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At least 19 recordsLinked to original sources
PubMedDate not supplied

Peptide identification using peptide amino acid attribute vectors.

We describe the theoretical basis for a peptide identification method wherein peptides are represented as vectors based on their amino acid composition and grouped into clusters. Unknown peptides are identified by finding the database cluster and peptide entries with the shortest Euclidian distance. We demonstrate that the amino acid composition of peptides is virtually as informative as the sequence and allows rapid peptide identification more accurately than peptide mass alone.

Algorithms↗
PubMed2004

Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data.

LC MS/MS has become an established technology in proteomic studies, and with the maturation of the technology the bottleneck has shifted from data generation to data validation and mining. To address this bottleneck we developed Experimental Peptide Identification Repository (EPIR), which is an integrated software platform for storage, validation, and mining of LC MS/MS-derived peptide evidence. EPIR is a cumulative data repository where precursor ions are linked to peptide assignments and protein associations returned by a search engine (e.g. Mascot, Sequest, or PepSea). Any number of datasets can be parsed into EPIR and subsequently validated and mined using a set of software modules that overlay the database. These include a peptide validation module, a protein grouping module, a generic module for extracting quantitative data, a comparative module, and additional modules for extracting statistical information. In the present study, the utility of EPIR and associated software tools is demonstrated on LC MS/MS data derived from a set of model proteins and complex protein mixtures derived from MCF-7 breast cancer cells. Emphasis is placed on the key strengths of EPIR, including the ability to validate and mine multiple combined datasets, and presentation of protein-level evidence in concise, nonredundant protein groups that are based on shared peptide evidence.

Breast Neoplasms↗
PubMedDate not supplied

Peptide identification using vectors of small fragment ions.

Traditionally, peptide identification using fragmentation spectra relies on extracting the maximum amount of information from spectra. Using different combinations of small ion masses, we show that identifying a small number of fragment ions in a spectrum is sufficient for peptide identification. We consider y2-, y3-, b2-, and b3-ions and find the combination of b2-y2 to be sufficient for many peptides. Adding either the y3- or the b3-ion increases specificity and allows reliable peptide identification in the human proteome. Fragmentation spectra and peptides are represented as n-dimensional vectors, where n is given by the number of fragment ions considered, and the peptide mass. The identification score is given by the Euclidian distance between the spectra and the matching peptide in n-dimensional space. We show that this approach, using minimal information, allows for precise and fast peptide identification.

Humans↗
PubMed2026

From Variability to Consensus: Rescoring Harmonizes Peptide Identification across Diverse Search Engines and Data Sets.

Peptide-spectrum match (PSM) rescoring has become standard in proteomics workflows, improving peptide identification accuracy across diverse search engines. Despite the availability of multiple rescoring strategies, systematic comparisons spanning several search engines, data sets, and database configurations remain limited. Here, we benchmarked seven publicly available search engines, evaluating standard target-decoy-based false discovery rate (FDR) estimation alongside Percolator, MS2Rescore, and Oktoberfest across four data sets acquired on different mass spectrometry platforms in data-dependent mode and searched against protein databases of varying size and composition. Rescoring substantially increased identification consensus and reduced variability between search engines, with prediction-based approaches yielding the largest gains. While database size had limited impact for human data sets, it significantly affected identification rates on a metaproteomic data set. Entrapment-based evaluation indicated generally adequate FDR control across methods, although prediction-based rescoring exhibited a higher tendency toward FDR underestimation in specific configurations. Overall, advanced rescoring strategies harmonize peptide identification outcomes across search engines, thereby enhancing robustness and comparability in proteomics analyses. However, careful feature selection and appropriate database choice remain essential to ensure reliable FDR control and optimal performance across diverse experimental settings.

Search Engine↗
PubMed1998

Brain natriuretic peptide: identification of a second natriuretic peptide in human aqueous humour.

