CHOMPER: a bioinformatic tool for rapid validation of tandem mass spectrometry search results associated with high-throughput proteomic strategies

Current efforts aimed at developing high-throughput proteomics focus on increasing the speed of protein identification. Although improvements in sample separation, enrichment, automated handling, mass spectrometric analysis, as well as data reduction and database interrogation strategies have done much to increase the quality, quantity and efficiency of data collection, significant bottlenecks still exist. Various separation techniques have been coupled with tandem mass spectrometric (MS/MS) approaches to allow a quicker analysis of complex mixtures of proteins, especially where a high number of unambiguous protein identifications are the exception, rather than the rule. MS/MS is required to provide structural / amino acid sequence information on a peptide and thus allow protein identity to be inferred from individual peptides. Currently these spectra need to be manually validated because: (a) the potential of false positive matches i.e., protein not in database, and (b) observed fragmentation trends may not be incorporated into current MS/MS search algorithms. This validation represents a significant bottleneck associated with high-throughput proteomic strategies. We have developed CHOMPER, a software program which reduces the time required to both visualize and confirm MS/MS search results and generate post-analysis reports and protein summary tables. CHOMPER extracts the identification information from SEQUEST MS/MS search result files, reproduces both the peptide and protein identification summaries, provides a more interactive visualization of the MS/MS spectra and facilitates the direct submission of manually validated identifications to a database.     

Added value for tandem mass spectrometry shotgun proteomics data validation through isoelectric focusing of peptides

A very popular approach in proteomics is the so-called "shotgun LC-MS/MS" strategy. In its mostly used form, a total protein digest is separated by ion exchange fractionation in the first dimension followed by off- or on-line RP LC-MS/MS. We replaced the first dimension by isoelectric focusing in the liquid phase using the Off-Gel device producing 15 fractions. As peptides are separated by their isoelectric point in the first dimension and hydrophobicity in the second, those experimentally derived parameters (pI and R(T)) can be used for the validation of potentially identified peptides. We applied this strategy to a cellular extract of Drosophila Kc167 cells and identified peptides with two different database search engines, namely PHENYX and SEQUEST, with PeptideProphet validation of the SEQUEST results. PHENYX returned 7582 potential peptide identifications and SEQUEST 7629. The SEQUEST results were reduced to 2006 identifications by validation with PeptideProphet. Validation of the PeptideProphet, SEQUEST and PHENYX results by pI and R(T) parameters confirmed 1837 PeptideProphet identifications while in the remainder of the SEQUEST results another 1130 peptides were found to be likely hits. The validation on PHENYX resulted in the fixation of a solid p-value threshold of <1 x 10(-04) that sets by itself the correct identification confidence to >95%, and a final count of 2034 highly confident peptide identifications was achieved after pI and R(T) validation. Although the PeptideProphet and PHENYX datasets have a very high confidence the overlap of common identifications was only at 79.4%, to be explained by the fact that data interpretation was done searching different protein databases with two search engines of different algorithms. The approach used in this study allowed for an automated and improved data validation process for shotgun proteomics projects producing MS/MS peptide identification results of very high confidence.     

Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC‐MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four‐protein mixture, the same four‐protein mixture spiked into a complex biological background, and a variety of other “system” type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.     

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.     

Optimization and use of peptide mass measurement accuracy in shotgun proteomics

Mass spectrometers that provide high mass accuracy such as FT-ICR instruments are increasingly used in proteomic studies. Although the importance of accurately determined molecular masses for the identification of biomolecules is generally accepted, its role in the analysis of shotgun proteomic data has not been thoroughly studied. To gain insight into this role, we used a hybrid linear quadrupole ion trap/FT-ICR (LTQ FT) mass spectrometer for LC-MS/MS analysis of a highly complex peptide mixture derived from a fraction of the yeast proteome. We applied three data-dependent MS/MS acquisition methods. The FT-ICR part of the hybrid mass spectrometer was either not exploited, used only for survey MS scans, or also used for acquiring selected ion monitoring scans to optimize mass accuracy. MS/MS data were assigned with the SEQUEST algorithm, and peptide identifications were validated by estimating the number of incorrect assignments using the composite target/decoy database search strategy. We developed a simple mass calibration strategy exploiting polydimethylcyclosiloxane background ions as calibrant ions. This strategy allowed us to substantially improve mass accuracy without reducing the number of MS/MS spectra acquired in an LC-MS/MS run. The benefits of high mass accuracy were greatest for assigning MS/MS spectra with low signal-to-noise ratios and for assigning phosphopeptides. Confident peptide identification rates from these data sets could be doubled by the use of mass accuracy information. It was also shown that improving mass accuracy at a cost to the MS/MS acquisition rate substantially lowered the sensitivity of LC-MS/MS analyses. The use of FT-ICR selected ion monitoring scans to maximize mass accuracy reduced the number of protein identifications by 40%.     

