A method for reducing the time required to match protein sequences with tandem mass spectra

Craig, Robertson; Beavis, Ronald C.

doi:10.1002/rcm.1198

Cited by 465 publications

(401 citation statements)

References 29 publications

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“…This is attractive as it allows typically expensive calculations to be computed quickly because of the greatly reduced search space. This mode of searching is implemented as "error tolerant" mode in Mascot (44), "refinement" mode in Tandem (45), "unassigned single mass gap" mode coupled with searching previous hits in Spectrum Mill, and OMSSA's interactive search. In a related strategy, the Paragon algorithm in ProteinPilot (46) does not focus on subset proteins but rather determines which sequence regions to evaluate more thoroughly during a search using a combination of statistics (using sequence tags to compute Sequence Temperature Values) and search feature probabilities.…”

Section: Figmentioning

confidence: 99%

A Face in the Crowd: Recognizing Peptides Through Database Search

Eng

Searle²,

Clauser

et al. 2011

Molecular & Cellular Proteomics

169

172

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Peptide identification via tandem mass spectrometry sequence database searching is a key method in the array of tools available to the proteomics researcher. The ability to rapidly and sensitively acquire tandem mass spectrometry data and perform peptide and protein identifications has become a commonly used proteomics analysis technique because of advances in both instrumentation and software. Although many different tandem mass spectrometry database search tools are currently available from both academic and commercial sources, these algorithms share similar core elements while maintaining distinctive features. This review revisits the mechanism of sequence database searching and discusses how various parameter settings impact the underlying search. Molecular & Cellular Proteomics 10: 10.1074/ mcp.R111.009522, 1-9, 2011.Innovations in tandem mass spectrometry (MS/MS) 1 have enabled the rapid growth of proteomics for chemists and biologists alike. In addition to the evolution of the instrument hardware to acquire spectra more rapidly and sensitively, improvements to data analysis facilitates the process of identifying and quantifying peptides and proteins for a wide user base. Researchers new to the field can benefit from a review of the fundamentals of tandem mass spectrometry sequence database searching, and even experienced proteomics researchers may profit from considerations of practical strategies to maximize information extraction from data.In a typical shotgun proteomics experiment, the sample is first denatured to enable proteolysis. An enzyme such as trypsin is applied to cleave proteins to smaller peptide components. This peptide mixture is usually then separated on a liquid chromatography (LC) column; more complex samples may necessitate prior fractionation by strong cation exchange or isoelectric focusing. Peptides are subjected to ionizing voltage as they are electrosprayed from the column, and these ions are introduced into the near-vacuum environment of the mass spectrometer.In a tandem mass spectrometry experiment, the instrument will first acquire a survey or precursor scan, also referred to as an MS scan, which measures all intact peptide ions eluting into the mass spectrometer at that given time. One or more peptide ions are selected, sequentially isolated, fragmented, and the resulting fragment ions are measured to produce an MS/MS spectrum. This process is repeated to automatically acquire MS/MS spectra on as many different peptide ions as possible throughout the LC gradient. See Fig. 1 for a schematic showing the relationship between precursor ions in the MS scans and the resulting MS/MS scans. While the MS/MS spectrum contains the peptide fragmentation pattern, the experimental peptide mass and charge state are obtained from the precursor ion measure in the MS spectrum.To clarify terminology, the intact peptide ions in the MS scans are termed precursor ions. Fragment ions are also called product ions and MS/MS spectra are referred to as product ion spectra. MS and MS/MS spectra can also ...

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Section: Figmentioning

confidence: 99%

A Face in the Crowd: Recognizing Peptides Through Database Search

Eng

Searle²,

Clauser

et al. 2011

Molecular & Cellular Proteomics

169

172

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“…Specifically, if one of the peaks in this series has a fragmentation spectrum, the spectrum can be used to search a database of predicted spectra based on the genome sequence of the species under study (5)(6)(7). From a high-scoring match to the database, one can determine the sequence of the tryptic fragment and the protein from which the fragment originated.…”

Section: Background Lc-ms/ms Datamentioning

confidence: 99%

“…We note that such database search is in itself a difficult computational challenge, one that is often addressed independently of quantification. We rely on existing algorithms for database search (5,6).…”

Section: Background Lc-ms/ms Datamentioning

confidence: 99%

Protein quantification across hundreds of experimental conditions

Khan

Bloom

Garcia³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use spacepartitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graphtheoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.kd-tree ͉ orthogonal range query ͉ quantitative proteomics ͉ space partitioning data structures ͉ tandem mass spectrometry

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“…Database search approaches have been described that iterate the search multiple times to significantly reduce the number of possible sequences considered. The result is that searches are accomplished more quickly, with fewer computational resources and with more stringent parameters [51,52]. The large amount of research in this field outlines the importance of the problem and although significant advances have been made, there is still much to be accomplished to address the challenges in this domain.…”

Section: Introductionmentioning

confidence: 99%

“…A goal of this strategy is to initially reduce the number of peptide sequences taken into account, in order to expedite subsequent MS 2 analysis. In this manner, it contains some similarity to previously described iteratively refined MS 2 -based search methods [51,52], but instead uses MS 1 interrogation as the first step. This approach is amenable for an automated computation and has been implemented in a software framework to resolve some of the key limitations of the MS 2 based methods: examination of nonfragmented peaks, identification of protein modifications that are associated with poor fragmentation patterns and computational algorithmic enhancements.…”

Section: Introductionmentioning

confidence: 99%

Software Analysis of Uncorrelated MS1 Peaks for Discovery of Post-Translational Modifications

Pascal

West

Scharager-Tapia

et al. 2015

J. Am. Soc. Mass Spectrom.

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Abstract. The goal in proteomics to identify all peptides in a complex mixture has been largely addressed using various LC MS/MS approaches, such as data dependent acquisition, SRM/MRM, and data independent acquisition instrumentation. Despite these developments, many peptides remain unsequenced, often due to low abundance, poor fragmentation patterns, or data analysis difficulties. Many of the unidentified peptides exhibit strong evidence in high resolution MS 1 data and are frequently post-translationally modified, playing a significant role in biological processes. Proteomics Workbench (PWB) software was developed to automate the detection and visualization of all possible peptides in MS 1 data, reveal candidate peptides not initially identified, and build inclusion lists for subsequent MS 2 analysis to uncover new identifications. We used this software on existing data on the autophagy regulating kinase Ulk1 as a proof of concept for this method, as we had already manually identified a number of phosphorylation sites Dorsey, F. C. et al (J. Proteome. Res. 8(11), 5253-5263 (2009)). PWB found all previously identified sites of phosphorylation. The software has been made freely available at http://www.proteomicsworkbench.com.

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A method for reducing the time required to match protein sequences with tandem mass spectra

Cited by 465 publications

References 29 publications

A Face in the Crowd: Recognizing Peptides Through Database Search

A Face in the Crowd: Recognizing Peptides Through Database Search

Protein quantification across hundreds of experimental conditions

Software Analysis of Uncorrelated MS1 Peaks for Discovery of Post-Translational Modifications

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