Shotgun Proteomics Experiments Research Articles

MassSieve: Panning MS/MS peptide data for proteins

We present MassSieve, a Java-based platform for visualization and parsimony analysis of single and comparative LC-MS/MS database search engine results. The success of mass spectrometric peptide sequence assignment algorithms has led to the need for a tool to merge and evaluate the increasing data set sizes that result from LC-MS/MS-based shotgun proteomic experiments. MassSieve supports reports from multiple search engines with differing search characteristics, which can increase peptide sequence coverage and/or identify conflicting or ambiguous spectral assignments.

Development of tools for the automated analysis of spectra generated by tandem mass spectrometry

Background While multiple tools exist for the analysis and identification of spectra generated in shotgun proteomics experiments, few easily implemented tools exist that allow for the automated analysis of the quality of spectra. A researcher’s knowledge of the quality of a spectra from an experiment can be helpful in determining possible reasons for misidentification or lack of identification of spectra in a sample.

Computational analysis of unassigned high‐quality MS/MS spectra in proteomic data sets

In a typical shotgun proteomics experiment, a significant number of high-quality MS/MS spectra remain "unassigned." The main focus of this work is to improve our understanding of various sources of unassigned high-quality spectra. To achieve this, we designed an iterative computational approach for more efficient interrogation of MS/MS data. The method involves multiple stages of database searching with different search parameters, spectral library searching, blind searching for modified peptides, and genomic database searching. The method is applied to a large publicly available shotgun proteomic data set.

Assessment of Resolution Parameters for CID-Based Shotgun Proteomic Experiments on the LTQ-Orbitrap Mass Spectrometer

Shotgun proteomics has been used extensively for characterization of a number of proteomes. High-resolution Fourier transform mass spectrometry (FTMS) has emerged as a powerful tool owing to its high mass accuracy and resolving power. One of its major limitations, however, is that the confidence level of peptide identification and sensitivity cannot be maximized simultaneously. Although it is generally assumed that higher resolution is better for peptide identifications, the precise effect of varying resolution as a parameter on peptide identification has not yet been systematically evaluated. We used the Escherichia coli proteome and a standard 48 protein mix to study the effect of different resolution parameters on peptide identifications in the setting of a shotgun proteomics experiment on an LTQ-Orbitrap mass spectrometer. We observed a higher number of peptide-spectrum matches (PSMs) whenever the MS scan was carried out by FT and the MS/MS in the ion-trap (IT) with the maximum PSMs obtained at an MS resolution of 30,000. In contrast, when samples were analyzed by FT for both MS and MS/MS, the number of PSMs was significantly lower (approximately 40% compared with FT-IT experiments) with the maximum PSMs obtained when both the MS and MS/MS resolution were set to 15,000. Thus, a 15K-15K resolution setting may provide the best compromise for studies where both speed and accuracy such as high-throughput post-translational analysis and de novo sequencing are important. We hope that our study will allow researchers to choose between different resolution parameters to achieve their desired results from proteomic analyses.

Census for proteome quantification.

Quantitative analysis has become increasingly important in the proteomics field; however, the large amount of mass spectrometric data and the different types of quantitative strategies make data analysis ever challenging. Here we describe a quantitative software tool called Census to analyze high-throughput mass spectrometry data from shotgun proteomics experiments in an efficient way. Census is capable of analyzing various stable isotope labeling experiments (using, e.g., (15)N, (18)O, SILAC, iTRAQ, TMT) in addition to labeling-free experiments. With high-resolution data, Census increases the quantitative accuracy by minimizing the contributions of interfering peaks and chemical noise with a small accuracy tolerance for each isotope peak. Census provides various scoring algorithms including least-squares correlation, weight average, singleton peptide detection with discriminant analysis, and probability score for each peptide. Furthermore, Census has built-in multiple statistical filters to maintain robust quality control on quantitative results.

Quantitative Clinical Proteomics by Liquid Chromatography–Tandem Mass Spectrometry: Assessing the Platform

