Multidimensional separations for protein/peptide analysis in the post-genomic era

Proteomics is the study of all or part of the protein complement of genes in an organism, often involving the analysis of complex protein/peptide samples. Such complex samples are beyond the separation capacity of 1-D separation techniques. This review describes several multidimensional separations for proteins and peptides. First, several variants of 2-D liquid chromatography (2DLC) are reviewed, including coupled size exclusion-reversed phase, ion exchange-reversed phase, and reversed phase-reversed phase chromatography. Second, we describe coupled liquid chromatography and capillary electrophoresis methods. Finally, a multidimensional protein identification technique (MudPIT) is explained in detail. Each of the described techniques has a much higher separation capacity than 1-D methods and can potentially be automated for high-throughput experiments. In particular, MudPIT takes advantage of both the high separation capacity of 2DLC and the powerful peptide characterization ability of tandem mass spectrometry to analyze complex protein samples. Additional applications and developments of multidimensional liquid separations for proteomics are expected in the future.     

Optimal selection of 2D reversed-phase–reversed-phase HPLC separation techniques in bottom-up proteomics

Evaluation of: Stephanowitz H, Lange S, Lang D et al. Improved two-dimensional reversed phase–reversed phase LC-MS/MS approach for identification of peptide–protein interactions. J. Proteome Res. 11(2), 1175–1183 (2011).

Recent developments in bottom-up proteomics have supplanted the use of gel-based approaches in favor of multidimensional chromatographic separations of peptide mixtures followed by mass spectrometry analysis. This trend is driven by the desire to eliminate labor-intensive in-gel digestion procedures and increase proteome coverage through better recovery of proteolytic fragments. Introduction of reversed-phase–reversed-phase 2D separation techniques is one of the major improvements that have made this possible. In this article, we review recent developments in 2D HPLC and highlight variations in reversed-phase HPLC separation selectivity that allow for superior peak capacity in peptide fractionation.     

Biochemical individuality reflected in chromatographic, electrophoretic and mass-spectrometric profiles

This review discusses the current trends in molecular profiling for the emerging systems biology applications. Historically, the methodological developments in separation science were coincident with the availability of new ionization techniques in mass spectrometry. Coupling miniaturized separation techniques with technologically-advanced MS instrumentation and the modern data processing capabilities are at the heart of current platforms for proteomics, glycomics and metabolomics. These are being featured here by the examples from quantitative proteomics, glycan mapping and metabolomic profiling of physiological fluids.     

Peptide identification by tandem mass spectrometry with alternate fragmentation modes

The high-throughput nature of proteomics mass spectrometry is enabled by a productive combination of data acquisition protocols and the computational tools used to interpret the resulting spectra. One of the key components in mainstream protocols is the generation of tandem mass (MS/MS) spectra by peptide fragmentation using collision induced dissociation, the approach currently used in the large majority of proteomics experiments to routinely identify hundreds to thousands of proteins from single mass spectrometry runs. Complementary to these, alternative peptide fragmentation methods such as electron capture/transfer dissociation and higher-energy collision dissociation have consistently achieved significant improvements in the identification of certain classes of peptides, proteins, and post-translational modifications. Recognizing these advantages, mass spectrometry instruments now conveniently support fine-tuned methods that automatically alternate between peptide fragmentation modes for either different types of peptides or for acquisition of multiple MS/MS spectra from each peptide. But although these developments have the potential to substantially improve peptide identification, their routine application requires corresponding adjustments to the software tools and procedures used for automated downstream processing. This review discusses the computational implications of alternative and alternate modes of MS/MS peptide fragmentation and addresses some practical aspects of using such protocols for identification of peptides and post-translational modifications.     

Capture and analysis of quantitative proteomic data

Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding MS scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.     

Mass spectrometry of protein modifications by reactive oxygen and nitrogen species

The modification of proteins by reactive oxygen and nitrogen species plays an important role in various biologic processes involving protein activation and inactivation, protein translocation and turnover during signal transduction, stress response, proliferation, and apoptosis. Recent advances in protein and peptide separation and mass spectrometry provide increasingly sophisticated tools for the quantitative analysis of such protein modifications, which are absolutely necessary for their correlation with biologic phenomena. The present review focuses specifically on the qualitative and quantitative mass spectrometric analysis of the most common protein modifications caused by reactive oxygen and nitrogen species in vivo and in vitro and details a case study on a membrane protein the sarco/endoplasmic reticulum Ca-ATPase (SERCA).     

