RScore: a peptide randomicity score for evaluating tandem mass spectra

Public proteomics data: How the field has evolved from sceptical inquiry to the promise of in silico proteomics

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Proteomics data sharing moved from validation to re-use.

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New tools and services make data very easily accessible.

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Metadata provision can still benefit from improvements.

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Quality control metrics will soon be reported along with submitted data.

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Data re-use will enable the advent of actual in silico proteomics.

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.

Proteome analysis of Aspergillus fumigatus total membrane proteins identifies proteins associated with the glycoconjugates and cell wall biosynthesis using 2D LC-MS/MS

We attempted to identify membrane proteins associated with the glycoconjugates and cell wall biosynthesis in the total membrane preparations of Aspergillus fumigatus. The total membrane preparations were first run on 1D gels, and then the stained gels were cut and submitted to in-gel digestion followed by 2D LC-MS/MS and database search. A total of 530 proteins were identified with at least two peptides detected with MS/MS spectra. Seventeen integral membrane proteins were involved in N-, O-glycosylation or GPI anchor biosynthesis. Nine membrane proteins were involved in cell wall biosynthesis. Eight proteins were identified as enzymes involved in sphingolipid synthesis. In addition, the proteins involved in cell wall and ergosterol biosynthesis can potentially be used as antifungal drug targets. Our method, for the first time, clearly provided a global view of the membrane proteins associated with glycoconjugates and cell wall biosynthesis in the total membrane proteome of A. fumigatus.

Cellulosilyticum ruminicola, a newly described rumen bacterium that possesses redundant fibrolytic-protein-encoding genes and degrades lignocellulose with multiple carbohydrate- borne fibrolytic enzymes

Cellulosilyticum ruminicola H1 is a newly described bacterium isolated from yak (Bos grunniens) rumen and is characterized by its ability to grow on a variety of hemicelluloses and degrade cellulosic materials. In this study, we performed the whole-genome sequencing of C. ruminicola H1 and observed a comprehensive set of genes encoding the enzymes essential for hydrolyzing plant cell wall. The corresponding enzymatic activities were also determined in strain H1; these included endoglucanases, cellobiohydrolases, xylanases, mannanase, pectinases, and feruloyl esterases and acetyl esterases to break the interbridge cross-link, as well as the enzymes that degrade the glycosidic bonds. This bacterium appears to produce polymer hydrolases that act on both soluble and crystal celluloses. Approximately half of the cellulytic activities, including cellobiohydrolase (50%), feruloyl esterase (45%), and one third of xylanase (31%) and endoglucanase (36%) activities were bound to cellulosic fibers. However, only a minority of mannase (6.78%) and pectinase (1.76%) activities were fiber associated. Strain H1 seems to degrade the plant-derived polysaccharides by producing individual fibrolytic enzymes, whereas the majority of polysaccharide hydrolases contain carbohydrate-binding module. Cellulosome or cellulosomelike protein complex was never isolated from this bacterium. Thus, the fibrolytic enzyme production of strain H1 may represent a different strategy in cellulase organization used by most of other ruminal microbes, but it applies the fungal mode of cellulose production.

Comparative proteomic analysis of an Aspergillus fumigatus mutant deficient in glucosidase I (AfCwh41)

Alpha-glucosidase I regulates trimming of the terminal alpha-1,2-glucose residue in the N-glycan processing pathway, which plays an important role in quality control systems in mammalian cells. Previously, we identified the gene encoding alpha-glucosidase I in the opportunistic human fungal pathogen Aspergillus fumigatus, namely Afcwh41. Deletion of the Afcwh41 gene results in a severe reduction of conidia formation, a temperature-sensitive deficiency of cell wall integrity, and abnormalities of polar growth and septation. An upregulation of the genes encoding Rho-type GTPases was also observed, which suggests activation of the cell wall integrity pathway in the mutant. Using 2D gel analysis, we revealed that the proteins involved in protein assembly, ubiquitin-mediated degradation and actin organization are altered in the DeltaAfcwh41 mutant. Evidence was obtained for a defect in the polarized localization of the actin cytoskeleton in the mutant. Our results suggest that blocking of the glucose trimming in A. fumigatus might induce accumulation of misfolded proteins in the endoplasmic reticulum; these misfolded proteins are probably required for cell wall synthesis and thus activate the cell wall integrity pathway, which then causes the abnormal polarity associated with the DeltaAfcwh41 mutant.

Oscore: a combined score to reduce false negative rates for peptide identification in tandem mass spectrometry analysis

Tandem mass spectrometry (MS/MS) has been widely used in proteomics studies. Multiple algorithms have been developed for assessing matches between MS/MS spectra and peptide sequences in databases. However, it is still a challenge to reduce false negative rates without compromising the high confidence of peptide identification. In this study, we developed the score, Oscore, by logistic regression using SEQUEST and AMASS variables to identify fully tryptic peptides. Since these variables showed complicated association with each other, combining them together rather than applying them to a threshold model improved the classification of correct and incorrect peptide identifications. Oscore achieved both a lower false negative rate and a lower false positive rate than PeptideProphet on datasets from 18 known protein mixtures and several proteome-scale samples of different complexity, database size and separation methods. By a three-way comparison among Oscore, PeptideProphet and another logistic regression model which made use of PeptideProphet's variables, the main contributor for the improvement made by Oscore is discussed.

