuracy. This article details a machine learning approach combining retention time based support vector regressor (RT-SVR) with q value based statistical analysis to improve peptide and protein identifications with high sensitivity and accuracy. The use of confident peptide identifications as training examples and careful feature selection ensures high R values (>0.900) for all models. The application of RT-SVR model on Mascot results (p=0.10) increases the sensitivity of peptide identifications. q Value, as a function of deviation between predicted and experimental RTs (ΔRT), is used to assess the significance of peptide identifications. We demonstrate that the peptide and protein identifications increase by up to 89.4% and 83.5%, respectively, for a specified q value of 0.01 when applying the method to proteomic analysis of the natural killer leukemia cell line (NKL). This study establishes an effective methodology and provides a platform for profiling confident proteomes in more relevant species as well as a future investigation of accurate protein quantification.     

Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells

In this study, we evaluated a concatenated low pH (pH 3) and high pH (pH 10) reversed-phase liquid chromatography strategy as a first dimension for two-dimensional liquid chromatography tandem mass spectrometry ("shotgun") proteomic analysis of trypsin-digested human MCF10A cell sample. Compared with the more traditional strong cation exchange method, the use of concatenated high pH reversed-phase liquid chromatography as a first-dimension fractionation strategy resulted in 1.8- and 1.6-fold increases in the number of peptide and protein identifications (with two or more unique peptides), respectively. In addition to broader identifications, advantages of the concatenated high pH fractionation approach include improved protein sequence coverage, simplified sample processing, and reduced sample losses. The results demonstrate that the concatenated high pH reversed-phased strategy is an attractive alternative to strong cation exchange for two-dimensional shotgun proteomic analysis.