串联质谱图谱从头测序算法研究进展

近年来,基于质谱技术的高通量蛋白质组学研究发展迅速,利用串联质谱图谱鉴定蛋白质是其数据处理中一个基础而又重要的环节．由于不需要利用蛋白质序列数据库,从头测序方法能够分析新物种或者基因组未测序物种的串联质谱数据,具有数据库搜索方法不可替代的优势．简要介绍高通量串联质谱图谱从头测序问题及其研究现状．归纳出几种典型的计算策略并分析了各种策略的优缺点．总结常用的从头测序算法和软件,介绍算法评估的各种指标和常用评估数据集,概括各种算法的特点,展望未来研究可能的发展方向．     

Optimization of Data-Dependent Acquisition Parameters for Coupling High-Speed Separations with LC-MS/MS for Protein Identifications

Recent developments in chromatography, such as ultra-HPLC and superficially porous particles, offer significantly improved peptide separation. The narrow peak widths, often only several seconds, can permit a 15-min liquid chromatography run to have a similar peak capacity as a 60-min run using traditional HPLC approaches. In theory, these larger peak capacities should provide higher protein coverage and/or more protein identifications when incorporated into a proteomic workflow. We initially observed a decrease in protein coverage when implementing these faster chromatographic approaches, due to data-dependent acquisition (DDA) settings that were not properly set to match the narrow peak widths resulting from newly implemented, fast separation techniques. Oversampling of high-intensity peptides lead to low protein-sequence coverage, and tandem mass spectra (MS/MS) from lower-intensity peptides were of poor quality, as automated MS/MS events were occurring late on chromatographic peaks. These observations led us to optimize DDA settings to use these fast separations. Optimized DDA settings were applied to the analysis of Trypanosome brucei peptides, yielding peptide identifications at a rate almost five times faster than previously used methodologies. The described approach significantly improves protein identification workflows that use typical available instrumentation.     

Protein and peptide fractionation, enrichment and depletion: tools for the complex proteome

The identification, quantitation and global characterisation of all proteins within a given proteome are extremely challenging. This is due to the absolute detection limits of technology as well as the dynamic range in expression of proteins; and the extreme diversity and heterogeneity of the proteome. To overcome such issues, the use of separation technologies has played a critical role in reducing sample complexity. To date, a plethora of chromatographic and electrophoretic fractionation tools have evolved over the years assisting in simplifying complex protein and peptide mixtures. Here, we review a range of these technologies highlighting the challenges of protein and peptide analysis in the context of proteome research and some of the advantages and disadvantages of present techniques.     

Proteomic reactors and their applications in biology

Proteomic analysis requires the combination of an extensive suite of technologies including protein processing and separation, micro-flow HPLC, MS and bioinformatics. Although proteomic technologies are still in flux, approaches that bypass gel electrophoresis (gel-free approaches) are dominating the field of proteomics. Along with the development of gel-free proteomics, came the development of devices for the processing of proteomic samples termed proteomic reactors. These microfluidic devices provide rapid, robust and efficient pre-MS sample procession by performing protein sample preparation/concentration, digestion and peptide fractionation. The proteomic reactor has advanced in two major directions: immobilized enzyme reactor and ion exchange-based proteomic reactor. This review summarizes the technical developments and biological applications of the proteomic reactor over the last decade.     

Analysing proteomic data

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.     

A computational pipeline for protein structure prediction and analysis at genome scale

MOTIVATION: Experimental techniques alone cannot keep up with the production rate of protein sequences, while computational techniques for protein structure predictions have matured to such a level to provide reliable structural characterization of proteins at large scale. Integration of multiple computational tools for protein structure prediction can complement experimental techniques. RESULTS: We present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is our threading-based protein structure prediction system PROSPECT. The pipeline consists of a dozen tools for identification of protein domains and signal peptide, protein triage to determine the protein type (membrane or globular), protein fold recognition, generation of atomic structural models, prediction result validation, etc. Different processing and prediction branches are determined automatically by a prediction pipeline manager based on identified characteristics of the protein. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. Genome-scale applications on Caenorhabditis elegans, Pyrococcus furiosus and three cyanobacterial genomes are presented. AVAILABILITY: The pipeline is available at http://compbio.ornl.gov/proteinpipeline/     

Proteomic analysis of rat atrial secretory granules: a platform for testable hypotheses

The recent development of powerful proteomic tools has enabled investigators to directly examine the population of proteins present in defined biological systems. We report here the first proteomic analysis of atrial secretory granules. Approximately 100 distinct protein components of the atrial secretory granule proteome were detected using subcellular fractionation and one-dimensional SDS-PAGE in conjunction with peptide mass fingerprinting by MALDI-TOF mass spectrometry. Of this number, 61 proteins were clearly identified by high probability data matches and repeated observation. The majority of the proteome was found to be membrane-associated with the most prominent proteins being peptidylglycine alpha-amidating monooxygenase (PAM) and pro-atrial natriuretic peptide (pro-ANP). This proteomic analysis of the rat atrium secretory granule produced an assembly of proteins with a diverse array of reported functions. The identified proteins fall into seven functional categories: (1) granular transport, docking and fusion; (2) signal transduction; (3) calcium-binding/calcium-dependent; (4) cellular architecture/chaperoning; (5) peptide/protein processing; (6) hormone; (7) proton transport. The novel finding of several protein processing enzymes and signal transduction proteins offer new perspectives on how pro-ANP is stored and processed to ANP during release. Accordingly, defining the proteome of the atrial secretory granule provides a framework for the development of new hypotheses that address key mechanisms governing granule function and ANP secretion.     

