Software for analysing ion mobility mass spectrometry data to improve peptide identification

The development of ion mobility (IM) MS instruments has the capability to provide an added dimension to peptide analysis pipelines in proteomics, but, as yet, there are few software tools available for analysing such data. IM can be used to provide additional separation of parent ions or product ions following fragmentation. In this work, we have created a set of software tools that are capable of converting three dimensional IM data generated from analysis of fragment ions into a variety of formats used in proteomics. We demonstrate that IM can be used to calculate the charge state of a fragment ion, demonstrating the potential to improve peptide identification by excluding non-informative ions from a database search. We also provide preliminary evidence of structural differences between b and y ions for certain peptide sequences but not others. All software tools and data sets are made available in the public domain at http://code.google.com/p/ion-mobility-ms-tools/.     

GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.     

A face in the crowd: recognizing peptides through database search

Peptide identification via tandem mass spectrometry sequence database searching is a key method in the array of tools available to the proteomics researcher. The ability to rapidly and sensitively acquire tandem mass spectrometry data and perform peptide and protein identifications has become a commonly used proteomics analysis technique because of advances in both instrumentation and software. Although many different tandem mass spectrometry database search tools are currently available from both academic and commercial sources, these algorithms share similar core elements while maintaining distinctive features. This review revisits the mechanism of sequence database searching and discusses how various parameter settings impact the underlying search.     

What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Targeted proteomics has gained significant popularity in mass spectrometry‐based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high‐throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).     

Mass spectrometric-based approaches in quantitative proteomics

Classically, experiments aimed at studying changes in protein expression have always followed a small set of proteins. This focused approach was necessary since tools to efficiently analyze large numbers of proteins were simply not available. Large-scale quantitative proteomics promises to produce reams of data that previously would have taken decades to measure with classical methods. Mass spectrometry is already a well-established protein identification tool and recent methodological developments indicate that it can also be successfully applied to extract quantitative data of protein abundance. From the first reports 4 years ago, numerous schemes to take advantage of stable isotope nuclei incorporation in proteins and peptides have been developed. Here we review the benefits and pitfalls of some of the most commonly used protocols, focusing on a procedure now being used extensively in our laboratory, stable isotope labeling with amino acids in cell culture (SILAC). The basic theory, application, and data analysis of a SILAC experiment are discussed. The emerging nature of these techniques and the rapid pace of technological development make forecasting the directions of the field difficult but we speculate that SILAC will soon be a key tool of quantitative proteomics.     

Analysis and validation of proteomic data generated by tandem mass spectrometry

The analysis of the large amount of data generated in mass spectrometry-based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.     

LFQuant: A label‐free fast quantitative analysis tool for high‐resolution LC‐MS/MS proteomics data

Database searching based methods for label‐free quantification aim to reconstruct the peptide extracted ion chromatogram based on the identification information, which can limit the search space and thus make the data processing much faster. The random effect of the MS/MS sampling can be remedied by cross‐assignment among different runs. Here, we present a new label‐free fast quantitative analysis tool, LFQuant, for high‐resolution LC‐MS/MS proteomics data based on database searching. It is designed to accept raw data in two common formats (mzXML and Thermo RAW), and database search results from mainstream tools (MASCOT, SEQUEST, and X!Tandem), as input data. LFQuant can handle large‐scale label‐free data with fractionation such as SDS‐PAGE and 2D LC. It is easy to use and provides handy user interfaces for data loading, parameter setting, quantitative analysis, and quantitative data visualization. LFQuant was compared with two common quantification software packages, MaxQuant and IDEAL‐Q, on the replication data set and the UPS1 standard data set. The results show that LFQuant performs better than them in terms of both precision and accuracy, and consumes significantly less processing time. LFQuant is freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/lfquant/ .     

Bottom up proteomics data analysis strategies to explore protein modifications and genomic variants

The quest to understand biological systems requires further attention of the scientific community to the challenges faced in proteomics. In fact the complexity of the proteome reaches uncountable orders of magnitude. This means that significant technical and data‐analytic innovations will be needed for the full understanding of biology. Current state of art MS is probably our best choice for studying protein complexity and exploring new ways to use MS and MS derived data should be given higher priority. We present here a brief overview of visualization and statistical analysis strategies for quantitative peptide values on an individual protein basis. These analysis strategies can help pinpoint protein modifications, splice, and genomic variants of biological relevance. We demonstrate the application of these data analysis strategies using a bottom‐up proteomics dataset obtained in a drug profiling experiment. Furthermore, we have also observed that the presented methods are useful for studying peptide distributions from clinical samples from a large number of individuals. We expect that the presented data analysis strategy will be useful in the future to define functional protein variants in biological model systems and disease studies. Therefore robust software implementing these strategies is urgently needed.     

