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A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics

Nature Methods volume 19, pages 1371–1375 (2022)Cite this article

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Abstract

Mass-spectrometry-based phosphoproteomics has become indispensable for understanding cellular signaling in complex biological systems. Despite the central role of protein phosphorylation, the field still lacks inexpensive, regenerable, and diverse phosphopeptides with ground-truth phosphorylation positions. Here, we present Iterative Synthetically Phosphorylated Isomers (iSPI), a proteome-scale library of human-derived phosphoserine-containing phosphopeptides that is inexpensive, regenerable, and diverse, with precisely known positions of phosphorylation. We demonstrate possible uses of iSPI, including use as a phosphopeptide standard, a tool to evaluate and optimize phosphorylation-site localization algorithms, and a benchmark to compare performance across data analysis pipelines. We also present AScorePro, an updated version of the AScore algorithm specifically optimized for phosphorylation-site localization in higher energy fragmentation spectra, and the FLR viewer, a web tool for phosphorylation-site localization, to enable community use of the iSPI resource. iSPI and its associated data constitute a useful, multi-purpose resource for the phosphoproteomics community.

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Fig. 1: iSPI is a multi-purpose, regenerable, proteome-scale human phosphopeptide resource for phosphoproteoimcs.
Fig. 2: Using iSPI to evaluate collision modes and labeling states, as well as to improve the AScore algorithm.

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Data Availability

The mass spectrometry data generated in this work have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD031171 (Fig. 2, Extended Data Fig. 1 and Supplementary Figs. 2, 4, 5, and 7). Previously published datasets re-analyzed in this work (Supplementary Fig. 6) are also available through ProteomeXchange via their dataset identifiers listed in Supplementary Table 5. Publicly available databases used include EcoCyc v17 (https://biocyc.org/organism-summary?object=ECOLI) and the Uniprot Human Database (11/2018 release, https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2018_11/).

Code Availability

The AScorePro software package is available as a standalone application from Github at https://github.com/gygilab/MPToolKit. The FLR viewer for phosphorylation site localization is available as a web application at http://wren.hms.harvard.edu/iSPI/. The source code for the FLR viewer is available from Github at https://github.com/gygilab/iSPI_Viewer.

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Acknowledgements

We would like to thank members of the Gygi Lab at Harvard Medical School for productive discussions, and S. Rogulina for technical assistance. This work was funded in part NIH/NIGMS grants GM132129 (J. A. P.), GM117230 (J. R.) and GM67945 (S. P. G). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Brandon M. Gassaway, Jiaming Li.

Authors and Affiliations

  1. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Brandon M. Gassaway, Jiaming Li, Ramin Rad, Julian Mintseris, Joao A. Paulo, Edward L. Huttlin & Steven P. Gygi

  2. Department of Cellular and Molecular Physiology and Systems Biology Institute, Yale Medical School, New Haven, CT, USA

    Kyle Mohler & Jesse Rinehart

  3. Cell Signaling Technology, Danvers, MA, USA

    Tyler Levy, Mike Aguiar & Sean A. Beausoleil

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Contributions

B. M. G., J.R. and S. P. G. conceived the study. J. R. and K. M. provided reagents. B. M. G. and J. A. P. performed experiments. B. M. G. and J. L. analyzed data. J. L. R. R. and J. M. conceived and provided computational tools. T. L., M. A. and S. A. B. provided advice on data analysis. E. L. H. and S. P. G. oversaw this work. B. M. G., J. L. and S. P. G. wrote the manuscript with input and editing from all authors.

Corresponding author

Correspondence to Steven P. Gygi.

Ethics declarations

Competing interests

The iSPI library is covered under patent EP3755798A4 (pending, inventor: J. R., assignee: Yale University, Agilent Technologies Inc), and pSerOTS is covered under patent US7723069B2 (active, inventor: J. R., assignee: Yale University). The other authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Tiannan Guo, Martin Larsen, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Using the iSPI to compare phosphoproteomics pipelines.

The datasets for the three subpools were analyzed using four different pipelines. Each pipeline included a database searching algorithm and a site localization tool. a) Similar to Fig. 2, receiver operating curves are shown displaying the number of combined sites from the three pools on the y-axis as a function of the FLR on the x-axis. Vertical grey dotted lines represent empirical FLR’s of 0.01 and 0.05 respectively. Labelled points represent commonly used localization cutoffs: 13 for AScore and AScorePro, 0.75 localization probability for MaxQuant, and 0.95 site probability for proteome discoverer. At an empirical FLR of 0.05, differences are apparent. b) The number of phosphorylation sites per pool is shown for label free peptides at an empirical 5% FLR. c) The number of phosphorylation sites per pool is shown for TMTpro-labeled peptides at an empirical 5% FLR. Bar represents the mean. Error bars represent the standard error of the mean for n = 3 subpools.

Supplementary information

Supplementary Information

Supplementary Notes 1–3 and Supplementary Figures 1–8

Reporting Summary

Peer Review File

Supplementary Table 1

iSPI amino acid and oligonucleotide sequences

Supplementary Table 2

Combined identified and unique iSPI phosphopeptides

Supplementary Table 3

iSPi mass spectrometry file metadata

Supplementary Table 4

iSPI phosphosite identifications and FLR at various score cutoffs

Supplementary Table 5

Biological data re-analysis metadata

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Gassaway, B.M., Li, J., Rad, R. et al. A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics. Nat Methods 19, 1371–1375 (2022). https://doi.org/10.1038/s41592-022-01638-5

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