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."     

An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates

The relevance of libraries of annotated MS/MS spectra is growing with the amount of proteomic data generated in high-throughput experiments. These reference libraries provide a fast and accurate way to identify newly acquired MS/MS spectra. In the context of multiple hypotheses testing, the control of the number of false-positive identifications expected in the final result list by means of the calculation of the false discovery rate (FDR). In a classical sequence search where experimental MS/MS spectra are compared with the theoretical peptide spectra calculated from a sequence database, the FDR is estimated by searching randomized or decoy sequence databases. Despite on-going discussion on how exactly the FDR has to be calculated, this method is widely accepted in the proteomic community. Recently, similar approaches to control the FDR of spectrum library searches were discussed. We present in this paper a detailed analysis of the similarity between spectra of distinct peptides to set the basis of our own solution for decoy library creation (DeLiberator). It differs from the previously published results in some key points, mainly in implementing new methods that prevent decoy spectra from being too similar to the original library spectra while keeping important features of real MS/MS spectra. Using different proteomic data sets and library creation methods, we evaluate our approach and compare it with alternative methods.     

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC‐MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re‐assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.     

Experiment-specific estimation of peptide identification probabilities using a randomized database

Determining the error rate for peptide and protein identification accurately and reliably is necessary to enable evaluation and crosscomparisons of high throughput proteomics experiments. Currently, peptide identification is based either on preset scoring thresholds or on probabilistic models trained on datasets that are often dissimilar to experimental results. The false discovery rates (FDR) and peptide identification probabilities for these preset thresholds or models often vary greatly across different experimental treatments, organisms, or instruments used in specific experiments. To overcome these difficulties, randomized databases have been used to estimate the FDR. However, the cumulative FDR may include low probability identifications when there are a large number of peptide identifications and exclude high probability identifications when there are few. To overcome this logical inconsistency, this study expands the use of randomized databases to generate experiment-specific estimates of peptide identification probabilities. These experiment-specific probabilities are generated by logistic and Loess regression models of the peptide scores obtained from original and reshuffled database matches. These experiment-specific probabilities are shown to very well approximate "true" probabilities based on known standard protein mixtures across different experiments. Probabilities generated by the earlier Peptide_Prophet and more recent LIPS models are shown to differ significantly from this study's experiment-specific probabilities, especially for unknown samples. The experiment-specific probabilities reliably estimate the accuracy of peptide identifications and overcome potential logical inconsistencies of the cumulative FDR. This estimation method is demonstrated using a Sequest database search, LIPS model, and a reshuffled database. However, this approach is generally applicable to any search algorithm, peptide scoring, and statistical model when using a randomized database.     

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.     

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 high accuracy, quantitative proteomics data in the MaxQB database

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.     

A bioinformatics workflow for variant peptide detection in shotgun proteomics

Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or cancer-related variations. Twenty-three out of 26 variants randomly selected from the 81 were confirmed by genomic sequencing. We further applied the workflow on data sets from three individual colorectal tumor specimens. A total of 204 distinct variant peptides were detected, and five carried known cancer-related mutations. Each individual showed a specific pattern of cancer-related mutations, suggesting potential use of this type of information for personalized medicine. Compatibility of the workflow has been tested with four popular database search engines including Sequest, Mascot, X!Tandem, and MyriMatch. In summary, we have developed a workflow that effectively uses existing genomic data to enable variant peptide detection in proteomics.     

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.     

