High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis

Tiwary, Shivani; Levy, Roie; Gutenbrunner, Petra; Soto, Favio Salinas; Palaniappan, Krishnan K.; Deming, Laura; Berndl, Marc; Brant, Arthur; Cimermančič, Peter; Cox, Jürgen

doi:10.1038/s41592-019-0427-6

Cited by 231 publications

(252 citation statements)

References 38 publications

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“…We achieve the best coverage and quantification when using tailor-made project specific spectral library for the DIA analyses, but this requires some effort and may not always be possible. However, predicting peptide retention time and MS/MS spectra 27,28 might circumvent the necessity of recording spectral libraries for DIA in future. Unfortunately, the current prediction tools are not yet developed for phosphoproteomics.…”

Section: Discussion (Brief and Focused)mentioning

confidence: 99%

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition (DIA) without the need for spectral libraries

Bekker-Jensen

Bernhardt

Hogrebe

et al. 2019

Preprint

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Unreferenced, ~150 words)Quantitative phosphoproteomics has in recent years revolutionized understanding of cell signaling, but it remains a challenge to scale the technology for high-throughput analyses. Here we present a rapid and reproducible phosphoproteomics approach to systematically analyze hundreds of samples by fast liquid chromatography tandem mass spectrometry using data independent acquisition (DIA). To overcome the inherent issue of positional phosphopeptide isomers in DIA-based phosphoproteomics, we developed and employed an accurate site localization scoring algorithm, which is incorporated into the Spectronaut software tool. Using a library of synthetic phosphopeptides spiked-in to a yeast phosphoproteome in different ratios we show that it is on par with the top site localization score for data-dependent acquisition (DDA) based phosphoproteomics. Single-shot DIA-based phosphoproteomics achieved an order of magnitude broader dynamic range, higher reproducibility of identification and improved sensitivity and accuracy of quantification compared to state-of-the-art DDA-based phosphoproteomics. Importantly, direct DIA without the need of spectral libraries performed almost on par with analyses using specific project-specific libraries. Moreover, we implemented and benchmarked an algorithm for globally determining phosphorylation site stoichiometry in DIA. Finally, we demonstrate the scalability of the DIA approach by systematically analyzing the effects of thirty different kinase inhibitors in context of epidermal growth factor (EGF) signaling showing that a large proportion of EGF-dependent phospho-regulation is mediated by a specific set of protein kinases.

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Section: Discussion (Brief and Focused)mentioning

confidence: 99%

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition (DIA) without the need for spectral libraries

Bekker-Jensen

Bernhardt

Hogrebe

et al. 2019

Preprint

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“…The in silico equivalent is that 95% of the detected peptides overlap when the MS²PIP engine is trained on either Orbitrap or TripleTOF data. As a result, other fragment ion intensity predictors such as Prosit and Deep Mass perform similarly when combined with narrow window DIA (Figures S5 and S6, Supporting Information). Overall, the peptide‐centric workflow seems to have matured to a level that has covered much of the most obvious growing potential.…”

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confidence: 86%

“…This compromises inter‐laboratory comparison and can even alter the biological conclusions between laboratories . However, thanks to the availability of state‐of‐the‐art prediction algorithms, these PQPs can now be predicted directly, setting the stage for much easier and much more reproducible peptide‐centric DIA data extraction …”

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confidence: 99%

Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries

et al. 2020

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Data‐independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data‐dependent acquisition (DDA) libraries for deep peptide‐centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter‐laboratory comparison.

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“…Recent innovative work by Gessulat et al (Gessulat et al, 2019) and Tiwary et al (Tiwary et al, 2019) demonstrated the feasibility of building a virtual spectral library based on separate predictions of fragment ion intensities and peptide retention times from deep learning models.…”

Section: Introductionmentioning

confidence: 99%

A hybrid spectral library combining DIA-MS data and a targeted virtual library substantially deepens the proteome coverage

Lou

Tang

Ding

et al. 2020

Preprint

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Data-independent acquisition mass spectrometry (DIA-MS) is a rapidly evolving technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein familytargeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein coupled receptors; ion channels; and transporters) in mouse brain tissues with increases in protein identification of 37-87%, and peptide identification of 58-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.

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High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis

Cited by 231 publications

References 38 publications

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition (DIA) without the need for spectral libraries

Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition (DIA) without the need for spectral libraries

Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries

A hybrid spectral library combining DIA-MS data and a targeted virtual library substantially deepens the proteome coverage

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