Plant membrane proteomics

Plant membrane proteins are involved in many different functions according to their location in the cell. For instance, the chloroplast has two membrane systems, thylakoids and envelope, with specialized membrane proteins for photosynthesis and metabolite and ion transporters, respectively. Although recent advances in sample preparation and analytical techniques have been achieved for the study of membrane proteins, the characterization of these proteins, especially the hydrophobic ones, is still challenging. The present review highlights recent advances in methodologies for identification of plant membrane proteins from purified subcellular structures. The interest of combining several complementary extraction procedures to take into account specific features of membrane proteins is discussed in the light of recent proteomics data, notably for chloroplast envelope, mitochondrial membranes and plasma membrane from Arabidopsis. These examples also illustrate how, on one hand, proteomics can feed bioinformatics for a better definition of prediction tools and, on the other hand, although prediction tools are not 100% reliable, they can give valuable information for biological investigations. In particular, membrane proteomics brings new insights over plant membrane systems, on both the membrane compartment where proteins are working and their putative cellular function.     

IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT

Isobaric peptide labeling plays an important role in relative quantitative comparisons of proteomes. Isobaric labeling techniques utilize MS/MS spectra for relative quantification, which can be either based on the relative intensities of reporter ions in the low mass region (iTRAQ and TMT) or on the relative intensities of quantification signatures throughout the spectrum due to isobaric peptide termini labeling (IPTL). Due to the increased quantitative information found in MS/MS fragment spectra generated by the recently developed IPTL approach, new software was required to extract the quantitative information. IsobariQ was specifically developed for this purpose; however, support for the reporter ion techniques iTRAQ and TMT is also included. In addition, to address recently emphasized issues about heterogeneity of variance in proteomics data sets, IsobariQ employs the statistical software package R and variance stabilizing normalization (VSN) algorithms available therein. Finally, the functionality of IsobariQ is validated with data sets of experiments using 6-plex TMT and IPTL. Notably, protein substrates resulting from cleavage by proteases can be identified as shown for caspase targets in apoptosis.     

Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ)

We present a tool to improve quantitative accuracy and precision in mass spectrometry based on shotgun proteomics: protein quantification by peptide quality control, PQPQ. The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns arise either from outlier peptides or because of the presence of different protein species. By correlation analysis, protein quantification by peptide quality control identifies and excludes outliers and detects the existence of different protein species. Alternative protein species are then quantified separately. By validating the algorithm on seven data sets related to different cancer studies we show that data processing by protein quantification by peptide quality control improves the information output from shotgun proteomics. Data from two labeling procedures and three different instrumental platforms was included in the evaluation. With this unique method using both peptide sequence data and quantitative data we can improve the quantitative accuracy and precision on the protein level and detect different protein species.     

Enabling proteomics: the need for an extendable 'workbench' for user-configurable solutions

Proteomics has the capability to generate overwhelming quantities of data in relatively short timescales, and it is not uncommon to see experimenters investing substantially more time in data analysis than in data gathering. Although several sophisticated tools for data reduction and analysis are available, they lack the flexibility to cope with increasingly innovative experimental strategies and new database resources that encode both qualitative and quantitative data. I will outline a specification of a flexible proteomics tool that could address many current bottlenecks and deficiencies.     

A Comparison of Protein Extraction Methods Suitable for Gel-Based Proteomic Studies of Aphid Proteins

Protein extraction methods can vary widely in reproducibility and in representation of the total proteome, yet there are limited data comparing protein isolation methods. The methodical comparison of protein isolation methods is the first critical step for proteomic studies. To address this, we compared three methods for isolation, purification, and solubilization of insect proteins. The aphid Schizaphis graminum, an agricultural pest, was the source of insect tissue. Proteins were extracted using TCA in acetone (TCA-acetone), phenol, or multi-detergents in a chaotrope solution. Extracted proteins were solubilized in a multiple chaotrope solution and examined using 1-D and 2-D electrophoresis and compared directly using 2-D Difference Gel Electrophoresis (2-D DIGE). Mass spectrometry was used to identify proteins from each extraction type. We were unable to ascribe the differences in the proteins extracted to particular physical characteristics, cell location, or biological function. The TCA-acetone extraction yielded the greatest amount of protein from aphid tissues. Each extraction method isolated a unique subset of the aphid proteome. The TCA-acetone method was explored further for its quantitative reliability using 2-D DIGE. Principal component analysis showed that little of the variation in the data was a result of technical issues, thus demonstrating that the TCA-acetone extraction is a reliable method for preparing aphid proteins for a quantitative proteomics experiment. These data suggest that although the TCA-acetone method is a suitable method for quantitative aphid proteomics, a combination of extraction approaches is recommended for increasing proteome coverage when using gel-based separation techniques.     