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

In shotgun proteomics, high-throughput mass spectrometry experiments and the subsequent data analysis produce thousands to millions of hypothetical peptide identifications. The common way to estimate the false discovery rate (FDR) of peptide identifications is the target-decoy database search strategy, which is efficient and accurate for large datasets. However, the legitimacy of the target-decoy strategy for protein-modification-centric studies has rarely been rigorously validated. It is often the case that a global FDR is estimated for all peptide identifications including both modified and unmodified peptides, but that only a subgroup of identifications with a certain type of modification is focused on. As revealed recently, the subgroup FDR of modified peptide identifications can differ dramatically from the global FDR at the same score threshold, and thus the former, when it is of interest, should be separately estimated. However, rare modifications often result in a very small number of modified peptide identifications, which makes the direct separate FDR estimation inaccurate because of the inadequate sample size. This paper presents a method called the transferred FDR for accurately estimating the FDR of an arbitrary number of modified peptide identifications. Through flexible use of the empirical data from a target-decoy database search, a theoretical relationship between the subgroup FDR and the global FDR is made computable. Through this relationship, the subgroup FDR can be predicted from the global FDR, allowing one to avoid an inaccurate direct estimation from a limited amount of data. The effectiveness of the method is demonstrated with both simulated and real mass spectra.Post-translational modifications of proteins often play an essential role in the functions of proteins in cells (1). Abnormal modifications can change the properties of proteins, causing serious diseases (2). Because protein modifications are not directly encoded in the nucleotide sequences of organisms, they must be investigated at the protein level. In recent years, mass spectrometry technology has developed rapidly and has become the standard method for identifying proteins and their modifications in biological and clinical samples (3–5).In shotgun proteomics experiments, proteins are digested into peptide mixtures that are then analyzed via high-throughput liquid chromatography–tandem mass spectrometry, resulting in thousands to millions of tandem mass spectra. To identify the peptide sequences and the modifications on them, the spectra are commonly searched against a protein sequence database (6–8). During the database search, according to the variable modification types specified by the user, all forms of modified candidate peptides are enumerated. For each spectrum, candidate peptides (with possible modifications) from the database are scored according to the quality of their match to the input spectrum. However, for many reasons, the top-scored matches are not always correct peptide identifications, and therefore they must be filtered according to their identification scores (9). Finding an appropriate score threshold that gives the desired false discovery rate (FDR)¹ is a multiple hypothesis testing problem (10–12).At present, the common way to control the FDR of peptide identifications is an empirical approach called the target-decoy search strategy (13). In this strategy, in addition to the target protein sequences, the mass spectra are also searched against the same number of decoy protein sequences (e.g. reverse sequences of the target proteins). Because an incorrect identification has an equal chance of being a match to the target sequences or to the decoy sequences, the number of decoy matches above a score threshold can be used as an estimate of the number of random target matches, and the FDR (of the target matches) can be simply estimated as the number of decoy matches divided by the number of target matches. The target-decoy method, although simple and effective, is applicable to large datasets only. When the number of matches being evaluated is very small, this method becomes inaccurate because of the inadequate sample size (13, 14). Fortunately, for high-throughput proteomic mass spectrometry experiments, the number of mass spectra is always sufficiently large. Current efforts are mostly devoted to increasing the sensitivity of peptide identification at a given FDR by using various techniques such as machine learning (15).When the purpose of an experiment is to search for protein modifications, the problem of FDR estimation becomes somewhat complex. In fact, the legality of the target-decoy method for modification-centric studies was not rigorously discussed until very recently (16). At present, for multiple reasons, the identifications of modified and unmodified peptides are usually combined in the search result, and a global FDR is estimated for them in combination, with only a subgroup of identifications with specific modifications being focused on. However, the FDR of