Identification of tryptic peptides from large databases using multiplexed tandem mass spectrometry: simulations and experimental results

Masselon, Christophe; Paša‐Tolić, Ljiljana; Lee, Sang Won; Anderson, Gordon A.; Harkewicz, Richard; Smith, Richard D.

doi:10.1002/pmic.200300448

Cited by 32 publications

(44 citation statements)

References 22 publications

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“…These observations highlight the importance of relaxing the one-peptide-one-spectrum assumption when designing the next generation of computational tools for identifying MS/MS spectra as instruments advances and higher complexity samples are being analyzed in less time in proteomic experiments. This also illustrates the potential of emerging data acquisition protocols (9,10,11,12,13,14,15,44) where multiple peptides are intentionally cofragmented in each MS/MS spectrum. Finally, even though the proposed MixGF approach focuses on mixture spectra from two peptides, the same approach should be extensible to more than two peptides.…”

Section: Discussionmentioning

confidence: 99%

“…The presence of multiple peptides in mixture spectra can decrease their identification rate to as low as one half of that for MS/MS spectra generated from only one peptide (6,7,8). In addition, there have been numerous developments in data independent acquisition (DIA) technologies where multiple peptide precursors are intentionally selected to cofragment in each MS/MS spectrum (9,10,11,12,13,14,15). These emerging technologies can address some of the enduring disadvantages of traditional data-dependent acquisition (DDA) methods (e.g.…”

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confidence: 99%

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MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

Jian

Bourne

Bandeira

2014

Molecular & Cellular Proteomics

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In large-scale proteomic experiments, multiple peptide precursors are often cofragmented simultaneously in the same mixture tandem mass (MS/MS) spectrum. These spectra tend to elude current computational tools because of the ubiquitous assumption that each spectrum is generated from only one peptide. Therefore, tools that consider multiple peptide matches to each MS/MS spectrum can potentially improve the relatively low spectrum identification rate often observed in proteomics experiments. More importantly, data independent acquisition protocols promoting the cofragmentation of multiple precursors are emerging as alternative methods that can greatly improve the throughput of peptide identifications but their success also depends on the availability of algorithms to identify multiple peptides from each MS/MS spectrum. Here we address a fundamental question in the identification of mixture MS/MS spectra: determining the statistical significance of multiple peptides matched to a given MS/MS spectrum. We propose the MixGF generating function model to rigorously compute the statistical significance of peptide identifications for mixture spectra and show that this approach improves the sensitivity of current mixture spectra database search tools by a Ϸ30 -390%. Analysis of multiple data sets with MixGF reveals that in complex biological samples the number of identified mixture spectra can be as high as 20% of all the identified spectra and the number of unique peptides identified only in mixture spectra can be up to 35.4% of those identified in single-peptide spectra. 1, 2, 3). In typical experiments, tens of thousands to millions of MS/MS spectra are generated and enable researchers to probe various aspects of the proteome on a large scale. Part of this success hinges on the availability of computational methods that can analyze the large amount of data generated from these experiments. The classical question in computational proteomics asks: given an MS/MS spectrum, what is the peptide that generated the spectrum? However, it is increasingly being recognized that this assumption that each MS/MS spectrum comes from only one peptide is often not valid. Several recent analyses show that as many as 50% of the MS/MS spectra collected in typical proteomics experiments come from more than one peptide precursor (4, 5). The presence of multiple peptides in mixture spectra can decrease their identification rate to as low as one half of that for MS/MS spectra generated from only one peptide (6,7,8). In addition, there have been numerous developments in data independent acquisition (DIA) technologies where multiple peptide precursors are intentionally selected to cofragment in each MS/MS spectrum (9,10,11,12,13,14,15). These emerging technologies can address some of the enduring disadvantages of traditional data-dependent acquisition (DDA) methods (e.g. low reproducibility (16)) and potentially increase the throughput of peptide identification 5-10 fold (4, 17). However, despite the growing importance of mixture spectra in var...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

Jian

Bourne

Bandeira

2014

Molecular & Cellular Proteomics

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“…Only one waveform is generated before the experiment and is used indiscriminately for all precursor ions. The use of broadband dissociation (insource dissociation, 33 MSAD, DDC or TNW-CID) enables MS/MS experiments even without MS acquisition. A downside for this approach (similar to CID in a triple quadrupole) is that fragment ions are themselves activated and can dissociate, potentially confusing assignments.…”

Section: Lc/fticr-ms/ms Analysismentioning

confidence: 99%

Tailored noise waveform/collision‐induced dissociation of ions stored in a linear ion trap combined with liquid chromatography/Fourier transform ion cyclotron resonance mass spectrometry