Clinical laboratories use electrospray ionization and mass spectrometry (MS)1 every day to measure dozens of different small molecule analytes. The capacity to precisely quantify these biomarkers has improved patient care. To build on the successful implementation of novel MS methodologies, many clinical mass spectrometrists hope to expand the list of measurable analytes to include peptides and proteins, several examples of which have been reported and reviewed (1). One can also envision the translation of novel biomarkers from the research-grade MS assays, with which they are discovered, to clinically acceptable MS assays for diagnosis, prognosis, and evaluation of therapeutic efficacy. An argument can be made that investigators should use clinically acceptable MS assays from the outset (2), which would facilitate the transition from research/discovery to clinical practice. Given the successes over the last few years in precisely measuring proteins by MS in clinically relevant matrices, this outcome may not be too much to ask. Traditionally, the word proteomics has encompassed efforts aimed at simultaneously measuring many proteins in a given complex sample. Shotgun proteomic experiments, which attempt to identify all of the proteins in a complex sample, and targeted proteomic experiments, which attempt to quantify a subset of the proteins known to exist in a complex sample, can both be carried out using liquid chromatography–tandem MS (LC-MS/MS). Shotgun proteomics is already being used clinically to identify the proteinaceous component of amyloid deposits (3). For the quantification of multiple protein concentrations in a complex sample, however, targeted methods are more precise because data-dependent shotgun proteomic methods variably sample fewer ions across chromatographic peaks. For this reason, targeted approaches are much more likely to be employed in the quantification of proteins in clinical specimens. Targeted methods typically use proteolysis with trypsin to digest proteins into peptides, which are more amenable to analysis …

Hunting down fungal secretomes using liquid‐phase IEF prior to high resolution 2‐DE

The secreted proteins (secretome) of fungi play a key role in interactions of pathogenic and symbiotic fungi with plants. Using the plant pathogenic fungus Leptosphaeria maculans and symbiont Laccaria bicolor grown in culture, we have established a proteomic protocol for extraction, concentration and resolution of the fungal secretome. As no proteomic data were available on mycelium tissues from both L. maculans and L. bicolor, mycelial proteins were studied; they also helped verifying the purity of secretome samples. The quality of protein extracts was initially assessed by both 1-DE and 2-DE using first a broad pH range for IEF, and then narrower acidic and basic pH ranges, prior to 2-DE. Compared with the previously published protocols for which only dozens of 2-D spots were recovered from fungal secretome samples, up to approximately 2000 2-D spots were resolved by our method. MS identification of proteins along several pH gradients confirmed this high resolution, as well as the presence of major secretome markers such as endopolygalacturonases, beta-glucanosyltransferases, pectate lyases and endoglucanases. Shotgun proteomic experiments evidenced the enrichment of secreted protein within the liquid medium. This is the first description of the proteome of L. maculans and L. bicolor, and the first application of liquid-phase IEF to any fungal extracts.

Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry

Comprehensive characterization of a proteome is a fundamental goal in proteomics. To achieve saturation coverage of a proteome or specific subproteome via tandem mass spectrometric identification of tryptic protein sample digests, proteomics data sets are growing dramatically in size and heterogeneity. The trend toward very large integrated data sets poses so far unsolved challenges to control the uncertainty of protein identifications going beyond well established confidence measures for peptide-spectrum matches. We present MAYU, a novel strategy that reliably estimates false discovery rates for protein identifications in large scale data sets. We validated and applied MAYU using various large proteomics data sets. The data show that the size of the data set has an important and previously underestimated impact on the reliability of protein identifications. We particularly found that protein false discovery rates are significantly elevated compared with those of peptide-spectrum matches. The function provided by MAYU is critical to control the quality of proteome data repositories and thereby to enhance any study relying on these data sources. The MAYU software is available as standalone software and also integrated into the Trans-Proteomic Pipeline.

Absolute quantification of Dehalococcoides proteins: enzyme bioindicators of chlorinated ethene dehalorespiration

The quantification of trace proteins in complex environmental samples and mixed microbial communities would be a valuable monitoring tool in countless applications, including the bioremediation of groundwater contaminated with chlorinated solvents. Measuring the concentrations of specific proteins provides unique information about the activity and physiological state of organisms in a sample. We developed sensitive (< 5 fmol), selective bioindicator assays for the absolute quantification of select proteins used by Dehalococcoides spp. when reducing carbon atoms in the common pollutants trichloroethene (TCE) and tetrachloroethene (PCE). From complex whole-sample digests of two different dechlorinating mixed communities, we monitored the chromatographic peaks of selected tryptic peptides chosen to represent 19 specific Dehalococcoides proteins. This was accomplished using multiple-reaction monitoring (MRM) assays using nano-liquid chromatography-tandem mass spectrometry (nLC-MS/MS), which provided the selectivity, sensitivity and reproducibility required to quantify Dehalococcoides proteins in complex samples. We observed reproducible peak areas (average CV = 0.14 over 4 days, n = 3) and linear responses in standard curves (n = 5, R(2) > 0.98) using synthetic peptide standards spiked into a background matrix of sediment peptides. We detected and quantified TCE reductive dehalogenase (TceA) at 7.6 +/- 1.7 x 10(3) proteins cell(-1) in the KB1 bioaugmentation culture, previously thought to be lacking TceA. Fragmentation data from MS/MS shotgun proteomics experiments were helpful in developing the MRM targets. Similar shotgun proteomics data are emerging in labs around the world for many environmentally relevant microbial proteins, and these data are a valuable resource for the future development of MRM assays. We expect targeted peptide quantification in environmental samples to be a useful tool in environmental monitoring.