AIMS/BACKGROUND: To measure aqueous humour levels of brain natriuretic peptide (BNP) and atrial natriuretic peptide (ANP) in humans. To compare peptide levels in glaucomatous and control eyes to test the hypothesis that these peptides are increased in glaucoma. BNP and ANP are cyclic endopeptides whose principal biological effects are natriuresis and vasodilatation. Experimental glaucoma in animal models results in elevated aqueous ANP. Intravenous ANP administration in both animals and humans causes lowering of intraocular pressure (IOP). There are equivocal data to support a role for ANP in IOP regulation in human eyes. There are as yet no published data on BNP in human aqueous humour. METHOD: This was a case-control study. Cases were primary open angle, pseudoexfoliation, and mixed mechanism glaucoma eyes undergoing trabeculectomy. Controls were cataract extraction eyes. There were 47 trabeculectomy eyes (44 patients) and 47 cataract extraction eyes (46 patients) matched for age, sex, race, systemic medications, and type of anaesthetic. 100-200 microliters of aqueous humour were aspirated by paracentesis as the first step in the surgical procedure. Peptide levels were later measured by radioimmunoassay. RESULTS: The presence of BNP and ANP in human aqueous humour was confirmed. BNP was present in higher concentrations than ANP. BNP levels tended to be greater in control eyes--glaucoma median 56.5 (range 0-3526.5) pg/ml versus control median 65.16 (range 0-1788) pg/ml (Wilcoxon signed rank test p = 0.78). ANP levels tended to be greater in glaucoma eyes than in controls: glaucoma median 3 (range 0-68.5) pg/ml versus control median 0 (range 0-60) pg/ml (Wilcoxon signed rank test p = 0.82). ANP and BNP were log linearly related in both groups (r glaucoma group = 0.961, r control group = 0.894). CONCLUSION: This is the first report of BNP and ANP in human aqueous humour. Peptide levels did not differ significantly between glaucoma and cataract extraction eyes. A linear relation between log BNP and ANP was found. Further studies are required to clarify the role of these peptides in aqueous humour production and IOP regulation.

Adult↗
PubMedDate not supplied

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.

Animals↗
PubMed2006

PepSeeker: a database of proteome peptide identifications for investigating fragmentation patterns.

Proteome science relies on bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. The resulting secondary ion mass spectra are compared with protein sequence databases in order to identify the amino acid sequence. Although these search tools (e.g. SEQUEST, Mascot, X!Tandem, Phenyx) are frequently successful, much is still not understood about the amino acid sequence patterns which promote/protect particular fragmentation pathways, and hence lead to the presence/absence of particular ions from different ion series. In order to advance this area, we have developed a database, PepSeeker (http://nwsr.smith.man.ac.uk/pepseeker), which captures this peptide identification and ion information from proteome experiments. The database currently contains >185,000 peptides and associated database search information. Users may query this resource to retrieve peptide, protein and spectral information based on protein or peptide information, including the amino acid sequence itself represented by regular expressions coupled with ion series information. We believe this database will be useful to proteome researchers wishing to understand gas phase peptide ion chemistry in order to improve peptide identification strategies. Questions can be addressed to j.selley@manchester.ac.uk.

Databases, Protein↗
PubMed2007

Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

In recent years, a variety of approaches have been developed using decoy databases to empirically assess the error associated with peptide identifications from large-scale proteomics experiments. We have developed an approach for calculating the expected uncertainty associated with false-positive rate determination using concatenated reverse and forward protein sequence databases. After explaining the theoretical basis of our model, we compare predicted error with the results of experiments characterizing a series of mixtures containing known proteins. In general, results from characterization of known proteins show good agreement with our predictions. Finally, we consider how these approaches may be applied to more complicated data sets, as when peptides are separated by charge state prior to false-positive determination.

Algorithms↗
PubMed2006

An SVM scorer for more sensitive and reliable peptide identification via tandem mass spectrometry.

Tandem mass spectrometry (MS/MS) has become increasingly important and indispensable in high-throughput proteomics for identifying complex protein mixtures. Database searching is the standard method to accomplish this purpose. A key sub-routine, peptide identification, is used to generate a list of candidate peptides from a protein database according to an experimental MS/MS spectrum, and then validate these candidate peptides for protein identification. Although currently there are many algorithms for peptide identification, most of them either lack an effective validation module or only validate the first-ranked peptide, thus leading to a low identification reliability or sensitivity. This paper proposes a new algorithm, named pepReap, to overcome the above drawbacks. It consists of a two-layered scoring scheme based on machine learning. The first layer is a rough scoring function which uses some simple and heuristic factors to measure the degree of the matches between an experimental MS/MS spectrum and the candidate peptides; thus a ranked list of candidate peptides is generated at a relatively low computational cost. The second layer is a fine scoring function which re-ranks the candidate peptides generated in the first layer and determines which one among them is the true positive. The fine scoring function was designed based on support vector machines (SVMs) using more comprehensive factors, such as the correlations between ions, the mass matching errors of fragment and peptide ions, etc. Consequently, the SVM classifier serves as not only a scorer but also a validation module. Experimental comparison with the popular SEQUEST algorithm coupled with threshold validation criteria on a reported dataset demonstrates that the pepReap algorithm achieves higher performance in terms of identification sensitivity with comparable precision.

