Using Annotated Peptide Mass Spectrum Libraries for Protein Identification

Craig, Robertson; Cortens, J. C.; Fenyö, David; Beavis, Ron

doi:10.1021/pr0602085

Cited by 282 publications

(266 citation statements)

References 23 publications

(37 reference statements)

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“…Therefore, in order for spectral searching to be a truly reliable alternative to sequence searching, we advocate a more conservative and deliberate approach in building spectral libraries that requires strict quality control and careful validation, than those taken in refs. [21,22].…”

Section: Library Qualitymentioning

confidence: 99%

“…With the explosion of proteomics data in recent years, the time is ripe to revisit the idea, with some preliminary demonstration of success being reported in two recent publications [21,22]. The availability of online data repositories developed in recent years [23][24][25][26][27], as well as emerging unified standards for representing shotgun proteomics data, such as mzXML [28] and mzData (http://psi dev.sourceforge.net/ms/#mzdata), have made it possible to collect and catalog an adequate set of peptide CID spectra to create a high-quality spectral library.…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a spectral library searching method for peptide identification from MS/MS

et al. 2007

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A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same identifiable peptides by sequence database searching methods, which often are time-consuming and error-prone. A more precise and efficient method, in which previously observed and identified peptide MS/MS spectra are catalogued and condensed into searchable spectral libraries to allow new identifications by spectral matching, is seen as a promising alternative. To that end, an open-source, functionally complete, high-throughput and readily extensible MS/MS spectral searching tool, SpectraST, was developed. A high-quality spectral library was constructed by combining the high-confidence identifications of millions of spectra taken from various data repositories and searched using four sequence search engines. The resulting library consists of over 30 000 spectra for Saccharomyces cerevisiae. Using this library, SpectraST vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits. A unique advantage of SpectraST is its full integration into the popular Trans Proteomic Pipeline suite of software, which facilitates user adoption and provides important functionalities such as peptide and protein probability assignment, quantification, and data visualization. This method of spectral library searching is especially suited for targeted proteomics applications, offering superior performance to traditional sequence searching.

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Section: Library Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and validation of a spectral library searching method for peptide identification from MS/MS

et al. 2007

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“…Search programs such as SEQUEST [1], Mascot [2], and X!TANDEM [3] are some of the most widely used software packages to identify the most likely peptide sequence to match an MS/MS spectrum. Development of better MS/MS identification tools is an active area of proteomics research [4 -7], MS/MS de novo tools [8 -10], MS/MS spectral search tools [11,12], and MS/MS search result refinement tools [13,14]. All of these tools rely on libraries of well-studied MS/MS spectra from a variety of instruments with accurate peptide assignments.…”

mentioning

confidence: 99%

Validated MALDI-TOF/TOF mass spectra for protein standards

Falkner

Kachman

Veine

et al. 2007

J. Am. Soc. Mass Spectrom.

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A current focus of proteomics research is the establishment of acceptable confidence measures in the assignment of protein identifications in an unknown sample. Development of new algorithmic approaches would greatly benefit from a standard reference set of spectra for known proteins for the purpose of testing and training. Here we describe an openly available library of mass spectra generated on an ABI 4700 MALDI TOF/TOF from 246 known, individually purified and trypsin-digested protein samples. The initial full release of the Aurum Dataset includes gel images, peak lists, spectra, search result files, decoy database analysis files, FASTA file of protein sequences, manual curation, and summary pages describing protein coverage and peptides matched by MS/MS followed by decoy database analysis using Mascot, Sequest, and X!Tandem. The data are publicly available for use at

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“…There was no conceivable way to build comprehensive spectral libraries for use in spectral searching. As a result, this elegant idea failed to catch on until 2006, when several groups published spectral searching methods: X!Hunter (15) and Bibliospec (16) almost simultaneously, and SpectraST (17) a few months later. By then, the technological platform of shotgun proteomics and sequence searching methods had become more mature and widely used, leading to the rapid accumulation of MS/MS data.…”

mentioning

confidence: 99%

“…Since 2006, there have been centralized efforts to build peptide MS/MS spectral libraries, most notably by NIST and also by others (15,16,21). These endeavors seek to collect data from many laboratories and a wide variety of instrument platforms, so as to maximize the coverage and quality of the libraries.…”

mentioning

confidence: 99%

Building and Searching Tandem Mass Spectral Libraries for Peptide Identification

Lam

2011

Molecular & Cellular Proteomics

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Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. Conceptually, the premise of this approach is that the tandem MS fragmentation pattern of a peptide under some fixed conditions is a reproducible fingerprint of that peptide, such that unknown spectra acquired under the same conditions can be identified by spectral matching. In actual practice, a spectral library is first meticulously compiled from a large collection of previously observed and identified tandem MS spectra, usually obtained from shotgun proteomics experiments of complex mixtures. Then, a query spectrum is then identified by spectral matching using recently developed spectral search engines. This review discusses the basic principles of the two pillars of this approach: spectral library construction, and spectral library searching. An overview of the software tools available for these two tasks, as well as a high-level description of the underlying algorithms, will be given. Finally, several new methods that utilize spectral libraries for peptide identification in ways other than straightforward spectral matching will also be described. Molecular & Cellular Proteomics 10: 10.1074/mcp.R111.008565, 1-10, 2011.In the past decade and a half, mass spectrometry-based proteomics has witnessed breathtaking advances. Today, many top research universities and institutes are equipped with proteomics facilities, and the ability to detect and quantify a large number of proteins in a high-throughput manner is having a positive and growing impact in life science research. Among the many proposed experimental workflows, the most widely practiced method is probably the "bottom-up" approach of shotgun proteomics. The key steps of this approach are: (1) proteins are digested into shorter peptides that are more amenable to liquid chromatography (LC)-MS analysis, (2) peptides are further fragmented by tandem mass spectrometry (MS/MS) 1 to yield characteristic fragmentation patterns, and (3) the MS/MS spectra are assigned to their originating peptides by various computational methods (1-4). This last step of assigning MS/MS spectra to their peptide identifications is often the rate-limiting step of the whole proteomics experiment, and has received well-deserved attention over the past decade. These computational methods can be generally classified into three groups, in terms of the "search space," i.e. the set of candidate sequences to consider as possible answers (Fig. 1). On one end of the search space scale are de novo sequencing methods (5-8), which made no initial assumption on what peptides might be present in the sample. Instead the algorithms consider exhaustively all permutations of the 20 amino acids as viable candidates, and try to infer the sequence directly from the MS/MS spectra. In the middle of the search space scale are sequence database searching methods (9 -13), which rely on available sequence databases to limit the search space to only peptides that are...

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Using Annotated Peptide Mass Spectrum Libraries for Protein Identification

Cited by 282 publications

References 23 publications

Development and validation of a spectral library searching method for peptide identification from MS/MS

Development and validation of a spectral library searching method for peptide identification from MS/MS

Validated MALDI-TOF/TOF mass spectra for protein standards

Building and Searching Tandem Mass Spectral Libraries for Peptide Identification

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