DBParser: web-based software for shotgun proteomic data analyses

We describe a web-based program called 'DBParser' for rapidly culling, merging, and comparing sequence search engine results from multiple LC-MS/MS peptide analyses. DBParser employs the principle of parsimony to consolidate redundant protein assignments and derive the most concise set of proteins consistent with all of the assigned peptide sequences observed in an experiment or series of experiments. The resulting reports summarize peptide and protein identifications from multidimensional experiments that may contain a single data set or combine data from a group of data sets, all related to a single analytical sample. Additionally, the results of multiple experiments, each of which may contain several data sets, can be compared in reports that identify features that are common or different. DBParser actively links to the primary mass spectral data and to public online databases such as NCBI, GO, and Swiss-Prot in order to structure contextually specific reports for biologists and biochemists.     

Monte carlo simulation-based algorithms for analysis of shotgun proteomic data

Two new statistical models based on Monte Carlo Simulation (MCS) have been developed to score peptide matches in shotgun proteomic data and incorporated in a database search program, MassMatrix (www.massmatrix.net). The first model evaluates peptide matches based on the total abundance of matched peaks in the experimental spectra. The second model evaluates amino acid residue tags within MS/MS spectra. The two models provide complementary scores for peptide matches that result in higher confidence in peptide identification when significant scores are returned from both models. The MCS-based models use a variance reduction technique that improves estimation precision. Due to the high computational expense of MCS-based models, peptide matches were prefiltered by other statistical models before further evaluation by the MCS-based models. Receiver operating characteristic analysis of the data sets confirmed that MCS-based models improved the overall performance of the MassMatrix search software, especially for low-mass accuracy data sets.     

Whole genome searching with shotgun proteomic data: applications for genome annotation

High-throughput genome sequencing continues to accelerate the rate at which complete genomes are available for biological research. Many of these new genome sequences have little or no genome annotation currently available and hence rely upon computational predictions of protein coding genes. Evidence of translation from proteomic techniques could facilitate experimental validation of protein coding genes, but the techniques for whole genome searching with MS/MS data have not been adequately developed to date. Here we describe GENQUEST, a novel method using peptide isoelectric focusing and accurate mass to greatly reduce the peptide search space, making fast, accurate, and sensitive whole human genome searching possible on common desktop computers. In an initial experiment, almost all exonic peptides identified in a protein database search were identified when searching genomic sequence. Many peptides identified exclusively in the genome searches were incorrectly identified or could not be experimentally validated, highlighting the importance of orthogonal validation. Experimentally validated peptides exclusive to the genomic searches can be used to reannotate protein coding genes. GENQUEST represents an experimental tool that can be used by the proteomics community at large for validating computational approaches to genome annotation.     

iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.     

Increasing information from shotgun proteomic data by accounting for misassigned precursor ion masses

Although mass spectrometers are capable of providing high mass accuracy data, assignment of true monoisotopic precursor ion mass is complicated during data-dependent ion selection for LC-MS/MS analysis of complex mixtures. The complication arises when chromatographic peak widths for a given analyte exceed the time required to acquire a precursor ion mass spectrum. The result is that many measured monoisotopic masses are misassigned due to calculation from a single mass spectrum with poor ion statistics based on only a fraction of the total available ions for a given analyte. Such data in turn produces errors in automated database searches, where precursor m/z value is one search parameter. We propose here a postacquisition approach to correct misassigned monoisotopic m/z values that involves peak detection over the entire elution profile and correction of the precursor ion monoisotopic mass. As a result of using this approach to reprocess shotgun proteomic data we increased peptide sequence assignments by 10% while reducing the estimated false positive ratio from 1 to 0.2%. We also show that 4% of the salvaged identifications may be accounted for by correction of mixed tandem mass spectra resulting from fragmentation of multiple peptides simultaneously, a situation which we refer to as accidental CID.     

HPLC-MS/MS shotgun proteomic research of deer antlers with multiparallel protein extraction methods

Deer antlers mature rapidly in 60 days, and subsequently shed in 5 days with rapid ossification. During this procedure, the function of deer antlers changes significantly. Therefore, the profiling of antler proteome is helpful to discover important growing and shedding regulation proteins, which might be of great significance for studying development and regeneration. In this study, a parallel protein extraction strategy was developed to extract proteins from antlers of red deer with five different lysis solutions, followed by shotgun proteomic analysis by microflow reversed-phase liquid chromatography/electrospray ionization/tandem mass spectrometry (μRPLC-ESI-MS/MS) with a 30 cm-long serially coupled microcolumn. Our experimental results showed that the identified proteins extracted by five kinds of lysis solution were complementary to each other. In total, 416 unique proteins were identified, with relative molecular masses from 2000 to 600,000, and isoelectric points from 3.84 to 11.57. All these results demonstrate that the combination of parallel protein extraction strategy and μRPLC-ESI-MS/MS analysis with serially coupled long microcolumns might be of great significance for comprehensive proteomic research of deer antler.     