Algorithms↗
PubMed2004

Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry.

MOTIVATION: The correlation among fragment ions in a tandem mass spectrum is crucial in reducing stochastic mismatches for peptide identification by database searching. Until now, an efficient scoring algorithm that considers the correlative information in a tunable and comprehensive manner has been lacking. RESULTS: This paper provides a promising approach to utilizing the correlative information for improving the peptide identification accuracy. The kernel trick, rooted in the statistical learning theory, is exploited to address this issue with low computational effort. The common scoring method, the tandem mass spectral dot product (SDP), is extended to the kernel SDP (KSDP). Experiments on a dataset reported previously demonstrate the effectiveness of the KSDP. The implementation on consecutive fragments shows a decrease of 10% in the error rate compared with the SDP. Our software tool, pFind, using a simple scoring function based on the KSDP, outperforms two SDP-based software tools, SEQUEST and Sonar MS/MS, in terms of identification accuracy. SUPPLEMENTARY INFORMATION: http://www.jdl.ac.cn/user/yfu/pfind/index.html

Algorithms↗
PubMed1992

Recognition of transition metal ions by peptides. Identification of specific metal-binding peptides in proteolytic digest maps by UV laser desorption time-of-flight mass spectrometry.

Metal-binding peptides in proteolytic digest maps have been identified by matrix-assisted UV laser desorption time-of-flight mass spectrometry (LDTOF-MS). The plasma and milk metal transport protein chosen to demonstrate this process, histidine-rich glycoprotein (HRG), was purified and then digested with trypsin; the cleavage products were analyzed by LDTOF-MS with dihydroxybenzoic acid as the matrix. The selective interaction of specific peptides with one or more Cu atoms was observed when Cu(II) ions were added to the digest mixture. At least one specific metal-binding peptide was identified by computerized sequence analysis using the molecular mass data and available cDNA sequence. These results demonstrate the first direct observation by mass spectrometry of differential peptide-metal ion interactions in protein digest maps. The ability to evaluate peptide-metal ion interactions, including stoichiometry, with less than 1 pmol of sample improves significantly our ability to identify metal binding domains in metal-binding proteins.

Amino Acid Sequence↗
PubMed2006

PRIDE: a public repository of protein and peptide identifications for the proteomics community.

PRIDE, the 'PRoteomics IDEntifications database' (http://www.ebi.ac.uk/pride) is a database of protein and peptide identifications that have been described in the scientific literature. These identifications will typically be from specific species, tissues and sub-cellular locations, perhaps under specific disease conditions. Any post-translational modifications that have been identified on individual peptides can be described. These identifications may be annotated with supporting mass spectra. At the time of writing, PRIDE includes the full set of identifications as submitted by individual laboratories participating in the HUPO Plasma Proteome Project and a profile of the human platelet proteome submitted by the University of Ghent in Belgium. By late 2005 PRIDE is expected to contain the identifications and spectra generated by the HUPO Brain Proteome Project. Proteomics laboratories are encouraged to submit their identifications and spectra to PRIDE to support their manuscript submissions to proteomics journals. Data can be submitted in PRIDE XML format if identifications are included or mzData format if the submitter is depositing mass spectra without identifications. PRIDE is a web application, so submission, searching and data retrieval can all be performed using an internet browser. PRIDE can be searched by experiment accession number, protein accession number, literature reference and sample parameters including species, tissue, sub-cellular location and disease state. Data can be retrieved as machine-readable PRIDE or mzData XML (the latter for mass spectra without identifications), or as human-readable HTML.

Databases, Protein↗
PubMed2006

Automated ultra-high-pressure multidimensional protein identification technology (UHP-MudPIT) for improved peptide identification of proteomic samples.

Multidimensional separation is one of the most successful approaches for proteomics studies that deal with complex samples. We have developed an automated ultra-high-pressure multidimensional liquid chromatography system that operates up to approximately 20 kpsi to improve separations and increase protein coverage from limited amount of samples. The reversed-phase gradient is operated in the constant-flow mode opposed to the constant-pressure mode, which is typical of previous ultra-high-pressure systems. In contrast to constant-pressure systems, the gradient shape is fully controllable and can be optimized for the type of samples to be run. The system also features fast sample loading/desalting using a vented column approach to improve sample throughput. This approach was validated on a soluble fraction from yeast lysate where we achieved approximately 30% more protein identifications using a 60-cm-long triphasic capillary column than with our traditional approach. Advantages of the use of a relatively long reversed-phase column (approximately 50 cm) for MudPIT-type experiments are also discussed.