Use of models of biomacromolecule separation in AMT database generation for shotgun proteomics

Generation of a complex proteome database requires use of powerful analytical methods capable of following rapid changes in the proteome due to changing physiological and pathological states of the organism under study. One of the promising technologies with this regard is the use of so-called Accurate Mass and Time (AMT) tag peptide databases. Generation of an AMT database for a complex proteome requires combined efforts by many research groups and laboratories, but the chromatography data resulting from these efforts are tied to the particular experimental conditions and, in general, are not transferable from one platform to another. In this work, we consider an approach to solve this problem that is based on the generation of a universal scale for the chromatography data using a multiple-point normalization method. The method follows from the concept of linear correlation between chromatography data obtained over a wide range of separation parameters. The method is further tested for tryptic peptide mixtures with experimental data collected from mutual studies by different independent research groups using different separation protocols and mass spectrometry data processing tools.     

A cost-benefit analysis of multidimensional fractionation of affinity purification-mass spectrometry samples

Affinity purification coupled to mass spectrometry (AP-MS) is gaining widespread use for the identification of protein-protein interactions. It is unclear, however, whether typical AP sample complexity is limiting for the identification of all protein components using standard one-dimensional LC-MS/MS. Multidimensional sample separation is useful for reducing sample complexity prior to MS analysis and increases peptide and protein coverage of complex samples. Here, we monitored the effects of upstream protein or peptide separation techniques on typical mammalian AP-MS samples, generated by FLAG affinity purification of four baits with different biological functions and/or subcellular distribution. As a first separation step, we employed SDS-PAGE, strong cation exchange LC, or reversed-phase LC at basic pH. We also analyzed the benefits of using an instrument with a faster scan rate, the new TripleTOF 5600 mass spectrometer. While all multidimensional approaches yielded a clear increase in spectral counts, the increase in unique peptides and additional protein identification was modest and came at the cost of increased instrument and handling time. The use of a high duty-cycle instrument achieved similar benefits without these drawbacks. An increase in spectral counts is beneficial when data analysis methods relying on spectral counts, including Significance Analysis of INTeractome (SAINT), are used.     

Automated reprocessing pipeline for searching heterogeneous mass spectrometric data of the HUPO Brain Proteome Project pilot phase

The newly available techniques for sensitive proteome analysis and the resulting amount of data require a new bioinformatics focus on automatic methods for spectrum reprocessing and peptide/protein validation. Manual validation of results in such studies is not feasible and objective enough for quality relevant interpretation. The necessity for tools enabling an automatic quality control is, therefore, important to produce reliable and comparable data in such big consortia as the Human Proteome Organization Brain Proteome Project. Standards and well-defined processing pipelines are important for these consortia. We show a way for choosing the right database model, through collecting data, processing these with a decoy database and end up with a quality controlled protein list merged from several search engines, including a known false-positive rate.     

Analysis of recombinat glycoproteins by mass spectrometry

The advent of new technologies for analysis of biopolymers by mass spectrometry has revolutionised strategies for recombinant protein characterization. The principal recent developments have been matrix-assisted laser desorption/ionization and electrospray ionization mass spectrometry. Using these tools, accurate molecular mass determinations can now be obtained routinely-often using minute (picomole-femtomole) quantities of protein or protein fragments. These techniques have proved indispensible for detailed characterization of the post-translational modifications of recombinant proteins produced by eukaryotic systems. Glycosylation is arguably the most important and complex of these modifications and has prompted widespread use of these new techniques. In this mini-review article I describe recent advances in the use of mass spectrometry for analysis of recombinant glycoproteins.     

Biotechnologies in new high-throughput food allergy tests: why we need them

The increase in prevalence of food allergies generates a need for more accurate and reliable quantitative allergy testing in order to help diagnosis. In this short review, we briefly outline the history of food allergy testing and extend our comments to current multiplex techniques. Particular emphasis is given to new developments in the protein microarray area, where the use of recent advances in biotechnology has the potential to produce high-throughput devices with improved clinical significance.     

In vitro and in silico processes to identify differentially expressed proteins

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.