SQID: an intensity-incorporated protein identification algorithm for tandem mass spectrometry

To interpret LC-MS/MS data in proteomics, most popular protein identification algorithms primarily use predicted fragment m/z values to assign peptide sequences to fragmentation spectra. The intensity information is often undervalued, because it is not as easy to predict and incorporate into algorithms. Nevertheless, the use of intensity to assist peptide identification is an attractive prospect and can potentially improve the confidence of matches and generate more identifications. On the basis of our previously reported study of fragmentation intensity patterns, we developed a protein identification algorithm, SeQuence IDentfication (SQID), that makes use of the coarse intensity from a statistical analysis. The scoring scheme was validated by comparing with Sequest and X!Tandem using three data sets, and the results indicate an improvement in the number of identified peptides, including unique peptides that are not identified by Sequest or X!Tandem. The software and source code are available under the GNU GPL license at http://quiz2.chem.arizona.edu/wysocki/bioinformatics.htm.     

In-gel isoelectric focusing of peptides as a tool for improved protein identification

In the analysis of proteins in complex samples, pre-fractionation is imperative to obtain the necessary depth in the number of reliable protein identifications by mass spectrometry. Here we explore isoelectric focusing of peptides (peptide IEF) as an effective fractionation step that at the same time provides the added possibility to eliminate spurious peptide identifications by filtering for pI. Peptide IEF in IPG strips is fast and sharply confines peptides to their pI. We have evaluated systematically the contribution of pI filtering and accurate mass measurements on the total number of protein identifications in a complex protein mixture (Drosophila nuclear extract). At the same time, by varying Mascot identification cutoff scores, we have monitored the false positive rate among these identifications by searching reverse protein databases. From mass spectrometric analyses at low mass accuracy using an LTQ ion trap, false positive rates can be minimized by filtering of peptides not focusing at their expected pI. Analyses using an LTQ-FT mass spectrometer delivers low false positive rates by itself due to the high mass accuracy. In a direct comparison of peptide IEF with SDS-PAGE as a pre-fractionation step, IEF delivered 25% and 43% more proteins when identified using FT-MS and LTQ-MS, respectively. Cumulatively, 2190 non redundant proteins were identified in the Drosophila nuclear extract at a false positive rate of 0.5%. Of these, 1751 proteins (80%) were identified after peptide IEF and FT-MS alone. Overall, we show that peptide IEF allows to increase the confidence level of protein identifications, and is more sensitive than SDS-PAGE.     

Quality classification of tandem mass spectrometry data

Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. RESULTS: We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.     

A computational method for assessing peptide- identification reliability in tandem mass spectrometry analysis with SEQUEST

High-throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides by a database search and then by combining the peptide hits for protein identification. One of the popular tools used for the database search is SEQUEST. Peptide identification is carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence, the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for normalizing SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.     

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.     

Application of peptide LC retention time information in a discriminant function for peptide identification by tandem mass spectrometry

We describe the application of a peptide retention time reversed phase liquid chromatography (RPLC) prediction model previously reported (Petritis et al. Anal. Chem. 2003, 75, 1039) for improved peptide identification. The model uses peptide sequence information to generate a theoretical (predicted) elution time that can be compared with the observed elution time. Using data from a set of known proteins, the retention time parameter was incorporated into a discriminant function for use with tandem mass spectrometry (MS/MS) data analyzed with the peptide/protein identification program SEQUEST. For singly charged ions, the number of confident identifications increased by 12% when the elution time metric is included compared to when mass spectral data is the sole source of information in the context of a Drosophila melanogaster database. A 3-4% improvement was obtained for doubly and triply charged ions for the same biological system. Application to the larger Rattus norvegicus (rat) and human proteome databases resulted in an 8-9% overall increase in the number of confident identifications, when both the discriminant function and elution time are used. The effect of adding "runner-up" hits (peptide matches that are not the highest scoring for a spectra) from SEQUEST is also explored, and we find that the number of confident identifications is further increased by 1% when these hits are also considered. Finally, application of the discriminant functions derived in this work with approximately 2.2 million spectra from over three hundred LC-MS/MS analyses of peptides from human plasma protein resulted in a 16% increase in confident peptide identifications (9022 vs 7779) using elution time information. Further improvements from the use of elution time information can be expected as both the experimental control of elution time reproducibility and the predictive capability are improved.     