Bayesian nonparametric model for the validation of peptide identification in shotgun proteomics

Tandem mass spectrometry combined with database searching allows high throughput identification of peptides in shotgun proteomics. However, validating database search results, a problem with a lot of solutions proposed, is still advancing in some aspects, such as the sensitivity, specificity, and generalizability of the validation algorithms. Here a Bayesian nonparametric (BNP) model for the validation of database search results was developed that incorporates several popular techniques in statistical learning, including the compression of feature space with a linear discriminant function, the flexible nonparametric probability density function estimation for the variable probability structure in complex problem, and the Bayesian method to calculate the posterior probability. Importantly the BNP model is compatible with the popular target-decoy database search strategy naturally. We tested the BNP model on standard proteins and real, complex sample data sets from multiple MS platforms and compared it with Peptide-Prophet, the cutoff-based method, and a simple nonparametric method (proposed by us previously). The performance of the BNP model was shown to be superior for all data sets searched on sensitivity and generalizability. Some high quality matches that had been filtered out by other methods were detected and assigned with high probability by the BNP model. Thus, the BNP model could be able to validate the database search results effectively and extract more information from MS/MS data.

Peptizer, a tool for assessing false positive peptide identifications and manually validating selected results

False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics. They occur in regions where the score distributions of true positives and true negatives overlap. Removal of these false positive identifications necessarily involves a trade-off between sensitivity and specificity. Existing postprocessing tools typically rely on a fixed or semifixed set of assumptions in their attempts to optimize both the sensitivity and the specificity of peptide and protein identification using MS/MS spectra. Because of the expanding diversity in available proteomics technologies, however, these postprocessing tools often struggle to adapt to emerging technology-specific peculiarity. Here we present a novel tool named Peptizer that solves this adaptability issue by making use of pluggable assumptions. This research-oriented postprocessing tool also includes a graphical user interface to perform efficient manual validation of suspect identifications for optimal sensitivity recovery. Peptizer is open source software under the Apache2 license and is written in Java.

Toward high-throughput and reliable peptide identification via MS/MS spectra

One fundamental problem in proteomics study is to identify proteins and determine their expression levels in cells. Coupled with advanced liquid chromatography, tandem mass spectrometry has become the standard tool for peptide sequencing. In the past decade, many different algorithms and software packages have been developed to support high-throughput proteomics studies. This chapter reviews and compares the computational methods and software for the interpretation of tandem mass spectra. We also present techniques to assess the reliability of peptide identification. Finally, future directions and new research paradigms in tandem mass spectrometry are discussed.

A nonparametric model for quality control of database search results in shotgun proteomics

BACKGROUND

Analysis of complex samples with tandem mass spectrometry (MS/MS) has become routine in proteomic research. However, validation of database search results creates a bottleneck in MS/MS data processing. Recently, methods based on a randomized database have become popular for quality control of database search results. However, a consequent problem is the ignorance of how to combine different database search scores to improve the sensitivity of randomized database methods.

RESULTS

In this paper, a multivariate nonlinear discriminate function (DF) based on the multivariate nonparametric density estimation technique was used to filter out false-positive database search results with a predictable false positive rate (FPR). Application of this method to control datasets of different instruments (LCQ, LTQ, and LTQ/FT) yielded an estimated FPR close to the actual FPR. As expected, the method was more sensitive when more features were used. Furthermore, the new method was shown to be more sensitive than two commonly used methods on 3 complex sample datasets and 3 control datasets.

CONCLUSION

Using the nonparametric model, a more flexible DF can be obtained, resulting in improved sensitivity and good FPR estimation. This nonparametric statistical technique is a powerful tool for tackling the complexity and diversity of datasets in shotgun proteomics.

MascotDatfile: An open-source library to fully parse and analyse MASCOT MS/MS search results

MS-based protein identification is an important part of both gel-based and gel-free proteome studies. The MASCOT search engine (http://www.matrixscience.com) provides one of the most popular automated algorithms for this task. Here we present an open-source software library written in Java that parses raw MASCOT results into an easily accessible and fully functional object model (http://genesis.ugent.be/MascotDatfile). Several scripts based on this library are provided as examples, allowing direct automation of important routine tasks involved in processing peptide identifications.