modified peptides can be significantly or even extremely different from that of unmodified peptides at the same score threshold. There are three reasons for this fact. First, because the spectra of modified peptides can have their own features (e.g. insufficient fragmentation or neutral losses), they can have different score distributions from those of unmodified peptides. Second, because the proportions of modified and unmodified peptides in the protein sample are different, the prior probabilities of obtaining a correct identification are different for modified and unmodified peptides. Third, because the proportions of modified and unmodified candidate peptides in the search space are different, the prior probabilities of obtaining an incorrect identification are also different for modified and unmodified peptides. Therefore, the modified peptide identifications of interest should be extracted from the identification result and subjected to a separate FDR estimation, as pointed out recently (16–18).The difficulty of separate FDR estimations is highlighted when there are too few modified peptide identifications to allow an accurate estimation. Many protein modifications are present in low abundance in cells but play important biological functions. These rare modifications have very low chances of being detected by mass spectrometry. A crucial question is, if very few modifications are identified from a very large dataset of mass spectra, can they be regarded as correct identifications? There was no answer to this question in the past in terms of FDR control. The target-decoy strategy loses its efficacy in such cases. For example, imagine that we have 10 modified peptide identifications above a score threshold after a search and that all of them are matches to target protein sequences. Can we say that the FDR of these identifications is zero (0/10)? If we decrease the score threshold slightly in such a way that one more modified peptide identification is included but find that that peptide is unfortunately a match to the decoy sequence, then can we say that the FDR of the top 10 target identifications is 10% (1/10)? It is clear here that the inclusion or exclusion of the 11th decoy identification has a great influence on the FDR estimated via the common target-decoy strategy. In fact, according to a binomial model (14), the probability that there are one or more false identifications among the top 10 target matches is as high as 0.5, which means that the real proportion of false discoveries has a half-chance of being no less than 10% (1/10). The appropriate way to estimate the FDR of the 10 target identifications is to give an appropriate estimate of the expected number of false identifications among them, and, most important, this estimate must not be an integer (e.g. 0 or 1) but can be a real number between 0 and 1. Note that single-spectrum significance measures (e.g. p values) are not appropriate for multiple hypothesis testing, not to mention that they can hardly be accurately computed in mass spectrometry.Separate FDR estimation for grouped multiple hypothesis testing is not new in statistics and bioinformatics. A typical example is the microarray data of mRNAs from different locations in an organism or from genes that are involved in different biological processes (19, 20). Efron (21) recently proposed a method for robust separate FDR estimation for small subgroups in the empirical Bayes framework. The underlying principle of this method is that if we can find the quantitative relationship between the subgroup FDR and the global FDR, the former can be indirectly inferred from the latter instead of being estimated from a limited amount of data. The relationship given by Efron is quite general and makes no use of domain-specific information. Furthermore, it requires known conditional probabilities of null and non-null cases given the score threshold. These probabilities are, however, unavailable in the modified peptide identification problem.This paper presents a dedicated method for accurate FDR estimation for rare protein modifications detected from large-scale mass spectral data. This method is based on a theoretical relationship between the subgroup FDR of modified peptide identifications and the global FDR of all peptide identifications. To make the relationship computable, the component factors in it are replaced by or fitted from the empirical data of the target-decoy database search results. Most important, the probability that an incorrect identification is an assignment of a modified peptide is approximated by a linear function of the score threshold. By extrapolation, this probability can be reliably obtained for high-tail scores that are suitable as thresholds. The proposed method was validated on both simulated and real mass spectra. To the best of our knowledge, this study is the first effort toward reliable FDR control of rare protein modifications identified from mass spectra. (Note that the error rate control for modification site location is another complex problem (22, 23) and is not the aim of this paper.)     