Analysis of the resolution limitations of peptide identification algorithms

Proteome identification using peptide-centric proteomics techniques is a routinely used analysis technique. One of the most powerful and popular methods for the identification of peptides from MS/MS spectra is protein database matching using search engines. Significance thresholding through false discovery rate (FDR) estimation by target/decoy searches is used to ensure the retention of predominantly confident assignments of MS/MS spectra to peptides. However, shortcomings have become apparent when such decoy searches are used to estimate the FDR. To study these shortcomings, we here introduce a novel kind of decoy database that contains isobaric mutated versions of the peptides that were identified in the original search. Because of the supervised way in which the entrapment sequences are generated, we call this a directed decoy database. Since the peptides found in our directed decoy database are thus specifically designed to look quite similar to the forward identifications, the limitations of the existing search algorithms in making correct calls in such strongly confusing situations can be analyzed. Interestingly, for the vast majority of confidently identified peptide identifications, a directed decoy peptide-to-spectrum match can be found that has a better or equal match score than the forward match score, highlighting an important issue in the interpretation of peptide identifications in present-day high-throughput proteomics.     

Peptide Finder: mapping measured molecular masses to peptides and proteins

The identification of unknown amino acid sequences of peptides as well as protein identification is of great significance in proteomics. Here, we present a publicly available web application that facilitates a high resolution mapping of measured molecular masses to peptides and proteins, irrespectively of the enzyme/digestion method used. Furthermore, multi-filtering may be applied in terms of measured mass tolerance, molecular mass and isoelectric point range as well as pattern matching to refine the results. This approach serves complementary to the existing solutions for protein identification and gives insights in novel peptides discovery and protein identification at the cases where the identification scores from the other approaches may be below significance threshold. Peptide Finder has been proven useful in proteomics procedures with experimental data from MALDI-TOF. AVAILABILITY: Peptide Finder web-application is available at http://bioserver-1.bioacademy.gr/Bioserver/PeptideFinder/.     

Characterisation of global protein expression by two-dimensional electrophoresis and mass spectrometry: proteomics of Toxoplasma gondii.

The development of tools for the analysis of global gene expression is vital for the optimal exploitation of the data on parasite genomes that are now being generated in abundance. Recent advances in two-dimensional electrophoresis (2-DE), mass spectrometry and bioinformatics have greatly enhanced the possibilities for mapping and characterisation of protein populations. We have employed these developments in a proteomics approach for the analysis of proteins expressed in the tachyzoite stage of Toxoplasma gondii. Over 1000 polypeptides were reproducibly separated by high-resolution 2-DE using the pH ranges 4-7 and 6-11. Further separations using narrow range gels suggest that at least 3000-4000 polypeptides should be resolvable by 2-DE using multiple single pH unit gels. Mass spectrometry was used to characterise a variety of protein spots on the 2-DE gels. Peptide mass fingerprints, acquired by matrix-assisted laser desorption/ionisation-(MALDI) mass spectrometry, enabled unambiguous protein identifications to be made where full gene sequence information was available. However, interpretation of peptide mass fingerprint data using the T. gondii expressed sequence tag (EST) database was less reliable. Peptide fragmentation data, acquired by post-source decay mass spectrometry, proved a more successful strategy for the putative identification of proteins using the T. gondii EST database and protein databases from other organisms. In some instances, several protein spots appeared to be encoded by the same gene, indicating that post-translational modification and/or alternative splicing events may be a common feature of functional gene expression in T. gondii. The data demonstrate that proteomic analyses are now viable for T. gondii and other protozoa for which there are good EST databases, even in the absence of complete genome sequence. Moreover, proteomics is of great value in interpreting and annotating EST databases.     

MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines

Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.     

Randomized sequence databases for tandem mass spectrometry peptide and protein identification

Tandem mass spectrometry (MS/MS) combined with database searching is currently the most widely used method for high-throughput peptide and protein identification. Many different algorithms, scoring criteria, and statistical models have been used to identify peptides and proteins in complex biological samples, and many studies, including our own, describe the accuracy of these identifications, using at best generic terms such as "high confidence." False positive identification rates for these criteria can vary substantially with changing organisms under study, growth conditions, sequence databases, experimental protocols, and instrumentation; therefore, study-specific methods are needed to estimate the accuracy (false positive rates) of these peptide and protein identifications. We present and evaluate methods for estimating false positive identification rates based on searches of randomized databases (reversed and reshuffled). We examine the use of separate searches of a forward then a randomized database and combined searches of a randomized database appended to a forward sequence database. Estimated error rates from randomized database searches are first compared against actual error rates from MS/MS runs of known protein standards. These methods are then applied to biological samples of the model microorganism Shewanella oneidensis strain MR-1. Based on the results obtained in this study, we recommend the use of use of combined searches of a reshuffled database appended to a forward sequence database as a means providing quantitative estimates of false positive identification rates of peptides and proteins. This will allow researchers to set criteria and thresholds to achieve a desired error rate and provide the scientific community with direct and quantifiable measures of peptide and protein identification accuracy as opposed to vague assessments such as "high confidence."     

Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification

Here we present the theoretical and experimental evaluation of peptide isoelectric point as a method to aid in the identification of peptides from complex mixtures. Predicted pI values were found to match closely the experimentally obtained data, resulting in the development of a unique filter that lowers the effective false positive rate for peptide identification. Due to the reduction of the false positive rate, the cross-correlation parameters Xcorr and deltaCn from the SEQUEST program can be lowered resulting in 25% more peptide identifications. This approach was successfully applied to analysis of the soluble fraction of the E. coli proteome, where 417 proteins were identified from 1022 peptides using just 20 microg of material.     

Direct maximization of protein identifications from tandem mass spectra

The goal of many shotgun proteomics experiments is to determine the protein complement of a complex biological mixture. For many mixtures, most methodological approaches fall significantly short of this goal. Existing solutions to this problem typically subdivide the task into two stages: first identifying a collection of peptides with a low false discovery rate and then inferring from the peptides a corresponding set of proteins. In contrast, we formulate the protein identification problem as a single optimization problem, which we solve using machine learning methods. This approach is motivated by the observation that the peptide and protein level tasks are cooperative, and the solution to each can be improved by using information about the solution to the other. The resulting algorithm directly controls the relevant error rate, can incorporate a wide variety of evidence and, for complex samples, provides 18-34% more protein identifications than the current state of the art approaches.     

Integrated approach for manual evaluation of peptides identified by searching protein sequence databases with tandem mass spectra

Quantitative proteomics relies on accurate protein identification, which often is carried out by automated searching of a sequence database with tandem mass spectra of peptides. When these spectra contain limited information, automated searches may lead to incorrect peptide identifications. It is therefore necessary to validate the identifications by careful manual inspection of the mass spectra. Not only is this task time-consuming, but the reliability of the validation varies with the experience of the analyst. Here, we report a systematic approach to evaluating peptide identifications made by automated search algorithms. The method is based on the principle that the candidate peptide sequence should adequately explain the observed fragment ions. Also, the mass errors of neighboring fragments should be similar. To evaluate our method, we studied tandem mass spectra obtained from tryptic digests of E. coli and HeLa cells. Candidate peptides were identified with the automated search engine Mascot and subjected to the manual validation method. The method found correct peptide identifications that were given low Mascot scores (e.g., 20-25) and incorrect peptide identifications that were given high Mascot scores (e.g., 40-50). The method comprehensively detected false results from searches designed to produce incorrect identifications. Comparison of the tandem mass spectra of synthetic candidate peptides to the spectra obtained from the complex peptide mixtures confirmed the accuracy of the evaluation method. Thus, the evaluation approach described here could help boost the accuracy of protein identification, increase number of peptides identified, and provide a step toward developing a more accurate next-generation algorithm for protein identification.     