Human plasma proteome analysis by reversed sequence database search and molecular weight correlation based on a bacterial proteome analysis

In shotgun proteomics, proteins can be fractionated by 1-D gel electrophoresis and digested into peptides, followed by liquid chromatography to separate the peptide mixture. Mass spectrometry generates hundreds of thousands of tandem mass spectra from these fractions, and proteins are identified by database searching. However, the search scores are usually not sufficient to distinguish the correct peptides. In this study, we propose a confident protein identification method for high-throughput analysis of human proteome. To build a filtering protocol in database search, we chose Pseudomonas putida KT2440 as a reference because this bacterial proteome contains fewer modifications and is simpler than the human proteome. First, the P. putida KT2440 proteome was filtered by reversed sequence database search and correlated by the molecular weight in 1-D-gel band positions. The characterization protocol was then applied to determine the criteria for clustering of the human plasma proteome into three different groups. This protein filtering method, based on bacterial proteome data analysis, represents a rapid way to generate higher confidence protein list of the human proteome, which includes some of heavily modified and cleaved proteins.

Concanavalin A-captured glycoproteins in healthy human urine

Both the urinary proteome and its glycoproteome can reflect human health status, and more directly, functions of kidney and urinary tracts. Because the high abundance protein albumin is not N-glycosylated, the urine N-glycoprotein enrichment procedure could deplete it, and urine proteome could thus provide a more detailed protein profile in addition to glycosylation information especially when albuminuria occurs in some kidney diseases. In terms of describing the details of urinary proteins, the urine glycoproteome is even a better choice than the proteome itself. Pooled urine samples from healthy volunteers were collected and acetone-precipitated for proteins. N-Linked glycoproteins enriched with concanavalin A affinity purification were separated and analyzed by SDS-PAGE-reverse phase LC/MS/MS or two-dimensional LC/MS/MS. A total of 225 urinary proteins were identified based on two-hit criteria with reliability over 97% for each peptide. Among these proteins, 94 were identified in previous urine proteome works, 150 were annotated as glycoproteins in Swiss-Prot, and 43 were predicted as glycoproteins by NetNGlyc 1.0. A number of known biomarkers and disease-related glycoproteins were identified. Because changes in protein quantity or the glycosylation status can lead to changes in the concanavalin A-captured glycoprotein profile, specific urine glycoproteome patterns might be observed for specific pathological conditions as multiplex urinary biomarkers. Knowledge of the urine glycoproteome is important in understanding kidney and body function.

Rapid enrichment of bioactive milk proteins and iterative, consolidated protein identification by multidimensional protein identification technology

Direct injection of complex protein mixtures, e.g. those derived from crude biological fluids, is often incompatible with conventional LC supports, because of column clogging and rapid deterioration of chromatographic performance. In this paper, we report the use of restricted access media to rapidly enrich and fractionate human breast milk. This resin, combining size exclusion and anion exchange functionalities, yielded a fraction enriched in soluble CD14 and showing specific sCD14-dependant activity. This fraction was split into five aliquots, which were individually characterized using multidimensional protein identification technology. Reproducibility of the results was addressed by analysing and comparing five datasets using different protein identification tools available within the Sequest software. Furthermore, a comparison of three major releases of the Ensembl human protein database was performed to examine the effect of database updates on our results. We report here the benefit of repeated analysis of aliquots of the same fraction: first to increase the confidence in peptide identification by repeated confirmation in several aliquots; and second to assess experimental reproducibility. We demonstrate furthermore the effect of database modifications on the results and the importance of constantly re-analysing data with new releases to keep them consistent and up to date with the latest protein identities and predictions available.

AMASS: Software for Automatically Validating the Quality of MS/MS Spectrum from SEQUEST Results

Time-consuming and experience-dependent manual validations of tandem mass spectra are usually applied to SEQUEST results. This inefficient method has become a significant bottleneck for MS/MS data processing. Here we introduce a program AMASS (advanced mass spectrum screener), which can filter the tandem mass spectra of SEQUEST results by measuring the match percentage of high-abundant ions and the continuity of matched fragment ions in b, y series. Compared with Xcorr and DeltaCn filter, AMASS can increase the number of positives and reduce the number of negatives in 22 datasets generated from 18 known protein mixtures. It effectively removed most noisy spectra, false interpretations, and about half of poor fragmentation spectra, and AMASS can work synergistically with Rscore filter. We believe the use of AMASS and Rscore can result in a more accurate identification of peptide MS/MS spectra and reduce the time and energy for manual validation.

Current literature in mass spectrometry

In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of mass spectrometry. Each bibliography is divided into 11 sections: 1 Books, Reviews & Symposia; 2 Instrumental Techniques & Methods; 3 Gas Phase Ion Chemistry; 4 Biology/Biochemistry: Amino Acids, Peptides & Proteins; Carbohydrates; Lipids; Nucleic Acids; 5 Pharmacology/Toxicology; 6 Natural Products; 7 Analysis of Organic Compounds; 8 Analysis of Inorganics/Organometallics; 9 Surface Analysis; 10 Environmental Analysis; 11 Elemental Analysis. Within each section, articles are listed in alphabetical order with respect to author (5 Weeks journals ‐ Search completed at 8th. Sept. 2004)