ITMSQ: A software tool for N‐ and C‐terminal fragment ion pairs based isobaric tandem mass spectrometry quantification

Tandem MS (MS2) quantification using the series of N‐ and C‐terminal fragment ion pairs generated from isobaric‐labelled peptides was recently considered an accurate strategy in quantitative proteomics. However, the presence of multiplexed terminal fragment ion in MS2 spectra may reduce the efficiency of peptide identification, resulting in lower identification scores or even incorrect assignments. To address this issue, we developed a quantitative software tool, denoted isobaric tandem MS quantification (ITMSQ), to improve N‐ and C‐terminal fragment ion pairs based isobaric MS2 quantification. A spectrum splitting module was designed to separate the MS2 spectra from different samples, increasing the accuracy of both identification and quantification. ITMSQ offers a convenient interface through which parameters can be changed along with the labelling method, and the result files and all of the intermediate files can be exported. We performed an analysis of in vivo terminal amino acid labelling labelled HeLa samples and found that the numbers of quantified proteins and peptides increased by 13.64 and 27.52% after spectrum splitting, respectively. In conclusion, ITMSQ provides an accurate and reliable quantitative solutionfor N‐ and C‐terminal fragment ion pairs based isobaric MS2 quantitative methods.     

MMS2plot: An R Package for Visualizing Multiple MS/MS Spectra for Groups of Modified and Non‐Modified Peptides

A large number of post‐translational modifications (PTMs) in proteins are buried in the unassigned mass spectrometric (MS) spectra in shot‐gun proteomics datasets. Because the modified peptide fragments are low in abundance relative to the corresponding non‐modified versions, it is critical to develop tools that allow facile evaluation of assignment of PTMs based on the MS/MS spectra. Such tools will preferably have the ability to allow comparison of fragment ion spectra and retention time between the modified and unmodified peptide pairs or group. Herein, MMS2plot, an R package for visualizing peptide‐spectrum matches (PSMs) for multiple peptides, is described. MMS2plot features a batch mode and generates the output images in vector graphics file format that facilitate evaluation and publication of the PSM assignment. MMS2plot is expected to play an important role in PTM discovery from large‐scale proteomics datasets generated by liquid chromatography‐MS/MS. The MMS2plot package is freely available at https://github.com/lileir/MMS2plot under the GPL‐3 license.     

Helicobacter pylori proteomics by 2‐DE/MS, 1‐DE‐LC/MS and functional data mining

With its predicted proteome of 1550 proteins (data set Etalon) Helicobacter pylori 26695 represents a perfect model system of medium complexity for investigating basic questions in proteomics. We analyzed urea‐solubilized proteins by 2‐DE/MS (data set 2‐DE) and by 1‐DE‐LC/MS (Supprot); proteins insoluble in 9 M urea but solubilized by SDS (Pellet); proteins precipitating in the Sephadex layer at the application side of IEF (Sephadex) by 1‐DE‐LC/MS; and proteins precipitating close to the application side within the IEF gel by LC/MS (Startline). The experimental proteomics data of H. pylori comprising 567 proteins (protein coverage: 36.6%) were stored in the Proteome Database System for Microbial Research ( http://www.mpiib‐berlin.mpg.de/2D‐PAGE/ ), which gives access to raw mass spectra (MALDI‐TOF/TOF) in T2D format, as well as to text files of peak lists. For data mining the protein mapping and comparison tool PROMPT ( http://webclu.bio.wzw.tum.de/prompt/ ) was used. The percentage of proteins with transmembrane regions, relative to all proteins detected, was 0, 0.2, 0, 0.5, 3.8 and 6.3% for 2‐DE, Supprot, Startline, Sephadex, Pellet, and Etalon, respectively. 2‐DE does not separate membrane proteins because they are insoluble in 9 M urea/70 mM DTT and 2% CHAPS. SDS solubilizes a considerable portion of the urea‐insoluble proteins and makes them accessible for separation by SDS‐PAGE and LC. The 2‐DE/MS analysis with urea‐solubilized proteins and the 1‐DE‐LC/MS analysis with the urea‐insoluble protein fraction (Pellet) are complementary procedures in the pursuit of a complete proteome analysis. Access to the PROMPT‐generated diagrams in the Proteome Database allows the mining of experimental data with respect to other functional aspects.     

