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.

SAINT: probabilistic scoring of affinity purification–mass spectrometry data


We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification–mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.

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

Access options

Rent or buy this article

Prices vary by article type



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

Figure 1: Probability model in SAINT.
Figure 2: Analysis of TIP49 and DUB datasets.

Similar content being viewed by others


  1. Ewing, R.M. et al. Mol. Syst. Biol. 3, 89 (2007).

    Article  Google Scholar 

  2. Gavin, A.C. et al. Nature 440, 631–636 (2006).

    Article  CAS  Google Scholar 

  3. Jeronimo, C. et al. Mol. Cell 27, 262–274 (2007).

    Article  CAS  Google Scholar 

  4. Krogan, N.J. et al. Nature 440, 637–643 (2006).

    Article  CAS  Google Scholar 

  5. Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Nat. Methods 4, 787–797 (2007).

    Article  CAS  Google Scholar 

  6. Sardiu, M.E. et al. Proc. Natl. Acad. Sci. USA 105, 1454–1459 (2008).

    Article  CAS  Google Scholar 

  7. Sowa, M.E., Bennett, E.J., Gygi, S.P. & Harper, J.W. Cell 138, 389–403 (2009).

    Article  CAS  Google Scholar 

  8. Breitkreutz, A. et al. Science 328, 1043–1046 (2010).

    Article  CAS  Google Scholar 

  9. Müller, P., Parmigiani, G & Rice, K. in Bayesian Statistics Vol. 8 (eds., Bernardo, J.M. et al.) 349–370 (Oxford University Press, 2007).

  10. Behrends, C., Sowa, M.E., Gygi, S.P. & Harper, J.W. Nature 466, 68–76 (2010).

    Article  CAS  Google Scholar 

  11. Breitkreutz, B.J. et al. Nucleic Acids Res. 36, D637–D640 (2008).

    Article  CAS  Google Scholar 

  12. Turner, B. et al. Database (Oxford) 2010, baq023 (2010).

    Article  Google Scholar 

  13. Hubner, N.C. et al. J. Cell Biol. 189, 739–754 (2010).

    Article  CAS  Google Scholar 

  14. Rinner, O. et al. Nat. Biotechnol. 25, 345–352 (2007).

    Article  CAS  Google Scholar 

  15. Griffin, N.M. et al. Nat. Biotechnol. 28, 83–89 (2010).

    Article  CAS  Google Scholar 

  16. Eng, J.K., McCormack, A.L. & Yates, J.R.I. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).

    Article  CAS  Google Scholar 

  17. Ishwaran, H. & James, L.F. J. Am. Stat. Assoc. 96, 161–173 (2001).

    Article  Google Scholar 

Download references


Supported by grants from the Canadian Institute of Health Research to A.-C.G. (MOP-84314) and M.T. (MOP-12246); the US National Institutes of Health to M.T. (5R01RR024031), A.I.N. and A.-C.G. (R01-GM094231), and A.I.N. (R01-CA126239); a Royal Society Wolfson Research Merit Award and a Scottish Universities Life Sciences Alliance Research Chair to M.T.; a Canada Research Chair in Functional Proteomics to A.-C.G.; and the Lea Reichmann Chair in Cancer Proteomics to A.-C.G. We thank M. Sardiu, M. Washburn and M. Sowa for providing additional information regarding the datasets used in this work, G. Bader for discussions and W. Dunham for reading the manuscript.

Author information

Authors and Affiliations



H.C. and A.I.N. developed, implemented and tested the SAINT method; H.C. wrote the software; B.L., A.B., Z.-Y.L., A.-C.G. and M.T. generated data for the initial SAINT modeling and provided feedback on the model performance; D.M. and D.F. assisted with data analysis and processing; Z.S.Q. contributed to statistical model development; H.C., A.-C.G. and A.I.N. wrote the manuscript; A.I.N. and A.-C.G. conceived the study; A.I.N. directed the project with input from A.-C.G.

Corresponding authors

Correspondence to Anne-Claude Gingras or Alexey I Nesvizhskii.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 (PDF 239 kb)

Supplementary Table 1

Data for the TIP49 dataset. (a) List of all detected interactions and scores from PP-NSAF, CompPASS and SAINT. (b) All interactions in control purifications were included in a separate table after merging of 35 technical replicate purifications into 9 purifications. (c) Table of technical replicates of control purifications. (d) GO terms enrichment in top scoring interactions for each scoring method. (XLS 1006 kb)

Supplementary Table 2

Data for the DUB dataset. (a) List of all detected interactions and scores from CompPASS and SAINT. (bd) GO terms enrichment in top scoring interactions for each scoring method. (XLS 3104 kb)

Supplementary Table 3

Data for the CDC23 dataset. List of all detected interactions with SAINT scores and results reported by t-test. (XLS 98 kb)

Supplementary Software (ZIP 2169 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Choi, H., Larsen, B., Lin, ZY. et al. SAINT: probabilistic scoring of affinity purification–mass spectrometry data. Nat Methods 8, 70–73 (2011).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing