Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry

Shotgun proteome analysis platforms based on multidimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) provide a powerful means to discover biomarker candidates in tissue specimens. Analysis platforms must balance sensitivity for peptide detection, reproducibility of detected peptide inventories and analytical throughput for protein amounts commonly present in tissue biospecimens (< 100 microg), such that platform stability is sufficient to detect modest changes in complex proteomes. We compared shotgun proteomics platforms by analyzing tryptic digests of whole cell and tissue proteomes using strong cation exchange (SCX) and isoelectric focusing (IEF) separations of peptides prior to LC-MS/MS analysis on a LTQ-Orbitrap hybrid instrument. IEF separations provided superior reproducibility and resolution for peptide fractionation from samples corresponding to both large (100 microg) and small (10 microg) protein inputs. SCX generated more peptide and protein identifications than did IEF with small (10 microg) samples, whereas the two platforms yielded similar numbers of identifications with large (100 microg) samples. In nine replicate analyses of tryptic peptides from 50 microg colon adenocarcinoma protein, overlap in protein detection by the two platforms was 77% of all proteins detected by both methods combined. IEF more quickly approached maximal detection, with 90% of IEF-detectable medium abundance proteins (those detected with a total of 3-4 peptides) detected within three replicate analyses. In contrast, the SCX platform required six replicates to detect 90% of SCX-detectable medium abundance proteins. High reproducibility and efficient resolution of IEF peptide separations make the IEF platform superior to the SCX platform for biomarker discovery via shotgun proteomic analyses of tissue specimens.     

Computational prediction of proteotypic peptides for quantitative proteomics

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).     

High throughput proteome screening for biomarker detection

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Current methods, while highly developed and powerful, are falling short of their goal of routinely analyzing whole proteomes mainly because the wealth of proteomic information accumulated from prior studies is not used for the planning or interpretation of present experiments. The consequence of this situation is that in every proteomic experiment the proteome is rediscovered. In this report we describe an approach for quantitative proteomics that builds on the extensive prior knowledge of proteomes and a platform for the implementation of the method. The method is based on the selection and chemical synthesis of isotopically labeled reference peptides that uniquely identify a particular protein and the addition of a panel of such peptides to the sample mixture consisting of tryptic peptides from the proteome in question. The platform consists of a peptide separation module for the generation of ordered peptide arrays from the combined peptide sample on the sample plate of a MALDI mass spectrometer, a high throughput MALDI-TOF/TOF mass spectrometer, and a suite of software tools for the selective analysis of the targeted peptides and the interpretation of the results. Applying the method to the analysis of the human blood serum proteome we demonstrate the feasibility of using mass spectrometry-based proteomics as a high throughput screening technology for the detection and quantification of targeted proteins in a complex system.     

VEMS 3.0: algorithms and computational tools for tandem mass spectrometry based identification of post-translational modifications in proteins

Protein and peptide mass analysis and amino acid sequencing by mass spectrometry is widely used for identification and annotation of post-translational modifications (PTMs) in proteins. Modification-specific mass increments, neutral losses or diagnostic fragment ions in peptide mass spectra provide direct evidence for the presence of post-translational modifications, such as phosphorylation, acetylation, methylation or glycosylation. However, the commonly used database search engines are not always practical for exhaustive searches for multiple modifications and concomitant missed proteolytic cleavage sites in large-scale proteomic datasets, since the search space is dramatically expanded. We present a formal definition of the problem of searching databases with tandem mass spectra of peptides that are partially (sub-stoichiometrically) modified. In addition, an improved search algorithm and peptide scoring scheme that includes modification specific ion information from MS/MS spectra was implemented and tested using the Virtual Expert Mass Spectrometrist (VEMS) software. A set of 2825 peptide MS/MS spectra were searched with 16 variable modifications and 6 missed cleavages. The scoring scheme returned a large set of post-translationally modified peptides including precise information on modification type and position. The scoring scheme was able to extract and distinguish the near-isobaric modifications of trimethylation and acetylation of lysine residues based on the presence and absence of diagnostic neutral losses and immonium ions. In addition, the VEMS software contains a range of new features for analysis of mass spectrometry data obtained in large-scale proteomic experiments. Windows binaries are available at http://www.yass.sdu.dk/.     