The proteomic to biology inference,a frequently overlooked concern in the interpretation of proteomic data: A plea for functional validation

Proteomics will celebrate its 20th year in 2014. In this relatively short period of time, it has invaded most areas of biology and its use will probably continue to spread in the future. These two decades have seen a considerable increase in the speed and sensitivity of protein identification and characterization, even from complex samples. Indeed, what was a challenge twenty years ago is now little more than a daily routine. Although not completely over, the technological challenge now makes room to another challenge, which is the best possible appraisal and exploitation of proteomic data to draw the best possible conclusions from a biological point of view. The point developed in this paper is that proteomic data are almost always fragmentary. This means in turn that although better than an mRNA level, a protein level is often insufficient to draw a valid conclusion from a biological point of view, especially in a world where PTMs play such an important role. This means in turn that transformation of proteomic data into biological data requires an important intermediate layer of functional validation, i.e. not merely the confirmation of protein abundance changes by other methods, but a functional appraisal of the biological consequences of the protein level changes highlighted by the proteomic screens.     

Quantitative shotgun proteomic analysis of cold-stressed mature rice anthers

Rice crops are vulnerable to low temperatures. During development, the reproductive stage is particularly sensitive to cold exposure, which causes abnormal pollen development and a high degree of male sterility. In this study, shotgun proteomic analysis was used to analyze rice anthers containing pollen grains from a cold-tolerant variety, Dianxi 4. Protein expression was compared between normal anthers and anthers exposed to cold temperatures at the young microspore stage. In total, 3835 non-redundant proteins were identified in the rice anther. Of these, 441 proteins were differentially expressed between normal and cold-treated anthers. Pollen allergens, ATP synthase, actin, profilin, and β-expansin proteins were highly abundant, reflecting anther development, pollen germination, and pollen tube elongation. Starch and sucrose metabolic proteins such as α-amylase precursor and 4-α-glucanotransferase exhibited reduced expression after cold exposure. Among the proteins that exhibited increased expression after cold exposure, C2 domain proteins, and GRPs were identified as candidate signaling factors for mediation of the cold tolerance response. Through high-throughput proteomic analysis we were able to reveal proteomic changes against cold stress and suggest two signaling factors as the candidate genes.     

膜整合蛋白质的“鸟枪法”蛋白质组学分析策略

疾病状态下生物膜表面蛋白质分子标记的表达量和修饰状态会发生改变。但由于其低丰度和不易溶解等特性,制约了膜蛋白质组学的研究,同时也制约了相关药物靶标的设计。近年来,为克服这些困难,学者们提出了"鸟枪法"的膜蛋白质组学研究策略。基于此,本文论述了"鸟枪法"的蛋白质组学分析的基本过程及其后续的部分改进工作。随着新的策略不断被采用,更多膜蛋白质的拓扑学特征和功能的相关研究一定会走上新的台阶。     

Comparison of protein and peptide prefractionation methods for the shotgun proteomic analysis of Synechocystis sp. PCC 6803

Proteome analysis by gel-free "shotgun" proteomics relies on the simplification of a peptide mixture before it is analyzed in a mass spectrometer. While separation on a reverse-phase (RP) liquid chromatographic column is widely employed, a variety of other methods have been used to fractionate both proteins and peptides before this step. We compared six different protein and peptide fractionation workflows, using Synechocystis sp. PCC 6803, a useful model cyanobacterium for potential exploitation to improve its production of hydrogen and other secondary metabolites. Pre-digestion protein separation was performed by strip-based isoelectric focusing, one-dimensional polyacrylamide gel electrophoresis, or weak anion exchange chromatography, while pre-RP peptide separation was accomplished by isoelectric focusing (IEF) or strong cation exchange chromatography. Peptides were identified using electrospray ionization quadrupole time of flight-tandem mass spectrometry. Mass spectrometry (MS) and tandem mass spectra were analyzed using ProID software employing both a single organism database and the entire NCBI non-redundant database, and a total of 776 proteins were identified using a stringent set of selection criteria. Method comparisons were made on the basis of the results obtained (number and types of proteins identified), as well as ease of use and other practical aspects. IEF-IEF protein and peptide fractionation prior to RP gave the best overall performance.     

A shotgun proteomic method for the identification of membrane-embedded proteins and peptides

Integral membrane proteins perform crucial cellular functions and are the targets for the majority of pharmaceutical agents. However, the hydrophobic nature of their membrane-embedded domains makes them difficult to work with. Here, we describe a shotgun proteomic method for the high-throughput analysis of the membrane-embedded transmembrane domains of integral membrane proteins which extends the depth of coverage of the membrane proteome.     