Mining gene functional networks to improve mass-spectrometry-based protein identification

Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly.Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8–29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets.Availability and Implementation: Software and datasets are available at http://aug.csres.utexas.edu/msnetContact: miranker@cs.utexas.edu, marcotte@icmb.utexas.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Utility of mass spectrometry for proteome analysis: part II. Ion-activation methods, statistics, bioinformatics and annotation

This is the second article in a series, intended as a tutorial to provide the interested reader with an overview of the concepts not covered in part I, such as: the principles of ion-activation methods, the ability of mass-spectrometric methods to interface with various proteomic strategies, analysis techniques, bioinformatics and data interpretation and annotation. Although these are different topics, it is important that a reader has a basic and collective understanding of all of them for an overall appreciation of how to carry out and analyze a proteomic experiment. Different ion-activation methods for MS/MS, such as collision-induced dissociation (including postsource decay) and surface-induced dissociation, electron capture and electron-transfer dissociation, infrared multiphoton and blackbody infrared radiative dissociation have been discussed since they are used in proteomic research. The high dimensionality of data generated from proteomic studies requires an understanding of the underlying analytical procedures used to obtain these data, as well as the development of improved bioinformatics tools and data-mining approaches for efficient and accurate statistical analyses of biological samples from healthy and diseased individuals, in addition to determining the utility of the interpreted data. Currently available strategies for the analysis of the proteome by mass spectrometry, such as those employed for the analysis of substantially purified proteins and complex peptide mixtures, as well as hypothesis-driven strategies, have been elaborated upon. Processing steps prior to the analysis of mass spectrometry data, statistics and the several informatics steps currently used for the analysis of shotgun proteomic experiments, as well as proteomics ontology, are also discussed.

Integrating shotgun proteomics and mRNA expression data to improve protein identification.

Motivation: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration.Results: We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by ∼40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19–63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.Availability and Implementation: Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from http://www.marcottelab.org/MSpresso/.Contact: marcotte@icmb.utexas.edu; miranker@cs.utexas.eduSupplementary Information: Supplementary data website: http://www.marcottelab.org/MSpresso/.

The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation.

BackgroundCrucial foundations of any quantitative systems biology experiment are correct genome and proteome annotations. Protein databases compiled from high quality empirical protein identifications that are in turn based on correct gene models increase the correctness, sensitivity, and quantitative accuracy of systems biology genome-scale experiments.ResultsIn this manuscript, we present the Drosophila melanogaster PeptideAtlas, a fly proteomics and genomics resource of unsurpassed depth. Based on peptide mass spectrometry data collected in our laboratory the portal allows querying fly protein data observed with respect to gene model confirmation and splice site verification as well as for the identification of proteotypic peptides suited for targeted proteomics studies. Additionally, the database provides consensus mass spectra for observed peptides along with qualitative and quantitative information about the number of observations of a particular peptide and the sample(s) in which it was observed.ConclusionPeptideAtlas is an open access database for the Drosophila community that has several features and applications that support (1) reduction of the complexity inherently associated with performing targeted proteomic studies, (2) designing and accelerating shotgun proteomics experiments, (3) confirming or questioning gene models, and (4) adjusting gene models such that they are in line with observed Drosophila peptides. While the database consists of proteomic data it is not required that the user is a proteomics expert.

Prospects for monolithic nano-LC columns in shotgun proteomics

We report a premier side-by-side comparison of two leading types of monolithic nano-LC column (silica-C(18), polystyrene) in shotgun proteomics experiments. Besides comparing the columns in terms of the number of peptides from a real-life sample (Arabidopsis thaliana chloroplast) that they identified, we compared the monoliths in terms of peak capacity and retention behavior for standard peptides. For proteomics applications where the mobile phase composition is constrained by electrospray ionization considerations (i.e., there is a restricted choice of ion-pairing modifiers), the polystyrene nano-LC column exhibited reduced identification power. The silica monolith column was superior in all measured values and compared very favorably with traditional packed columns. Finally, we investigated the performances of the monoliths at high flow rates in an attempt to demonstrate their advantages for high-throughput identification.