The program Proteocat as a tool for planning of proteomic experiments

ProteoCat is a computer program that has been designed to help researchers in the planning of large-scale proteomic experiments. The central part of this program is the unit of hydrolysis simulation that supports 4 proteases (trypsin, lysine C, endoproteinases Asp-N and GluC). For peptides obtained after virtual hydrolysis or loaded from data files a number of properties important in mass-spectrometric experiments can be calculated and predicted; the resultant data can be analyzed or filtered (to reduce a set of peptides). The program is using new and improved modifications of own earlier developed methods for pI prediction, which can be also predicted by means of popular pKa scales proposed by other reseachers. The algorithm for prediction of peptide retention time has been realized similarly to the algorithm used in the SSRCalc program. Using ProteoCat it is possible to estimate the coverage of amino acid sequences of analyzed proteins under defined limitation on peptides detection, as well as the possibility of assembly of peptide fragments with user-defined minimal sizes of “sticky” ends. The program has a graphical user interface, written on JAVA and available at http://www.ibmc.msk.ru/LPCIT/ProteoCat.     

VennDIS: A JavaFX‐based Venn and Euler diagram software to generate publication quality figures

Venn diagrams are graphical representations of the relationships among multiple sets of objects and are often used to illustrate similarities and differences among genomic and proteomic datasets. All currently existing tools for producing Venn diagrams evince one of two traits; they require expertise in specific statistical software packages (such as R), or lack the flexibility required to produce publication‐quality figures. We describe a simple tool that addresses both shortcomings, Venn Diagram Interactive Software (VennDIS), a JavaFX‐based solution for producing highly customizable, publication‐quality Venn, and Euler diagrams of up to five sets. The strengths of VennDIS are its simple graphical user interface and its large array of customization options, including the ability to modify attributes such as font, style and position of the labels, background color, size of the circle/ellipse, and outline color. It is platform independent and provides real‐time visualization of figure modifications. The created figures can be saved as XML files for future modification or exported as high‐resolution images for direct use in publications.     

Fiducial-less alignment of cryo-sections

Cryo-electron tomography of vitreous sections is currently the most promising technique for visualizing arbitrary regions of eukaryotic cells or tissue at molecular resolution. Despite significant progress in the sample preparation techniques over the past few years, the three dimensional reconstruction using electron tomography is not as simple as in plunge frozen samples for various reasons, but mainly due to the effects of irradiation on the sections and the resulting poor alignment. Here, we present a new algorithm, which can provide a useful three-dimensional marker model after investigation of hundreds to thousands of observations calculated using local cross-correlation throughout the tilt series. The observations are chosen according to their coherence to a particular model and assigned to virtual markers. Through this type of measurement a merit figure can be calculated, precisely estimating the quality of the reconstruction. The merit figures of this alignment method are comparable to those obtained with plunge frozen samples using fiducial gold markers. An additional advantage of the algorithm is the implicit detection of areas in the sections that behave as rigid bodies and can thus be properly reconstructed.     

Reprint of "Fiducial-less alignment of cryo-sections" [J. Struct. Biol. 159 (2007) 413-423

Cryo-electron tomography of vitreous sections is currently the most promising technique for visualizing arbitrary regions of eukaryotic cells or tissue at molecular resolution. Despite significant progress in the sample preparation techniques over the past few years, the three dimensional reconstruction using electron tomography is not as simple as in plunge frozen samples for various reasons, but mainly due to the effects of irradiation on the sections and the resulting poor alignment. Here, we present a new algorithm, which can provide a useful three-dimensional marker model after investigation of hundreds to thousands of observations calculated using local cross-correlation throughout the tilt series. The observations are chosen according to their coherence to a particular model and assigned to virtual markers. Through this type of measurement a merit figure can be calculated, precisely estimating the quality of the reconstruction. The merit figures of this alignment method are comparable to those obtained with plunge frozen samples using fiducial gold markers. An additional advantage of the algorithm is the implicit detection of areas in the sections that behave as rigid bodies and can thus be properly reconstructed.     