Amino Acid Sequence↗
PubMed1999

Protein and Peptide Identification by Capillary Zone Electrophoresis-Tandem Mass Spectrometry.

A method for highly sensitive protein and peptide identification by capillary zone electrophoresis/electrospray ionization-tandem mass spectrometry (CZE/ESI/MS/MS) was described. A mixture of peptides Met-enkephalin and Leu-enkephalin was analyzed, and their amino acid sequences were identified, respectively, using CZE/MS/MS. Peptide mapping for a tryptic digest of horse cytochrome c was performed by the same method. The results indicated that almost all peptides generated by tryptic digestion were identified, and their sequences were interpreted by their collision-induced (CID) mass spectra. Applying the uninterpreted CID spectra of the protein digest to searching in sequence database by the SEQUEST program could identify proteins conveniently and rapidly. The samples consumed in the CZE/MS/MS experiments were at picomole level, so this method is quite suitable for quality control of recombinant proteins and trace amount protein identification in the proteome research.

Journal Article↗
PubMed2005

Increasing peptide identification in tandem mass spectrometry through automatic function switching optimization.

Comprehensive proteomic studies that employ MS directed peptide sequencing are limited by optimal peptide separation and MS and tandem MS data acquisition routines. To identify the optimal parameters for data acquisition, we developed a system that models the automatic function switching behavior of a mass spectrometer using an MS-only dataset. Simulations were conducted to characterize the number and the quality of simulated fragmentation as a function of the data acquisition routines and used to construct operating curves defining tandem mass spectra quality and the number of peptides fragmented. Results demonstrated that one could optimize for quality or quantity, with the number of peptides fragmented decreasing as quality increased. The predicted optimal operating curve indicated that significant improvements can be realized by selecting the appropriate data acquisition parameters. The simulation results were confirmed experimentally by testing 10 LC MS/MS data acquisition parameter sets on an LC-Q-TOF-MS. Database matching of the experimental fragmentation returned peptide scores consistent with the predictions of the model. The results of the simulations of mass spectrometer data acquisition routines reveal an inverse relationship between the quality and the quantity of peptide identifications and predict an optimal operating curve that can be used to select an optimal data acquisition parameter for a given (or any) sample.

Algorithms↗
PubMed2004

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.

Algorithms↗
PubMed2007

Methods for peptide identification by spectral comparison.

BACKGROUND: Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies. RESULTS: This paper investigates computational issues related to this spectral comparison approach. Different methods have been empirically evaluated over several large sets of spectra. First, we illustrate that the peak intensities follow a Poisson distribution. This implies that applying a square root transform will optimally stabilize the peak intensity variance. Our results show that the square root did indeed outperform other transforms, resulting in improved accuracy of spectral matching. Second, different measures of spectral similarity were compared, and the results illustrated that the correlation coefficient was most robust. Finally, we examine how to assemble multiple spectra associated with the same peptide to generate a synthetic reference spectrum. Ensemble averaging is shown to provide the best combination of accuracy and efficiency. CONCLUSION: Our results demonstrate that when combined, these methods can boost the sensitivity and specificity of spectral comparison. Therefore they are capable of enhancing and complementing existing tools for consistent and accurate peptide identification.

Journal Article↗
PubMed2003

Efficiency improvement of peptide identification for an organism without complete genome sequence, using expressed sequence tag database and tandem mass spectral data.

We compared peptide identification by database (DB) search methods with de novo sequencing results for proteomics study in an organism without genome sequence information. When the former was done by searching the Expressed Sequence Tag (EST) DB of the sample organism or the NCBI nonredundant (nr) protein DB of green plants using either the MASCOT or SEQUEST software program, it was confirmed that the former is as accurate as the latter. Peptides identified from EST DB were twice as many as those from the nr protein DB, in spite of the fact that the EST DB has less data (26 222 EST) than the NCBI nr protein DB (224 238). This study demonstrates that EST DB with tandem mass spectra can be used reliably for high-throughput proteomics studies in an organism without genome information.

Algorithms↗