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.     

Analysing proteomic data

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.     

RT-SVR+q: a strategy for post-Mascot analysis using retention time and q value metric to improve peptide and protein identifications

Shotgun proteomics commonly utilizes database search like Mascot to identify proteins from tandem MS/MS spectra. False discovery rate (FDR) is often used to assess the confidence of peptide identifications. However, a widely accepted FDR of 1% sacrifices the sensitivity of peptide identification while improving the accuracy. This article details a machine learning approach combining retention time based support vector regressor (RT-SVR) with q value based statistical analysis to improve peptide and protein identifications with high sensitivity and accuracy. The use of confident peptide identifications as training examples and careful feature selection ensures high R values (>0.900) for all models. The application of RT-SVR model on Mascot results (p=0.10) increases the sensitivity of peptide identifications. q Value, as a function of deviation between predicted and experimental RTs (ΔRT), is used to assess the significance of peptide identifications. We demonstrate that the peptide and protein identifications increase by up to 89.4% and 83.5%, respectively, for a specified q value of 0.01 when applying the method to proteomic analysis of the natural killer leukemia cell line (NKL). This study establishes an effective methodology and provides a platform for profiling confident proteomes in more relevant species as well as a future investigation of accurate protein quantification.     

Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome

Large-scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, suggesting that the false positive rates are significantly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria were compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-1048), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-247). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications from mammalian proteomes without greatly decreasing the number of identifications.     

Experimental protein mixture for validating tandem mass spectral analysis

Several methods have been used to identify peptides that correspond to tandem mass spectra. In this work, we describe a data set of low energy tandem mass spectra generated from a control mixture of known protein components that can be used to evaluate the accuracy of these methods. As an example, these spectra were searched by the SEQUEST application against a human peptide sequence database. The numbers of resulting correct and incorrect peptide assignments were then determined. We show how the sensitivity and error rate are affected by the use of various filtering criteria based upon SEQUEST scores and the number of tryptic termini of assigned peptides.     

Validation of endogenous peptide identifications using a database of tandem mass spectra

The SwePep database is designed for endogenous peptides and mass spectrometry. It contains information about the peptides such as mass, pl, precursor protein and potential post-translational modifications. Here, we have improved and extended the SwePep database with tandem mass spectra, by adding a locally curated version of the global proteome machine database (GPMDB). In peptidomic experiment practice, many peptide sequences contain multiple tandem mass spectra with different quality. The new tandem mass spectra database in SwePep enables validation of low quality spectra using high quality tandem mass spectra. The validation is performed by comparing the fragmentation patterns of the two spectra using algorithms for calculating the correlation coefficient between the spectra. The present study is the first step in developing a tandem spectrum database for endogenous peptides that can be used for spectrum-to-spectrum identifications instead of peptide identifications using traditional protein sequence database searches.     

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

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

ProbIDtree: an automated software program capable of identifying multiple peptides from a single collision-induced dissociation spectrum collected by a tandem mass spectrometer

In MS/MS experiments with automated precursor ion, selection only a fraction of sequencing attempts lead to the successful identification of a peptide. A number of reasons may contribute to this situation. They include poor fragmentation of the selected precursor ion, the presence of modified residues in the peptide, mismatches with sequence databases, and frequently, the concurrent fragmentation of multiple precursors in the same CID attempt. Current database search engines are incapable of correctly assigning the sequences of multiple precursors to such spectra. We have developed a search engine, ProbIDtree, which can identify multiple peptides from a CID spectrum generated by the concurrent fragmentation of multiple precursor ions. This is achieved by iterative database searching in which the submitted spectra are generated by subtracting the fragment ions assigned to a tentatively matched peptide from the acquired spectrum and in which each match is assigned a tentative probability score. Tentatively matched peptides are organized in a tree structure from which their adjusted probability scores are calculated and used to determine the correct identifications. The results using MALDI-TOF-TOF MS/MS data demonstrate that multiple peptides can be effectively identified simultaneously with high confidence using ProbIDtree.     

Estimating the statistical significance of peptide identifications from shotgun proteomics experiments

We present a wrapper-based approach to estimate and control the false discovery rate for peptide identifications using the outputs from multiple commercially available MS/MS search engines. Features of the approach include the flexibility to combine output from multiple search engines with sequence and spectral derived features in a flexible classification model to produce a score associated with correct peptide identifications. This classification model score from a reversed database search is taken as the null distribution for estimating p-values and false discovery rates using a simple and established statistical procedure. Results from 10 analyses of rat sera on an LTQ-FT mass spectrometer indicate that the method is well calibrated for controlling the proportion of false positives in a set of reported peptide identifications while correctly identifying more peptides than rule-based methods using one search engine alone.