Prediction of missed cleavage sites in tryptic peptides aids protein identification in proteomics

Protein identification via peptide mass fingerprinting (PMF) remains a key component of high-throughput proteomics experiments in post-genomic science. Candidate protein identifications are made using bioinformatic tools from peptide peak lists obtained via mass spectrometry (MS). These algorithms rely on several search parameters, including the number of potential uncut peptide bonds matching the primary specificity of the hydrolytic enzyme used in the experiment. Typically, up to one of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using two distinct, nonredundant datasets of peptides identified via PMF and tandem MS, a simple predictive method based on information theory is presented which is able to identify experimentally defined missed cleavages with up to 90% accuracy from amino acid sequence alone. Using this simple protocol, we are able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines, using the PMF dataset as a test set. In addition, the improved approach is also demonstrated on an independent PMF data set of known proteins that also has corresponding high-quality tandem MS data, validating the protein identifications. This approach has wider applicability for proteomics database searching, and the program for predicting missed cleavages and masking Fasta-formatted protein sequence databases has been made available via http:// ispider.smith.man.ac uk/MissedCleave.     

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.     

Role and challenges of proteomics in pharma and biotech: technical, scientific and commercial perspective

Contemporary proteomics, currently in its exponential growth phase, is a bewildering array of tools. Proteomic methods are the result of a convergence of rapidly improving mass spectrometry technologies, protein chemistry and separation sciences, genomics and bioinformatics. Strides in improving proteomics technologies to map and measure proteomes and subproteomes are being made. However, no single proteomic platform appears ideally suited to address all research needs or accomplish ambitious goals satisfactorily. However, proteomics is in a unique position to contribute to protein discovery and to public health in terms of better biomarkers, diagnostics and treatment of disease. While the potential is great, many challenges and issues remain to be solved. Fundamental issues, such as biological variability, pre-analytic factors and analytical reproducibility, remain to be resolved. Neither an all-genetic approach nor an all-proteomic approach will solve biological complexity. Proteomics will be the foundation for constructing and extracting useful knowledge to pharma and biotech depicted in the following path: data --> structured data --> information --> information architecture --> knowledge --> useful knowledge.     

A computational approach toward label-free protein quantification using predicted peptide detectability

We propose here a new concept of peptide detectability which could be an important factor in explaining the relationship between a protein's quantity and the peptides identified from it in a high-throughput proteomics experiment. We define peptide detectability as the probability of observing a peptide in a standard sample analyzed by a standard proteomics routine and argue that it is an intrinsic property of the peptide sequence and neighboring regions in the parent protein. To test this hypothesis we first used publicly available data and data from our own synthetic samples in which quantities of model proteins were controlled. We then applied machine learning approaches to demonstrate that peptide detectability can be predicted from its sequence and the neighboring regions in the parent protein with satisfactory accuracy. The utility of this approach for protein quantification is demonstrated by peptides with higher detectability generally being identified at lower concentrations over those with lower detectability in the synthetic protein mixtures. These results establish a direct link between protein concentration and peptide detectability. We show that for each protein there exists a level of peptide detectability above which peptides are detected and below which peptides are not detected in an experiment. We call this level the minimum acceptable detectability for identified peptides (MDIP) which can be calibrated to predict protein concentration. Triplicate analysis of a biological sample showed that these MDIP values are consistent among the three data sets.     

Computational analysis of shotgun proteomics data

Proteomics technology is progressing at an incredible rate. The latest generation of tandem mass spectrometers can now acquire tens of thousands of fragmentation spectra in a matter of hours. Furthermore, quantitative proteomics methods have been developed that incorporate a stable isotope-labeled internal standard for every peptide within a complex protein mixture for the measurement of relative protein abundances. These developments have opened the doors for 'shotgun' proteomics, yet have also placed a burden on the computational approaches that manage the data. With each new method that is developed, the quantity of data that can be derived from a single experiment increases. To deal with this increase, new computational approaches are being developed to manage the data and assess false positives. This review discusses current approaches for analyzing proteomics data by mass spectrometry and identifies present computational limitations and bottlenecks.