Virtual Expert Mass Spectrometrist: iTRAQ tool for database‐dependent search,quantitation and result storage

A frequent goal of MS‐based proteomics experiments nowadays is to quantify changes in the abundance of proteins across several biological samples. The iTRAQ labeling method is a powerful technique; when combined with LC coupled to MS/MS it allows relative quantitation of up to eight different samples simultaneously. Despite the usefulness of iTRAQ current software solutions have limited functionality and require the combined use of several software programs for analysis of the data from different MS vendors. We developed an integrated tool, now available in the virtual expert mass spectrometrist (VEMS) program, for database‐dependent search of MS/MS spectra, quantitation and database storage for iTRAQ‐labeled samples. VEMS also provides useful alternative report types for large‐scale quantitative experiments. The implemented statistical algorithms build on quantitative algorithms previously used in proposed iTRAQ tools as described in detail herein. We propose a new algorithm, which provides more accurate peptide ratios for data that show an intensity‐dependent saturation. The accuracy of the proposed iTRAQ algorithm and the performance of VEMS are demonstrated by comparing results from VEMS, MASCOT and PEAKS Q obtained by analyzing data from a reference mixture of six proteins. Users can download VEMS and test data from “ http://www.portugene.com/software.html ”.     

Two‐dimensional reference map for the basic proteome of the human dorsolateral prefrontal cortex (dlPFC) of the prefrontal lobe region of the brain

We describe a 2‐DE proteomic reference map containing 227 basic proteins in the dorsolateral prefrontal cortex region of the human brain. Proteins were separated in the first dimension on pH 6–11 IPG strips using paper‐bridge loading and on 12% SDS‐PAGE in the second dimension. Proteins were subsequently identified by MS and spectra were analyzed using an in‐house proteomics data analysis platform, Proline. The 2‐DE reference map is available via the UCD 2‐DE Proteome Database ( http://proteomics‐portal.ucd.ie:8082 ) and can also be accessed via the WORLD‐2DPAGE Portal ( http://www.expasy.ch/world‐2dpage/ ). The associated protein identification data have been submitted to the PRIDE database (accession numbers 10018–10033). Separation of proteins in the basic region resolves more membrane associated proteins relevant to the synaptic pathology central to many neurological disorders. The 2‐DE reference map will aid with further characterisation of neurological disorders such as bipolar and schizophrenia.     

Implementation and application of a versatile clustering tool for tandem mass spectrometry data

High-throughput proteomics experiments typically generate large amounts of peptide fragmentation mass spectra during a single experiment. There is often a substantial amount of redundant fragmentation of the same precursors among these spectra, which is usually considered a nuisance. We here discuss the potential of clustering and merging redundant spectra to turn this redundancy into a useful property of the dataset. To this end, we have created the first general-purpose, freely available open-source software application for clustering and merging MS/MS spectra. The application also introduces a novel approach to calculating the similarity of fragmentation mass spectra that takes into account the increased precision of modern mass spectrometers, and we suggest a simple but effective improvement to single-linkage clustering. The application and the novel algorithms are applied to several real-life proteomic datasets and the results are discussed. An analysis of the influence of the different algorithms available and their parameters is given, as well as a number of important applications of the overall approach.     