SIR: Deterministic protein inference from peptides assigned to MS data

Currently the bottom up approach is the most popular for characterizing protein samples by mass spectrometry. This is mainly attributed to the fact that the bottom up approach has been successfully optimized for high throughput studies. However, the bottom up approach is associated with a number of challenges such as loss of linkage information between peptides. Previous publications have addressed some of these problems which are commonly referred to as protein inference. Nevertheless, all previous publications on the subject are oversimplified and do not represent the full complexity of the proteins identified. To this end we present here SIR (spectra based isoform resolver) that uses a novel transparent and systematic approach for organizing and presenting identified proteins based on peptide spectra assignments. The algorithm groups peptides and proteins into five evidence groups and calculates sixteen parameters for each identified protein that are useful for cases where deterministic protein inference is the goal. The novel approach has been incorporated into SIR which is a user-friendly tool only concerned with protein inference based on imports of Mascot search results. SIR has in addition two visualization tools that facilitate further exploration of the protein inference problem.     

Systematic assessment of the benefits and caveats in mining microbial post-translational modifications from shotgun proteomic data: the response of Shewanella oneidensis to chromate exposure

Microbes are known to regulate both gene expression and protein activity through the use of post-translational modifications (PTMs). Common PTMs involved in cellular signaling and gene control include methylations, acetylations, and phosphorylations, whereas oxidations have been implicated as an indicator of stress. Shewanella oneidensis MR-1 is a Gram-negative bacterium that demonstrates both respiratory versatility and the ability to sense and adapt to diverse environmental conditions. The data set used in this study consisted of tandem mass spectra derived from midlog phase aerobic cultures of S. oneidensis either native or shocked with 1 mM chromate [Cr(VI)]. In this study, three algorithms (DBDigger, Sequest, and InsPecT) were evaluated for their ability to scrutinize shotgun proteomic data for evidence of PTMs. The use of conservative scoring filters for peptides or proteins versus creating a subdatabase first from a nonmodification search was evaluated with DBDigger. The use of higher-scoring filters for peptide identifications was found to result in optimal identifications of PTM peptides with a 2% false discovery rate (FDR) for the total data set using the DBDigger algorithm. However, the FDR climbs to unacceptably high levels when only PTM peptides are considered. Sequest was evaluated as a method for confirming PTM peptides putatively identified using DBDigger; however, there was a low identification rate ( approximately 25%) for the searched spectra. InsPecT was found to have a much lower, and thus more acceptable, FDR than DBDigger for PTM peptides. Comparisons between InsPecT and DBDigger were made with respect to both the FDR and PTM peptide identifications. As a demonstration of this approach, a number of S. oneidensis chemotaxis proteins as well as low-abundance signal transduction proteins were identified as being post-translationally modified in response to chromate challenge.     

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.     

Hierarchical clustering of shotgun proteomics data

A new result report for Mascot search results is described. A greedy set cover algorithm is used to create a minimal set of proteins, which is then grouped into families on the basis of shared peptide matches. Protein families with multiple members are represented by dendrograms, generated by hierarchical clustering using the score of the nonshared peptide matches as a distance metric. The peptide matches to the proteins in a family can be compared side by side to assess the experimental evidence for each protein. If the evidence for a particular family member is considered inadequate, the dendrogram can be cut to reduce the number of distinct family members.     

Analysis of cytoplasmic membrane proteome of Streptococcus pneumoniae by shotgun proteomic approach

In this study, cytoplasmic membrane proteins of S. pneumoniae strain R6 (ATCC BBA-255) were effectively separated from cell wall or extracellular proteins by sodium carbonate precipitation (SCP) and ultracentrifugation. Forty seven proteins were analyzed as cytoplasmic membrane proteins from the 260 proteins identified by the shotgun proteomic method using SDS-PAGE/LC/MS-MS. ABC transporters for metabolites such as metals, oligopeptides, phosphate, sugar, and amino acids, and membrane proteins involved in phosphotransferse systems, were identified as the predominant and abundant, cytoplasmic membrane proteins that would be essential for nutrient uptake, antibiotic resistance and virulence mechanisms. Our result supports that gel-based shotgun proteomics combined with sodium carbonate precipitation and ultracentrifugation is an effective method for analysis of cytoplasmic membrane proteins of S. pneumoniae.     

Predicting co-complexed protein pairs using genomic and proteomic data integration

Background  

Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship.     

Comparison between procedures using SDS for shotgun proteomic analyses of complex samples

Filter-aided sample preparation (FASP) and a new sample preparation method using a modified commercial SDS removal spin column are quantitatively compared in terms of their performance for shotgun proteomic experiments in three complex proteomic samples: a Saccharomyces cerevisiae lysate (insoluble fraction), a Caenorhabditis elegans lysate (soluble fraction), and a human embryonic kidney cell line (HEK293T). The characteristics and total number of peptides and proteins identified are compared between the two procedures. The SDS spin column procedure affords a conservative fourfold improvement in throughput, is more reproducible, less expensive (i.e. requires less materials), and identifies between 30 and 107% more peptides at q≤0.01, than the FASP procedure. The peptides identified by SDS spin column are more hydrophobic than species identified by the FASP procedure as indicated by the distribution of GRAVY scores. Ultimately, these improvements correlate to as great as a 50% increase in protein identifications with two or more peptides.