Skip to main content

Thank you for visiting nature.com. 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.

  • Correspondence
  • Published:

Picky: a simple online PRM and SRM method designer for targeted proteomics

Nature Methods volume 15, pages 156–157 (2018)Cite this article

Subjects

To the Editor:

Targeted proteomics methods such as selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) are increasingly popular because they allow sensitive and rapid analysis of preselected proteins1,2,3. However, the development of targeted assays is tedious and typically requires selection, synthesis and mass spectrometric analysis of candidate peptides. SRMAtlas and ProteomeTools provide fragmentation spectra of synthetic peptides covering the entire human proteome4,5, but it is difficult to extract relevant data for specific proteins. Also, the development of scheduled acquisition methods (i.e., for the analysis of specific peptides in defined elution time windows) requires adjustments to specific chromatographic conditions. The number of peptides to be targeted in parallel often exceeds the speed of the mass spectrometer, which forces researchers to ask which peptides can be omitted without sacrificing too much information. Available method design tools do not generate optimized scheduled acquisition methods (Supplementary Fig. 1).

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

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Picky flowchart and benchmark results.

References

  1. Shi, T. et al. Proteomics 16, 2160–2182 (2016).

    Article  CAS  Google Scholar 

  2. Peterson, A.C., Russell, J.D., Bailey, D.J., Westphall, M.S. & Coon, J.J. Mol. Cell. Proteomics 11, 1475–1488 (2012).

    Article  Google Scholar 

  3. Picotti, P. & Aebersold, R. Nat. Methods 9, 555–566 (2012).

    Article  CAS  Google Scholar 

  4. Kusebauch, U. et al. Cell 166, 766–778 (2016).

    Article  CAS  Google Scholar 

  5. Zolg, D.P. et al. Nat. Methods 14, 259–262 (2017).

    Article  CAS  Google Scholar 

  6. MacLean, B. et al. Bioinformatics 26, 966–968 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank F. Büttner and C. Sommer for their excellent technical support and setup of the Linux server system. We also thank M. Ziehm and D. Perez-Hernandez for intense testing of the Picky user interface, and three reviewers for their constructive comments.

Author information

Authors and Affiliations

  1. Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Henrik Zauber & Matthias Selbach

  2. BIH Core Facility Proteomics, Berlin Institute of Health, Berlin, Germany

    Marieluise Kirchner

Authors
  1. Henrik Zauber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marieluise Kirchner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthias Selbach
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthias Selbach.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparison between different available SRM or PRM method generators.

Supplementary Figure 2 Flowchart of the Picky Algorithm.

For more details see section “Picky algorithm” in the supplemental method description.

Supplementary Figure 3 Performance of peptide retention time (RT) prediction implemented in Picky.

Differences between observed and predicted RTs based on the rescaled experimentally determined RTs from ProteomeTools. More than 80 % of RTs are correctly predicted within +/− 3 min (or +/− 6) min tolerance in a 30 min (or 60 min) HPLC gradient. The number of unique peptides analyzed is shown in the title. Shown is one representative technical replicate out of three (n=3).

Source data

Supplementary Figure 4 Performance of peptide retention time (RT) prediction based on hydrophobicity scores.

Same as in Fig. S3 but with predicted RTs based on hydrophobicity scores. Predictions based on hydrophobicity scores alone are considerably less accurate than predictions based on experimental RTs (compare to Fig. S3).

Source data

Supplementary Figure 5 Accuracy of retention time (RT) predictions displaying all three technical replicates (n = 3; 30 and 60 min gradient) from the RT benchmark experiment (Figure S3 and S4).

Displayed are the fractions of predicted RTs that fall into a given RT window. Two RT prediction algorithms are compared: Predictions based on rescaled experimentally observed RTs (from ProteomeTools) as implemented in Picky ("Rescaled RTs") and predictions based only on peptide hydrophobicity scores (HP Scores). The algorithm based on rescaled experimentally observed RTs shows consistently better performance across replicates. The numbers in each stack depict cumulative peptide counts (in %).

Source data

Supplementary Figure 6 Protein abundance distribution from ProteomicsDB (based on iBAQ values).

The abundance range was divided into three bins (divided by turquoise lines) to assign the depicted protein abundance-specific dwell times in Picky (10, 50 or 100 ms). Peptides of proteins not listed in ProteomicsDB receive a dwell time of 100 ms.

Supplementary Figure 7 Extracted fragment peaks of the peptide AGALNSNDAFVLK from the protein GSN.

Figures were exported from Skyline for the four spike-in amounts 30 fmol, 3 fmol, 300 amol and 30 amol (n=1). Different colors represent the trace for the corresponding fragment ion and are indicated in each plot.

Supplementary Figure 8 Peak Areas of the peptide AGALNSNDAFVLK from the UPS1 protein GSN at different spike-in amounts (see also Fig. S7).

The normalized spectrum contrast angle (CA) and the number of matched transitions are depicted above each stack and indicate spectrum similarity with the library spectrum. The different colors represent the different fragment ions. Library intensities were scaled to the maximal stack sum.

Source data

Supplementary Figure 9 Cross spectrum comparisons between the Picky library and all experimentally observed spectra from peptides of all UPS1 proteins at all concentrations in the benchmark dataset.

The normalized spectrum contrast angle (CA) was calculated between spectra with matching precursor and transition masses (20 ppm mass accuracy). True and false matches for different numbers of transitions are shown (turquoise and orange, respectively). With at least five transitions no false match is observed. The top row shows results for all matches (A–D) while the bottom row depicts the highest CA for every unique sequence (E–H).

Source data

Supplementary information

Supplementary Figures 1–9 and Supplementary Methods

Supplementary Figures 1–9 and Supplementary Methods (PDF 1825 kb)

Life Sciences Reporting Summary (PDF 72 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zauber, H., Kirchner, M. & Selbach, M. Picky: a simple online PRM and SRM method designer for targeted proteomics. Nat Methods 15, 156–157 (2018). https://doi.org/10.1038/nmeth.4607

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4607

This article is cited by

Search

Advanced search

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