Proteomic profiling and protein identification by MALDI-TOF mass spectrometry in unsequenced parasitic nematodes

Lack of genomic sequence data and the relatively high cost of tandem mass spectrometry have hampered proteomic investigations into helminths, such as resolving the mechanism underpinning globally reported anthelmintic resistance. Whilst detailed mechanisms of resistance remain unknown for the majority of drug-parasite interactions, gene mutations and changes in gene and protein expression are proposed key aspects of resistance. Comparative proteomic analysis of drug-resistant and -susceptible nematodes may reveal protein profiles reflecting drug-related phenotypes. Using the gastro-intestinal nematode, Haemonchus contortus as case study, we report the application of freely available expressed sequence tag (EST) datasets to support proteomic studies in unsequenced nematodes. EST datasets were translated to theoretical protein sequences to generate a searchable database. In conjunction with matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF-MS), Peptide Mass Fingerprint (PMF) searching of databases enabled a cost-effective protein identification strategy. The effectiveness of this approach was verified in comparison with MS/MS de novo sequencing with searching of the same EST protein database and subsequent searches of the NCBInr protein database using the Basic Local Alignment Search Tool (BLAST) to provide protein annotation. Of 100 proteins from 2-DE gel spots, 62 were identified by MALDI-TOF-MS and PMF searching of the EST database. Twenty randomly selected spots were analysed by electrospray MS/MS and MASCOT Ion Searches of the same database. The resulting sequences were subjected to BLAST searches of the NCBI protein database to provide annotation of the proteins and confirm concordance in protein identity from both approaches. Further confirmation of protein identifications from the MS/MS data were obtained by de novo sequencing of peptides, followed by FASTS algorithm searches of the EST putative protein database. This study demonstrates the cost-effective use of available EST databases and inexpensive, accessible MALDI-TOF MS in conjunction with PMF for reliable protein identification in unsequenced organisms.     

Result-driven strategies for protein identification and quantitation--a way to optimize experimental design and derive reliable results

Uni- or multidimensional microcapillary liquid chromatography (microLC) matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS) approaches have gained significant attention for quantifying and identifying proteins in complex biological samples. The off-line coupling of microLC with MS quantitation and MS/MS identification methods makes new result-dependent workflows possible. A relational database is used to store the results from multiple high performance liquid chromatography runs, including information about MALDI plate positions, and both peptide and protein quantitations, and identifications. Unlike electrospray methodology, where all the decisions about which peptide to fragment, must be made during peptide fractionations, in the MALDI experiments the samples are effectively "frozen in time". Therefore, additional MS and MS/MS spectra can be acquired, to promote more accurate quantitation or additional identifications until reliable results are derived that meet experimental design criteria. In the case of what can be designated the expression-dependent workflow, quantitation can be detached from identification and only peak pairs with biological relevant expression changes can be selected for further MS/MS analyses. Alternatively, additional MS/MS data can be acquired to confirm tentative peptide mass fingerprint hits in what is designated a search result-dependent workflow. In the MS data-dependent workflow, the goal is to collect as many meaningful spectra as possible by judiciously adjusting the acquisition parameters based on characteristics of the parent masses. This level of sophistication requires the development of innovative algorithms for these three result-dependent workflows that make MS and MS/MS analysis more efficient and also add confidence to experimental results.     