General framework for developing and evaluating database scoring algorithms using the TANDEM search engine

MOTIVATION: Tandem mass spectrometry (MS/MS) identifies protein sequences using database search engines, at the core of which is a score that measures the similarity between peptide MS/MS spectra and a protein sequence database. The TANDEM application was developed as a freely available database search engine for the proteomics research community. To extend TANDEM as a platform for further research on developing improved database scoring methods, we modified the software to allow users to redefine the scoring function and replace the native TANDEM scoring function while leaving the remaining core application intact. Redefinition is performed at run time so multiple scoring functions are available to be selected and applied from a single search engine binary. We introduce the implementation of the pluggable scoring algorithm and also provide implementations of two TANDEM compatible scoring functions, one previously described scoring function compatible with PeptideProphet and one very simple scoring function that quantitative researchers may use to begin their development. This extension builds on the open-source TANDEM project and will facilitate research into and dissemination of novel algorithms for matching MS/MS spectra to peptide sequences. The pluggable scoring schema is also compatible with related search applications P3 and Hunter, which are part of the X! suite of database matching algorithms. The pluggable scores and the X! suite of applications are all written in C++. AVAILABILITY: Source code for the scoring functions is available from http://proteomics.fhcrc.org     

Morpheus Spectral Counter: A computational tool for label‐free quantitative mass spectrometry using the Morpheus search engine

Label‐free quantitative MS based on the Normalized Spectral Abundance Factor (NSAF) has emerged as a straightforward and robust method to determine the relative abundance of individual proteins within complex mixtures. Here, we present Morpheus Spectral Counter (MSpC) as the first computational tool that directly calculates NSAF values from output obtained from Morpheus, a fast, open‐source, peptide‐MS/MS matching engine compatible with high‐resolution accurate‐mass instruments. NSAF has distinct advantages over other MS‐based quantification methods, including a greater dynamic range as compared to isobaric tags, no requirement to align and re‐extract MS1 peaks, and increased speed. MSpC features an easy‐to‐use graphic user interface that additionally calculates both distributed and unique NSAF values to permit analyses of both protein families and isoforms/proteoforms. MSpC determinations of protein concentration were linear over several orders of magnitude based on the analysis of several high‐mass accuracy datasets either obtained from PRIDE or generated with total cell extracts spiked with purified Arabidopsis 20S proteasomes. The MSpC software was developed in C# and is open sourced under a permissive license with the code made available at http://dcgemperline.github.io/Morpheus_SpC/ .     

Quantitative profile of five murine core proteomes using label-free functional proteomics

Analysis of primary animal and human tissues is key in biological and biomedical research. Comparative proteomics analysis of primary biological material would benefit from uncomplicated experimental work flows capable of evaluating an unlimited number of samples. In this report we describe the application of label-free proteomics to the quantitative analysis of five mouse core proteomes. We developed a computer program and normalization procedures that allow exploitation of the quantitative data inherent in LC-MS/MS experiments for relative and absolute quantification of proteins in complex mixtures. Important features of this approach include (i) its ability to compare an unlimited number of samples, (ii) its applicability to primary tissues and cultured cells, (iii) its straightforward work flow without chemical reaction steps, and (iv) its usefulness not only for relative quantification but also for estimation of absolute protein abundance. We applied this approach to quantitatively characterize the most abundant proteins in murine brain, heart, kidney, liver, and lung. We matched 8,800 MS/MS peptide spectra to 1,500 proteins and generated 44,000 independent data points to profile the approximately 1,000 most abundant proteins in mouse tissues. This dataset provides a quantitative profile of the fundamental proteome of a mouse, identifies the major similarities and differences between organ-specific proteomes, and serves as a paradigm of how label-free quantitative MS can be used to characterize the phenotype of mammalian primary tissues at the molecular level.     

APP: an Automated Proteomics Pipeline for the analysis of mass spectrometry data based on multiple open access tools

Background

Mass spectrometry analyses of complex protein samples yield large amounts of data and specific expertise is needed for data analysis, in addition to a dedicated computer infrastructure. Furthermore, the identification of proteins and their specific properties require the use of multiple independent bioinformatics tools and several database search algorithms to process the same datasets. In order to facilitate and increase the speed of data analysis, there is a need for an integrated platform that would allow a comprehensive profiling of thousands of peptides and proteins in a single process through the simultaneous exploitation of multiple complementary algorithms.

Results

We have established a new proteomics pipeline designated as APP that fulfills these objectives using a complete series of tools freely available from open sources. APP automates the processing of proteomics tasks such as peptide identification, validation and quantitation from LC-MS/MS data and allows easy integration of many separate proteomics tools. Distributed processing is at the core of APP, allowing the processing of very large datasets using any combination of Windows/Linux physical or virtual computing resources.