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

BackgroundRejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches.ResultsA robust linear regression based algorithm was developed to automate the evaluation of peptide identifications obtained from shotgun proteomic experiments. The algorithm scores peptides based on their predicted and observed reversed-phase liquid chromatography retention times. The robust algorithm does not require internal or external peptide standards to train or calibrate the linear regression model used for peptide retention time prediction. The algorithm is generic and can be incorporated into any database search program to perform automated evaluation of the candidate peptide matches based on their retention times. It provides a statistical score for each peptide match based on its retention time.ConclusionAnalysis of peptide matches where the retention time score was included resulted in a significant reduction of false positive matches with little effect on the number of true positives. Overall higher sensitivities and specificities were achieved for database searches carried out with MassMatrix, Mascot and X!Tandem after implementation of the retention time based score algorithm.

SeMoP: A New Computational Strategy for the Unrestricted Search for Modified Peptides Using LC−MS/MS Data

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

8‐Plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease

An 8-plex version of an isobaric reagent for the quantitation of proteins using shotgun methods is presented. The 8-plex version of the reagent relies on amine-labeling chemistry of peptides similar to 4-plex reagents. MS/MS reporter ions at 113, 114, 115, 116, 117, 118, 119, and 121 m/z are used to quantify protein expression. This technology which was first applied to a test mixture consisting of eight proteins and resulted in accurate quantitation, has the potential to increase throughput of analysis for quantitative shotgun proteomics experiments when compared to 2- and 4-plex methods. The technology was subsequently applied to a longitudinal study of cerebrospinal fluid (CSF) proteins from subjects undergoing intravenous Ig treatment for Alzheimer's disease. Results from this study identify a number of protein expression changes that occur in CSF after 3 and 6 months of treatment compared to a baseline and compared to a drug washout period. A visualization tool was developed for this dataset and is presented. The tool can aid in the identification of key peptides and measurements. One conclusion aided by the visualization tool is that there are differences in considering peptide-based observations versus protein-based observations from quantitative shotgun proteomics studies.

Subcellular shotgun proteomics in plants: Looking beyond the usual suspects

In this review we examine the current state of analytical methods used for shotgun proteomics experiments in plants. The rapid advances in this field in recent years are discussed, and contrasted with experiments performed using current widely used procedures. We also examine the use of subcellular fractionation approaches as they apply to plant proteomics, and discuss how appropriate sample preparation can produce a great increase in proteome coverage in subsequent analysis. We conclude that the conjunction of these two techniques represents a significant advance in plant proteomics, and the future of plant biology research will continue to be enriched by the ongoing development of proteomic analytical technology.

High-Speed Data Reduction, Feature Detection, and MS/MS Spectrum Quality Assessment of Shotgun Proteomics Data Sets Using High-Resolution Mass Spectrometry

Advances in Fourier transform mass spectrometry have made the acquisition of high-resolution and accurate mass measurements routine on a chromatographic time scale. Here we report an algorithm, Hardklör, for the rapid and robust analysis of high-resolution mass spectra acquired in shotgun proteomics experiments. Our algorithm is demonstrated in the analysis of an Escherichia coli enriched membrane fraction. The mass spectrometry data of the respective peptides are acquired by microcapillary HPLC on an LTQ-orbitrap mass spectrometer with data-dependent acquisition of MS/MS spectra. Hardklör detects 211,272 total peptide isotope distributions over a 2-h analysis (75-min gradient) in only a small fraction of the time required to acquire the data. From these data there are 13,665 distinct, chromatographically persistent peptide isotope distributions. Hardklör is also used to assess the quality of the product ion spectra and finds that more than 11.2% of the MS/MS spectra are composed of fragment ions from multiple different molecular species. Additionally, a method is reported that enzymatically labels N-linked glycosylation sites on proteins, creating a unique isotope signature that can be detected with Hardklör. Using the protein invertase, Hardklör identifies 18O-labeled peptide isotope distributions of four glycosylation sites. The speed and robustness of the algorithm create a versatile tool that can be used in many different areas of mass spectrometry data analysis.

Development and validation of a spectral library searching method for peptide identification from MS/MS

A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same identifiable peptides by sequence database searching methods, which often are time-consuming and error-prone. A more precise and efficient method, in which previously observed and identified peptide MS/MS spectra are catalogued and condensed into searchable spectral libraries to allow new identifications by spectral matching, is seen as a promising alternative. To that end, an open-source, functionally complete, high-throughput and readily extensible MS/MS spectral searching tool, SpectraST, was developed. A high-quality spectral library was constructed by combining the high-confidence identifications of millions of spectra taken from various data repositories and searched using four sequence search engines. The resulting library consists of over 30,000 spectra for Saccharomyces cerevisiae. Using this library, SpectraST vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits. A unique advantage of SpectraST is its full integration into the popular Trans Proteomic Pipeline suite of software, which facilitates user adoption and provides important functionalities such as peptide and protein probability assignment, quantification, and data visualization. This method of spectral library searching is especially suited for targeted proteomics applications, offering superior performance to traditional sequence searching.