A new strategy to filter out false positive identifications of peptides in SEQUEST database search results

Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.     

YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

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.     

牛血清白蛋白胰蛋白酶酶解产物的色谱-质谱联用分析及其三种数据库搜寻鉴定方法的比较

利用反相高效液相色谱 (RP HPLC)和电喷雾串联质谱 (ESI MS MS)联用技术直接对模式蛋白分子 (牛血清白蛋白 ,BSA)的胰蛋白酶酶解产物进行分离和测定 .获得的一系列BSA酶解片段的一级 (MS)和二级 (MS MS)质谱数据经分析软件处理后 ,分别在不同处理和不同参数条件下 ,用 3种不同的方法通过网上蛋白质数据库进行蛋白质搜寻鉴定 .结果显示 ,3种搜寻法都能正确地鉴定该蛋白质 ,其中以利用MS数据的肽质量指纹谱搜寻法 (PMF法 )较为快捷方便 ,但鉴定结果易受数据处理和数据库搜寻鉴定时参数设置等因素的影响 ;利用未解析MS MS数据 (rawMS MSdata)的搜寻法可在较宽的搜寻参数变化范围内获得明确的鉴定结果 ;而借助从头测序 (denovosequencing)结果的序列搜寻法 (sequencequery)则显示出更高的专一性 ,利用较少酶解片段数据就能得到稳定和明确的鉴定结果 ,搜寻参数变化的影响很小 .就酶解条件、数据处理和搜寻参数设置对蛋白质鉴定结果的影响展开详细的讨论 ,为蛋白质组学研究中的数据处理和库搜寻鉴定积累了可借鉴的资料     

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.     

Developing the wool proteome

The wool proteome has been largely uncharted due to a lack of database coverage, poor protein extractability and dynamic range issues. Yet, investigating correlations between wool physical properties and protein content, or characterising UV-, heat- or processing-induced protein damage requires the availability of an identifiable and identified proteome.In this study we have achieved unprecedented wool proteome identification through a strategy of comprehensive data acquisition, iterative protein identification/validation and concurrent augmentation of the sequence database. Data acquisition comprised a range of different hyphenated MS techniques including LC–MS/MS, LC–MALDI, 2D-LC–MS/MS and SDS-PAGE LC–MS. Using iterative searching of databases and search result combination using ProteinScape, a systematic expansion of identifiable proteins in the sequence database was achieved. This was followed by extensive validation and rationalisation of the protein identifications. In total, 72 complete and 30 partial ovine-specific protein sequences were added to the database, and 113 wool proteins were identified.Enhanced access to ovine-specific protein identification and characterisation will facilitate all wool fibre protein chemistry and proteomics research.     

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."     

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.     

Glioblastoma cell secretome: analysis of three glioblastoma cell lines reveal 148 non-redundant proteins

Glioblastoma multiforme (GBM) is the most aggressive among human gliomas with poor prognosis. Study of tumor cell secretome - proteins secreted by cancer cell lines, is a powerful approach to discover potential diagnostic or prognostic biomarkers. Here we report, for the first time, proteins secreted by three GBM cell lines, HNGC2, LN229 and U87MG. Analysis of the conditioned media of these cell lines by LC-MS/MS using ESI-IT mass spectrometer (LTQ) resulted in the confident identification of 102, 119 and 64 proteins, respectively. Integration of the results from all the three cell lines lead to a dataset of 148 non-redundant proteins. Subcellular classification using Genome Ontology indicated that 42% of the proteins identified belonged to extracellular or membrane proteins, viz. Vinculin, Tenascin XB, SERPIN F1 and TIMP-1. 52 proteins matched with the secretomes of 11 major cancer types reported earlier whereas remaining 96 are unique to our study. 25 protein identifications from the dataset represent proteins related to GBM or other cancer tissues as per Human Protein Atlas; at least 22 are detectable in plasma, 11 of them being reported even in cerebrospinal fluid. Our study thus provides a valuable resource of GBM cell secretome with potential for further investigation as GBM biomarkers.     

