Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

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


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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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 ( and the Uniprot Human Database (11/2018 release,

Code Availability

The AScorePro software package is available as a standalone application from Github at The FLR viewer for phosphorylation site localization is available as a web application at The source code for the FLR viewer is available from Github at


  1. Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Yu, K. et al. QPhos: a database of protein phosphorylation dynamics in humans. Nucleic Acids Res. 47, D451–D458 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Krug, K. et al. A curated resource for phosphosite-specific signature analysis. Mol. Cell. Proteom. 18, 576–593 (2019).

    Article  CAS  Google Scholar 

  4. Ochoa, D. et al. The functional landscape of the human phosphoproteome. Nat. Biotechnol. 38, 365–373 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Kalyuzhnyy, A. et al. Profiling the human phosphoproteome to estimate the true extent of protein phosphorylation. J. Proteome Res. 21, 1510–1524 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Marx, H. et al. A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics. Nat. Biotechnol. 31, 557–564 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Ferries, S. et al. Evaluation of parameters for confident phosphorylation site localization using an orbitrap fusion tribrid mass spectrometer. J. Proteome Res. 16, 3448–3459 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Cui, L. & Reid, G. E. Examining factors that influence erroneous phosphorylation site localization via competing fragmentation and rearrangement reactions during ion trap CID-MS/MS and -MS(3.). Proteomics 13, 964–973 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Wiese, H. et al. Comparison of alternative MS/MS and bioinformatics approaches for confident phosphorylation site localization. J. Proteome Res. 13, 1128–1137 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Suni, V. et al. SimPhospho: a software tool enabling confident phosphosite assignment. Bioinformatics 34, 2690–2692 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583–1594 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Ramsbottom, K. A. et al. Method for independent estimation of the false localization rate for phosphoproteomics. J. Proteome Res. 21, 1603–1615 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jiang, W. et al. Deep-learning-derived evaluation metrics enable effective benchmarking of computational tools for phosphopeptide identification. Mol. Cell. Proteom. 20, 100171 (2021).

    Article  CAS  Google Scholar 

  14. Pirman, N. L. et al. A flexible codon in genomically recoded Escherichia coli permits programmable protein phosphorylation. Nat. Commun. 6, 8130 (2015).

    Article  PubMed  Google Scholar 

  15. Mohler, K., Moen, J., Rogulina, S. & Rinehart, J. Principles for systematic optimization of an orthogonal translation system with enhanced biological tolerance. Preprint at bioRxiv (2021).

  16. Barber, K. W. et al. Encoding human serine phosphopeptides in bacteria for proteome-wide identification of phosphorylation-dependent interactions. Nat. Biotechnol. 36, 638–644 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schroeder, M. J., Shabanowitz, J., Schwartz, J. C., Hunt, D. F. & Coon, J. J. A neutral loss activation method for improved phosphopeptide sequence analysis by quadrupole ion trap mass spectrometry. Anal. Chem. 76, 3590–3598 (2004).

    Article  CAS  PubMed  Google Scholar 

  18. Beausoleil, S. A., Villén, J., Gerber, S. A., Rush, J. & Gygi, S. P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24, 1285–1292 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Mintseris, J. & Gygi, S. P. High-density chemical cross-linking for modeling protein interactions. Proc. Natl Acad. Sci. USA 117, 93–102 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Pedrioli, P. G. A. et al. A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 22, 1459–1466 (2004).

    Article  CAS  PubMed  Google Scholar 

  21. Martens, L. et al. mzML — a community standard for mass spectrometry data. Mol. Cell. Proteomics 10, R110.000133 (2011).

  22. Taus, T. et al. Universal and confident phosphorylation site localization using phosphoRS. J. Proteome Res. 10, 5354–5362 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Hogrebe, A. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 9, 1–13 (2018).

    Article  CAS  Google Scholar 

  24. Potel, C. M., Lemeer, S. & Heck, A. J. R. Phosphopeptide fragmentation and site localization by mass spectrometry: an update. Anal. Chem. 91, 126–141 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Verheggen, K. et al. Anatomy and evolution of database search engines—a central component of mass spectrometry based proteomic workflows. Mass Spectrom. Rev. 39, 292–306 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Locard-Paulet, M., Bouyssié, D., Froment, C., Burlet-Schiltz, O. & Jensen, L. J. Comparing 22 popular phosphoproteomics pipelines for peptide identification and site localization. J. Proteome Res. 19, 1338–1345 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Tabb, D. L. The SEQUEST family tree. J. Am. Soc. Mass. Spectrom. 26, 1814–1819 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Dorfer, V. et al. MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra. J. Proteome Res. 13, 3679–3684 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yu, F. et al. Identification of modified peptides using localization-aware open search. Nat. Commun. 11, 1–9 (2020).

    Article  CAS  Google Scholar 

  33. Geiszler, D. J. et al. PTM-shepherd: Analysis and summarization of post-translational and chemical modifications from open search results. Mol. Cell. Proteomics 20, 100018 (2021).

  34. Amiram, M. et al. Evolution of translation machinery in recoded bacteria enables multi-site incorporation of nonstandard amino acids. Nat. Biotechnol. 33, 1272–1279 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Muehlbauer, L. K., Hebert, A. S., Westphall, M. S., Shishkova, E. & Coon, J. J. Global phosphoproteome analysis using high-field asymmetric waveform ion mobility spectrometry on a hybrid orbitrap mass spectrometer. Anal. Chem. 92, 15959–15967 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rad, R. et al. Improved monoisotopic mass estimation for deeper proteome coverage. J. Proteome Res. 20, 591–598 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Keseler, I. M. et al. The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res. 45, D543–D550 (2017).

    Article  CAS  PubMed  Google Scholar 

  38. Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Savitski, M. M., Wilhelm, M., Hahne, H., Kuster, B. & Bantscheff, M. A scalable approach for protein false discovery rate estimation in large proteomic data sets. Mol. Cell. Proteom. 14, 2394–2404 (2015).

    Article  CAS  Google Scholar 

  41. Li, J. et al. TMTpro-18plex: the expanded and complete set of TMTpro reagents for sample multiplexing. J. Proteome Res. 20, 2964–2972 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li, J. et al. TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat. Methods 17, 399–404 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Li, J., Paulo, J. A., Nusinow, D. P., Huttlin, E. L. & Gygi, S. P. Investigation of proteomic and phosphoproteomic responses to signaling network perturbations reveals functional pathway organizations in yeast. Cell Rep. 29, 2092–2104.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Popow, O., Liu, X., Haigis, K. M., Gygi, S. P. & Paulo, J. A. A compendium of murine (phospho)peptides encompassing different isobaric labeling and data acquisition strategies. J. Proteome Res. 20, 3678–3688 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


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.

Author information

Authors and Affiliations



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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research