A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry

There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics.     

Multi-Q: a fully automated tool for multiplexed protein quantitation

The iTRAQ labeling method combined with shotgun proteomic techniques represents a new dimension in multiplexed quantitation for relative protein expression measurement in different cell states. To expedite the analysis of vast amounts of spectral data, we present a fully automated software package, called Multi-Q, for multiplexed iTRAQ-based quantitation in protein profiling. Multi-Q is designed as a generic platform that can accommodate various input data formats from search engines and mass spectrometer manufacturers. To calculate peptide ratios, the software automatically processes iTRAQ's signature peaks, including peak detection, background subtraction, isotope correction, and normalization to remove systematic errors. Furthermore, Multi-Q allows users to define their own data-filtering thresholds based on semiempirical values or statistical models so that the computed results of fold changes in peptide ratios are statistically significant. This feature facilitates the use of Multi-Q with various instrument types with different dynamic ranges, which is an important aspect of iTRAQ analysis. The performance of Multi-Q is evaluated with a mixture of 10 standard proteins and human Jurkat T cells. The results are consistent with expected protein ratios and thus demonstrate the high accuracy, full automation, and high-throughput capability of Multi-Q as a large-scale quantitation proteomics tool. These features allow rapid interpretation of output from large proteomic datasets without the need for manual validation. Executable Multi-Q files are available on Windows platform at http://ms.iis.sinica.edu.tw/Multi-Q/.     

Peptidomics-based discovery of novel neuropeptides

Modern proteomic methodologies have significantly improved the possibilities of large-scale identification of proteins. However, these methodologies are limited by their inability to reliably detect endogenously expressed peptides. We describe a novel approach of combining sample preparation, comprising focused microwave irradiation and mass spectrometric peptide profiling that has enabled us to simultaneously detect more than 550 endogenous neuropeptides in 1 mg of hypothalamic extracts. Automatic switching tandem mass spectrometry and amino acid sequence determination of the peptides showed that they consist of both novel and previously described neuropeptides. The methodology includes virtual visualization of the peptides as two- and three-dimensional image maps. In addition, several novel and known post-translational modifications of the neuropeptides were identified. The peptidomic approach proved to be a powerful method for investigating endogenous peptides and their post-translational modifications in complex tissues such as the brain. It is anticipated that this approach will complement proteomic methods in the future.     

Utility of characteristic QTOF MS/MS fragmentation for MHC class I peptides

Systematic investigation of cellular process by mass spectrometric detection of peptides obtained from proteins digestion or directly from immuno-purification can be a powerful tool when used appropriately. The true sequence of these peptides is defined by the interpretation of spectral data using a variety of available algorithms. However peptide match algorithm scoring is typically based on some, but not all, of the mechanisms of peptide fragmentation. Although algorithm rules for soft ionization techniques generally fit very well to tryptic peptides, manual validation of spectra is often required for endogenous peptides such as MHC class I molecules where traditional trypsin digest techniques are not used. This study summarizes data mining and manual validation of hundreds of peptide sequences from MHC class I molecules in publically available data files. We herein describe several important features to improve and quantify manual validation for these endogenous peptides--post automated algorithm searching. Important fragmentation patterns are discussed for the studied MHC Class I peptides. These findings lead to practical rules that are helpful when performing manual validation. Furthermore, these observations may be useful to improve current peptide search algorithms or development of novel software tools.     

The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.     