Conclusions

APP provides distributed computing nodes that are simple to set up, greatly relieving the need for separate IT competence when handling large datasets. The modular nature of APP allows complex workflows to be managed and distributed, speeding up throughput and setup. Additionally, APP logs execution information on all executed tasks and generated results, simplifying information management and validation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0441-8) contains supplementary material, which is available to authorized users.     

A label‐free internal standard method for the differential analysis of bioactive lupin proteins using nano HPLC‐Chip coupled with Ion Trap mass spectrometry

Quantitative proteomics based on MS is useful for pointing out the differences in some food proteomes relevant to human nutrition. Stable isotope label‐free (SIF) techniques are suitable for comparing an unlimited number of samples by the use of relatively simple experimental workflows. We have developed an internal standard label‐free method based on the intensities of peptide precursor ions from MS/MS spectra, collected in data dependent runs, for the simultaneous qualitative characterization and relative quantification of storage proteins of Lupinus albus seeds in protein extracts of four lupin cultivars (cv Adam, Arés, Lucky, Multitalia). The use of an innovative microfluidic system, the HPLC‐Chip, coupled with a classical IT mass spectrometer, has allowed a complete qualitative characterization of all proteins. In particular, the homology search mode has permitted to identify single amino acid substitutions in the sequences of vicilins (β‐conglutin precursor and vicilin‐like protein). The MS/MS sequencing of substituted peptides confirms the high heterogeneity of vicilins according to the peculiar characteristics of the vicilin‐encoding gene family. Two suitable bioinformatics parameters were optimized for the differential analyses of the main bioactive proteins: the “normalized protein average of common reproducible peptides” (N‐ACRP) for γ‐conglutin, which is a homogeneous protein, and the “normalized protein mean peptide spectral intensity” (N‐MEAN) for the highly heterogenous class of the vicilins.     

Combining selected reaction monitoring with discovery proteomics in limited biological samples

Simultaneous quantification of multiple proteins by selected reaction monitoring (SRM) has several applications in cell signaling studies including embryo proteomics. However, concerns have recently been raised over the specificity of SRM assays due to possible ion redundancy and/or sequence similarity of selected peptide with multiple non‐related proteins. In this Viewpoint article, we discuss some simple measures that can increase our confidence in the accuracy of SRM scans used in proteomic experiments. At least in embryonic samples from porcine species, these measures were found to be useful in validating MS‐identified differentially expressed proteins. Among the nine proteins analyzed by SRM assay, all the proteins that were found to be up‐ or down‐regulated in MS experiment were also faithfully up‐ or down‐regulated in SRM assay.     

XTandem Parser: An open‐source library to parse and analyse X!Tandem MS/MS search results

Identification of proteins by MS plays an important role in proteomics. A crucial step concerns the identification of peptides from MS/MS spectra. The X!Tandem Project ( http://www.thegpm.org/tandem ) supplies an open‐source search engine for this purpose. In this study, we present an open‐source Java library called XTandem Parser that parses X!Tandem XML result files into an easily accessible and fully functional object model ( http://xtandem‐parser.googlecode.com ). In addition, a graphical user interface is provided that functions as a usage example and an end‐user visualization tool.     

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large‐scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2‐DE/MS‐based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2‐DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2‐DE experiments with MS‐data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our “PROTEOMER” database is its high cross‐referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross‐referencing can transform information into biological knowledge.     

IQuant: An automated pipeline for quantitative proteomics based upon isobaric tags

Quantitative proteomics technology based on isobaric tags is playing an important role in proteomic investigation. In this paper, we present an automated software, named IQuant, which integrates a postprocessing tool of protein identification and advanced statistical algorithms to process the MS/MS signals generated from the peptides labeled by isobaric tags and aims at proteomics quantification. The software of IQuant, which is freely downloaded at http://sourceforge.net/projects/iquant/ , can run from a graphical user interface and a command‐line interface, and can work on both Windows and Linux systems.     