Secreted proteins as a fundamental source for biomarker discovery

The proteins secreted by various cells (the secretomes) are a potential rich source of biomarkers as they reflect various states of the cells at real time and at given conditions. To have accessible, sufficient and reliable protein markers is desirable as they mark various stages of disease development and their presence/absence can be used for diagnosis, prognosis, risk stratification and therapeutic monitoring. As direct analysis of blood/plasma, a common and noninvasive patient screening method, can be difficult for candidate protein biomarker identification, the alternative/complementary approaches are required, one of them is the analysis of secretomes in cell conditioned media in vitro. As the proteins secreted by cells as a response to various stimuli are most likely secreted into blood/plasma, the identification and pre-selection of candidate protein biomarkers from cell secretomes with subsequent validation of their presence at higher levels in serum/plasma is a promising approach. In this review, we discuss the proteins secreted by three progenitor cell types (smooth muscle, endothelial and cardiac progenitor cells) and two adult cell types (neonatal rat ventrical myocytes and smooth muscle cells) which can be relevant to cardiovascular research and which have been recently published in the literature. We found, at least for secretome studies included in this review, that secretomes of progenitor and adult cells overlap by 48% but the secretomes are very distinct among progenitor cell themselves as well as between adult cells. In addition, we compared secreted proteins to protein identifications listed in the Human Plasma PeptideAtlas and in two reports with cardiovascular-related proteins and we performed the extensive literature search to find if any of these secreted proteins were identified in a biomarker study. As expected, many proteins have been identified as biomarkers in cancer but 18 proteins (out of 62) have been tested as biomarkers in cardiovascular diseases as well.     

Automatic validation of phosphopeptide identifications by the MS2/MS3 target-decoy search strategy

Manual checking is commonly employed to validate the phosphopeptide identifications from database searching of tandem mass spectra. It is very time-consuming and labor intensive as the number of phosphopeptide identifications increases greatly. In this study, a simple automatic validation approach was developed for phosphopeptide identification by combining consecutive stage mass spectrometry data and the target-decoy database searching strategy. Only phosphopeptides identified from both MS2 and its corresponding MS3 were accepted for further filtering, which greatly improved the reliability in phosphopeptide identification. Before database searching, the spectra were validated for charge state and neutral loss peak intensity, and then the invalid MS2/MS3 spectra were removed, which greatly reduced the database searching time. It was found that the sensitivity was significantly improved in MS2/MS3 strategy as the number of identified phosphopeptides was 2.5 times that obtained by the conventional filter-based MS2 approach. Because of the use of the target-decoy database, the false-discovery rate (FDR) of the identified phosphopeptides could be easily determined, and it was demonstrated that the determined FDR can precisely reflect the actual FDR without any manual validation stage.     

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.     

Gaining knowledge from previously unexplained spectra-application of the PTM-Explorer software to detect PTM in HUPO BPP MS/MS data

A novel software tool named PTM-Explorer has been applied to LC-MS/MS datasets acquired within the Human Proteome Organisation (HUPO) Brain Proteome Project (BPP). PTM-Explorer enables automatic identification of peptide MS/MS spectra that were not explained in typical sequence database searches. The main focus was detection of PTMs, but PTM-Explorer detects also unspecific peptide cleavage, mass measurement errors, experimental modifications, amino acid substitutions, transpeptidation products and unknown mass shifts. To avoid a combinatorial problem the search is restricted to a set of selected protein sequences, which stem from previous protein identifications using a common sequence database search. Prior to application to the HUPO BPP data, PTM-Explorer was evaluated on excellently manually characterized and evaluated LC-MS/MS data sets from Alpha-A-Crystallin gel spots obtained from mouse eye lens. Besides various PTMs including phosphorylation, a wealth of experimental modifications and unspecific cleavage products were successfully detected, completing the primary structure information of the measured proteins. Our results indicate that a large amount of MS/MS spectra that currently remain unidentified in standard database searches contain valuable information that can only be elucidated using suitable software tools.     

Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Liquid chromatography and tandem mass spectrometry (LC-MS/MS) has become the preferred method for conducting large-scale surveys of proteomes. Automated interpretation of tandem mass spectrometry (MS/MS) spectra can be problematic, however, for a variety of reasons. As most sequence search engines return results even for 'unmatchable' spectra, proteome researchers must devise ways to distinguish correct from incorrect peptide identifications. The target-decoy search strategy represents a straightforward and effective way to manage this effort. Despite the apparent simplicity of this method, some controversy surrounds its successful application. Here we clarify our preferred methodology by addressing four issues based on observed decoy hit frequencies: (i) the major assumptions made with this database search strategy are reasonable; (ii) concatenated target-decoy database searches are preferable to separate target and decoy database searches; (iii) the theoretical error associated with target-decoy false positive (FP) rate measurements can be estimated; and (iv) alternate methods for constructing decoy databases are similarly effective once certain considerations are taken into account.