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis,and the application of statistical tools for data analysis and interpretation

Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive, and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope-coded affinity tag (ICAT) approach to quantitative protein profiling, in this case proteins that copurified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry. Individual tandem mass spectrometry spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze, and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.     

The utility of ETD mass spectrometry in proteomic analysis

Mass spectrometry has played an integral role in the identification of proteins and their post-translational modifications (PTM). However, analysis of some PTMs, such as phosphorylation, sulfonation, and glycosylation, is difficult with collision-activated dissociation (CAD) since the modification is labile and preferentially lost over peptide backbone fragmentation, resulting in little to no peptide sequence information. The presence of multiple basic residues also makes peptides exceptionally difficult to sequence by conventional CAD mass spectrometry. Here we review the utility of electron transfer dissociation (ETD) mass spectrometry for sequence analysis of post-translationally modified and/or highly basic peptides. Phosphorylated, sulfonated, glycosylated, nitrosylated, disulfide bonded, methylated, acetylated, and highly basic peptides have been analyzed by CAD and ETD mass spectrometry. CAD fragmentation typically produced spectra showing limited peptide backbone fragmentation. However, when these peptides were fragmented using ETD, peptide backbone fragmentation produced a complete or almost complete series of ions and thus extensive peptide sequence information. In addition, labile PTMs remained intact. These examples illustrate the utility of ETD as an advantageous tool in proteomic research by readily identifying peptides resistant to analysis by CAD. A further benefit is the ability to analyze larger, non-tryptic peptides, allowing for the detection of multiple PTMs within the context of one another.     

Detecting protein sequence conservation via metric embeddings

MOTIVATION: Comparing two protein databases is a fundamental task in biosequence annotation. Given two databases, one must find all pairs of proteins that align with high score under a biologically meaningful substitution score matrix, such as a BLOSUM matrix (Henikoff and Henikoff, 1992). Distance-based approaches to this problem map each peptide in the database to a point in a metric space, such that peptides aligning with higher scores are mapped to closer points. Many techniques exist to discover close pairs of points in a metric space efficiently, but the challenge in applying this work to proteomic comparison is to find a distance mapping that accurately encodes all the distinctions among residue pairs made by a proteomic score matrix. Buhler (2002) proposed one such mapping but found that it led to a relatively inefficient algorithm for protein-protein comparison. RESULTS: This work proposes a new distance mapping for peptides under the BLOSUM matrices that permits more efficient similarity search. We first propose a new distance function on peptides derived from a given score matrix. We then show how to map peptides to bit vectors such that the distance between any two peptides is closely approximated by the Hamming distance (i.e. number of mismatches) between their corresponding bit vectors. We combine these two results with the LSH-ALL-PAIRS-SIM algorithm of Buhler (2002) to produce an improved distance-based algorithm for proteomic comparison. An initial implementation of the improved algorithm exhibits sensitivity within 5% of that of the original LSH-ALL-PAIRS-SIM, while running up to eight times faster.     

Multidimensional proteomic analysis of the soluble subproteome of the emerging nosocomial pathogen Ochrobactrum anthropi

We report the first large-scale gel-free proteomic analysis of the soluble subproteome of the emerging pathogen Ochrobactrum anthropi. Utilizing our robust offline multidimensional protein identification protocol, a total of 57 280 peptides were initially identified utilizing automated MS/MS analysis software. We describe our investigation of the heuristic protein validation tool PROVALT and demonstrate its ability to increase the speed and accuracy of the curation process of large-scale proteomic datasets. PROVALT reduced our peptide list to 8517 identified peptides and further manual curation of these peptides led to a final list of 984 uniquely identified peptides that resulted in the positive identification of 249 proteins. These identified proteins were functionally classified and physiochemically characterized. A variety of typical "housekeeping" functions identified within the proteome included nucleic acid, amino and fatty acid anabolism and catabolism, glycolysis, TCA cycle, and pyruvate and selenoamino acid metabolism. In addition, a number of potential virulence factors of relevance to both plant and human disease were identified.     