Pre‐fractionation of rat liver cytosol proteins prior to mass spectrometry‐based proteomic analysis using tandem biomimetic affinity chromatography

Efficient and high resolution separation of the protein mixture prior to trypsin digestion and mass spectrometry (MS) analysis is generally used to reduce the complexity of samples, an approach that highly increases the probability of detecting low‐copy‐number proteins. Our laboratory has constructed an affinity ligand library composed of thousands of ligands with different protein absorbance effects. Structural differences between these ligands result in different non‐bonded protein–ligand interactions, thus each ligand exhibits a specific affinity to some protein groups. In this work, we first selected out several synthetic affinity ligands showing large band distribution differences in proteins absorbance profiles, and a tandem composition of these affinity ligands was used to distribute complex rat liver cytosol into simple subgroups. Ultimately, all the fractions collected from tandem affinity pre‐fractionation were digested and then analyzed by LC‐MS/MS, which resulted in high confidence identification of 665 unique rat protein groups, 1.8 times as many proteins as were detected in the un‐fractionated sample (371 protein groups). Of these, 375 new proteins were identified in tandem fractions, and most of the proteins identified in un‐fractionated sample (290, 80%) also emerged in tandem fractions. Most importantly, 430 unique proteins (64.7%) only characterized in specific fractions, indicating that the crude tissue extract was well distributed by tandem affinity fractionation. All detected proteins were bioinformatically annotated according to their physicochemical characteristics (such as MW, pI, GRAVY value, TM Helices). This approach highlighted the sensitivity of this method to a wide variety of protein classes. Combined usage of tandem affinity pre‐fractionation with MS‐based proteomic analysis is simple, low‐cost, and effective, providing the prospect of broad application in proteomics. Copyright © 2009 John Wiley & Sons, Ltd.     

PAS‐cal: A repetitive peptide sequence calibration standard for MALDI mass spectrometry

Mass spectrometers equipped with matrix‐assisted laser desorption/ionization (MALDI‐MS) require frequent multipoint calibration to obtain good mass accuracy over a wide mass range and across large numbers of samples. In this study, we introduce a new synthetic peptide mass calibration standard termed PAS‐cal tailored for MALDI‐MS based bottom‐up proteomics. This standard consists of 30 peptides between 8 and 37 amino acids long and each constructed to contain repetitive sequences of Pro, Ala and Ser as well as one C‐terminal arginine residue. MALDI spectra thus cover a mass range between 750 and 3200 m/z in MS mode and between 100 and 3200 m/z in MS/MS mode. Our results show that multipoint calibration of MS spectra using PAS‐cal peptides compares well to current commercial reagents for protein identification by PMF. Calibration of tandem mass spectra from LC‐MALDI experiments using the longest peptide, PAS‐cal37, resulted in smaller fragment ion mass errors, more matching fragment ions and more protein and peptide identifications compared to commercial standards, making the PAS‐cal standard generically useful for bottom‐up proteomics.     

SwissPIT: An workflow‐based platform for analyzing tandem‐MS spectra using the Grid

The identification and characterization of peptides from MS/MS data represents a critical aspect of proteomics. It has been the subject of extensive research in bioinformatics resulting in the generation of a fair number of identification software tools. Most often, only one program with a specific and unvarying set of parameters is selected for identifying proteins. Hence, a significant proportion of the experimental spectra do not match the peptide sequences in the screened database due to inappropriate parameters or scoring schemes. The Swiss protein identification toolbox (swissPIT) project provides the scientific community with an expandable multitool platform for automated in‐depth analysis of MS data also able to handle data from high‐throughput experiments. With swissPIT many problems have been solved: The missing standards for input and output formats (A), creation of analysis workflows (B), unified result visualization (C), and simplicity of the user interface (D). Currently, swissPIT supports four different programs implementing two different search strategies to identify MS/MS spectra. Conceived to handle the calculation‐intensive needs of each of the programs, swissPIT uses the distributed resources of a Swiss‐wide computer Grid (http://www.swing‐grid.ch).