Improved strategies for rapid identification of chemically cross-linked peptides using protein interaction reporter technology

Protein interaction reporter (PIR) technology can enable identification of in vivo protein interactions with the use of specialized chemical cross-linkers, liquid chromatography, and high-resolution mass spectrometry. PIR-cross-linkers contain labile bonds that are specifically fragmented under low energy collision or photodissociation conditions in the mass spectrometer source, thus releasing cross-linked peptides. Successful analysis of PIR-cross-linked proteins requires the use of expected mathematical relationships between cross-linked complexes and released peptides after fragmentation of the labile PIR bonds. Presented here is a next-generation software tool, BLinks, for use in the analysis and identification of PIR-cross-linked proteins. BLinks is an advancement beyond our previous efforts by incorporation of chromatographic profiles that must match between cross-linked complexes and released peptides to enable estimation of p-values to help filter true relationships from complex data sets. Additionally, BLinks was used to incorporate Mascot database searching results from subsequent MS/MS analysis of the released peptides to facilitate identification of cross-linked proteins. BLinks was used in the analysis of human serum albumin, and 46 interpeptide relationships were found spanning 30 proximal residues with a 2.2% false discovery rate. BLinks was also used to track peptides involved in multiple, coeluting relationships that make accurate identification of protein interactions difficult. An additional 10 interpeptide relationships were identified despite poor correlation using the profiling tools provided with BLinks. Additionally, BLinks can be used to globally map all interpeptide relationships from the data analysis and customize subsequent analysis to target specific peptides of interest, thus making it a useful tool for both discovery of protein interactions and mapping protein topology.     

MudPIT: multidimensional protein identification technology

Large-scale biology emerged out of the efforts to sequence genomes of important organisms. Based on resources created by whole genome sequencing, large-scale analyses of messenger RNA (mRNA) and protein expression are now possible. With the availability of large amounts of genomic sequence information, a convenient method for the identification and analysis of proteins based on proteolytic digestion into peptides emerged. Processes to fragment peptides using collision-activated dissociation (CAD) in tandem mass spectrometers and computer algorithms to match the tandem mass spectra of peptides to sequences in databases enable rapid identification of amino acid sequences, and hence proteins, present in mixtures. The inherent complexity of the peptide mixtures has necessitated improvements in methodology for mass spectrometry (MS) analysis of peptides.     

Enhanced peptide identification by electron transfer dissociation using an improved Mascot Percolator

Peptide identification using tandem mass spectrometry is a core technology in proteomics. Latest generations of mass spectrometry instruments enable the use of electron transfer dissociation (ETD) to complement collision induced dissociation (CID) for peptide fragmentation. However, a critical limitation to the use of ETD has been optimal database search software. Percolator is a post-search algorithm, which uses semi-supervised machine learning to improve the rate of peptide spectrum identifications (PSMs) together with providing reliable significance measures. We have previously interfaced the Mascot search engine with Percolator and demonstrated sensitivity and specificity benefits with CID data. Here, we report recent developments in the Mascot Percolator V2.0 software including an improved feature calculator and support for a wider range of ion series. The updated software is applied to the analysis of several CID and ETD fragmented peptide data sets. This version of Mascot Percolator increases the number of CID PSMs by up to 80% and ETD PSMs by up to 60% at a 0.01 q-value (1% false discovery rate) threshold over a standard Mascot search, notably recovering PSMs from high charge state precursor ions. The greatly increased number of PSMs and peptide coverage afforded by Mascot Percolator has enabled a fuller assessment of CID/ETD complementarity to be performed. Using a data set of CID and ETcaD spectral pairs, we find that at a 1% false discovery rate, the overlap in peptide identifications by CID and ETD is 83%, which is significantly higher than that obtained using either stand-alone Mascot (69%) or OMSSA (39%). We conclude that Mascot Percolator is a highly sensitive and accurate post-search algorithm for peptide identification and allows direct comparison of peptide identifications using multiple alternative fragmentation techniques.