Public proteomic MS repositories and pipelines: available tools and biological applications

As proteomic MS has increased in throughput, so has the demand to catalogue the increasing number of peptides and proteins observed by the community using this technique. As in other 'omics' fields, this brings obvious scientific benefits such as sharing of results and prevention of unnecessary repetition, but also provides technical insights, such as the ability to compare proteome coverage between different laboratories, or between different proteomic platforms. Journals are also moving towards mandating that proteomics data be submitted to public repositories upon publication. In response to these demands, several web-based repositories have been established to store protein and peptide identifications derived from MS data, and a similar number of peptide identification software pipelines have emerged to deliver identifications to these repositories. This paper reviews the latest developments in public domain peptide and protein identification databases and describes the analysis pipelines that feed them. Recent applications of the tools to pertinent biological problems are examined, and through comparing and contrasting the capabilities of each system, the issues facing research users of web-based repositories are explored. Future developments and mechanisms to enhance system functionality and user-interfacing opportunities are also suggested.     

Practical 4'-phosphopantetheine active site discovery from proteomic samples

Polyketide and nonribosomal peptides constitute important classes of small molecule natural products. Due to the proven biological activities of these compounds, novel methods for discovery and study of the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) enzymes responsible for their production remains an area of intense interest, and proteomic approaches represent a relatively unexplored avenue. While these enzymes may be distinguished from the proteomic milieu by their use of the 4'-phosphopantetheine (PPant) post-translational modification, proteomic detection of PPant peptides is hindered by their low abundance and labile nature which leaves them unassigned using traditional database searching. Here we address key experimental and computational challenges to facilitate practical discovery of this important post-translational modification during shotgun proteomics analysis using low-resolution ion-trap mass spectrometers. Activity-based enrichment maximizes MS input of PKS/NRPS peptides, while targeted fragmentation detects putative PPant active sites. An improved data analysis pipeline allows experimental identification and validation of these PPant peptides directly from MS2 data. Finally, a machine learning approach is developed to directly detect PPant peptides from only MS2 fragmentation data. By providing new methods for analysis of an often cryptic post-translational modification, these methods represent a first step toward the study of natural product biosynthesis in proteomic settings.     

Improved detection of hydrophilic phosphopeptides using graphite powder microcolumns and mass spectrometry: evidence for in vivo doubly phosphorylated dynamin I and dynamin III

A common strategy in proteomics to improve the number and quality of peptides detected by mass spectrometry (MS) is to desalt and concentrate proteolytic digests using reversed phase (RP) chromatography prior to analysis. However, this does not allow for detection of small or hydrophilic peptides, or peptides altered in hydrophilicity such as phosphopeptides. We used microcolumns to compare the ability of RP resin or graphite powder to retain phosphopeptides. A number of standard phosphopeptides and a biologically relevant phosphoprotein, dynamin I, were analyzed. MS revealed that some phosphopeptides did not bind the RP resin but were retained efficiently on the graphite. Those that did bind the RP resin often produced much stronger signals from the graphite powder. In particular, the method revealed a doubly phosphorylated peptide in a tryptic digest of dynamin I purified from rat brain nerve terminals. The detection of this peptide was greatly enhanced by graphite micropurification. Sequencing by tandem MS confirmed the presence of phosphate at both Ser-774 and Ser-778, while a singly phosphorylated peptide was predominantly phosphorylated only on Ser-774. The method further revealed a singly and doubly phosphorylated peptide in dynamin III, analogous to the dynamin I sequence. A pair of dynamin III phosphorylation sites were found at Ser-759 and Ser-763 by tandem MS. The results directly define the in vivo phosphorylation sites in dynamins I and III for the first time. The findings indicate a large improvement in the detection of small amounts of phosphopeptides by MS and the approach has major implications for both small- and large-scale projects in phosphoproteomics.