Vilkov

Bogdanov

Paša-Tolić

et al. 2004

Rapid Comm Mass Spectrometry

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A new collision-induced dissociation (CID) technique based on broadband tailored noise waveform (TNW) excitation of ions stored in a linear ion trap has been developed. In comparison with the conventional sustained off-resonance irradiation (SORI) CID method commonly used in Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), this MS/MS technique increases throughput by eliminating the long pump-down delay associated with gas introduction into the high vacuum ICR cell region. In addition, the TNW-CID method speeds spectrum acquisition since it does not require Fourier transformation, calculation of resonant frequencies and generation of the excitation waveforms. We demonstrate TNW-CID coupled with on-line capillary reverse-phase liquid chromatography separations for the identification of peptides. The experimental results are compared with data obtained using conventional quadrupole ion trap MS/MS and SORI-CID MS/MS in an ICR cell.

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“…Examples include certain permutations of the peptide sequence or posttranslational modifications (see (18) for examples of co-eluting histone modification variants). In addition, innovative experimental setups have demonstrated the potential for increased throughput in peptide identification using mixture spectra; examples include data-independent acquisition (19) ion-mobility MS (20), and MS E strategies (21). To alleviate the algorithmic bottleneck in such scenarios, we describe a computational approach, M-SPLIT (mixturespectrum partitioning using library of identified tandem mass spectra), that is able to reliably and efficiently identify peptides from mixture spectra, which are generated from a pair of peptides.…”

Section: The Success Of Tandem Ms (Ms/msmentioning

confidence: 99%

Peptide Identification from Mixture Tandem Mass Spectra

Wang

Pérez-Santiago

Katz

et al. 2010

Molecular & Cellular Proteomics

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The success of high-throughput proteomics hinges on the ability of computational methods to identify peptides from tandem mass spectra (MS/MS). However, a common limitation of most peptide identification approaches is the nearly ubiquitous assumption that each MS/MS spectrum is generated from a single peptide. We propose a new computational approach for the identification of mixture spectra generated from more than one peptide. Capitalizing on the growing availability of large libraries of singlepeptide spectra (spectral libraries), our quantitative approach is able to identify up to 98% of all mixture spectra from equally abundant peptides and automatically adjust to varying abundance ratios of up to 10:1. Furthermore, we show how theoretical bounds on spectral similarity avoid the need to compare each experimental spectrum against all possible combinations of candidate peptides (achieving speedups of over five orders of magnitude) and demonstrate that mixture-spectra can be identified in a matter of seconds against proteome-scale spectral libraries. Although our approach was developed for and is demonstrated on peptide spectra, we argue that the generality of the methods allows for their direct application to other types of spectral libraries and mixture spectra. Molecular & Cellular Proteomics 9: 1476 -1485, 2010. The success of tandem MS (MS/MS1 ) approaches to peptide identification is partly due to advances in computational techniques allowing for the reliable interpretation of MS/MS spectra. Mainstream computational techniques mainly fall into two categories: database search approaches that score each spectrum against peptides in a sequence database (1-4) or de novo techniques that directly reconstruct the peptide sequence from each spectrum (5-8). The combination of these methods with advances in high-throughput MS/MS have promoted the accelerated growth of spectral libraries, collections of peptide MS/MS spectra the identification of which were validated by accepted statistical methods (9, 10) and often also manually confirmed by mass spectrometry experts. The similar concept of spectral archives was also recently proposed to denote spectral libraries including "interesting" nonidentified spectra (11) (i.e. recurring spectra with good de novo reconstructions but no database match). The growing availability of these large collections of MS/MS spectra has reignited the development of alternative peptide identification approaches based on spectral matching (12-14) and alignment (15-17) algorithms.However, mainstream approaches were developed under the (often unstated) assumption that each MS/MS spectrum is generated from a single peptide. Although chromatographic procedures greatly contribute to making this a reasonable assumption, there are several situations where it is difficult or even impossible to separate pairs of peptides. Examples include certain permutations of the peptide sequence or posttranslational modifications (see (18) for examples of co-eluting histone modification variants). In addition,...

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Identification of tryptic peptides from large databases using multiplexed tandem mass spectrometry: simulations and experimental results

Cited by 32 publications

References 22 publications

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

Tailored noise waveform/collision‐induced dissociation of ions stored in a linear ion trap combined with liquid chromatography/Fourier transform ion cyclotron resonance mass spectrometry

Peptide Identification from Mixture Tandem Mass Spectra

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