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.

Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry


The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Schematic representation of TPP experiments.
Figure 2: Workflow for the isobarQuant Python package.
Figure 3: Operational workflow breakdown.
Figure 4: Example tables required to analyze TPP-TR data using the TPP package.
Figure 5: Example tables required to analyze TPP-CCR data using the TPP package.
Figure 6: Panobinostat TPP-TR experiment.
Figure 7: Panobinostat TPP-CCR experiment.


  1. 1

    Schenone, M., Dancik, V., Wagner, B.K. & Clemons, P.A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).

    CAS  Article  Google Scholar 

  2. 2

    Martinez Molina, D. et al. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341, 84–87 (2013).

    Article  Google Scholar 

  3. 3

    Jafari, R. et al. The cellular thermal shift assay for evaluating drug target interactions in cells. Nat. Protoc. 9, 2100–2122 (2014).

    CAS  Article  Google Scholar 

  4. 4

    Linderstrøm-Lang, K. & Schellman, J.A. Protein structure and enzyme activity. Enzymes 1, 443–510 (1959).

    Google Scholar 

  5. 5

    Pantoliano, M.W. et al. High-density miniaturized thermal shift assays as a general strategy for drug discovery. J. Biomol. Screen. 6, 429–440 (2001).

    CAS  Article  Google Scholar 

  6. 6

    Bantscheff, M. et al. Chemoproteomics profiling of HDAC inhibitors reveals selective targeting of HDAC complexes. Nat. Biotechnol. 29, 255–265 (2011).

    CAS  Article  Google Scholar 

  7. 7

    Becher, I. et al. Chemoproteomics reveals time-dependent binding of histone deacetylase inhibitors to endogenous repressor complexes. ACS Chem. Biol. 9, 1736–1746 (2014).

    CAS  Article  Google Scholar 

  8. 8

    Becher, I. et al. Affinity profiling of the cellular kinome for the nucleotide cofactors ATP, ADP, and GTP. ACS Chem. Biol. 8, 599–607 (2013).

    CAS  Article  Google Scholar 

  9. 9

    Huang, J. Tracking drugs. N. Engl. J. Med. 369, 1168–1169 (2013).

    CAS  Article  Google Scholar 

  10. 10

    Werner, T. et al. High-resolution enabled TMT 8-plexing. Anal. Chem. 84, 7188–7194 (2012).

    CAS  Article  Google Scholar 

  11. 11

    Werner, T. et al. Ion coalescence of neutron encoded TMT 10-plex reporter ions. Anal. Chem. 86, 3594–3601 (2014).

    CAS  Article  Google Scholar 

  12. 12

    Savitski, M.M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).

    Article  Google Scholar 

  13. 13

    Oda, T. et al. Crkl is the major tyrosine-phosphorylated protein in neutrophils from patients with chronic myelogenous leukemia. J. Biol. Chem. 269, 22925–22928 (1994).

    CAS  PubMed  Google Scholar 

  14. 14

    Bantscheff, M., Lemeer, S., Savitski, M.M. & Kuster, B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 404, 939–965 (2012).

    CAS  Article  Google Scholar 

  15. 15

    Perkins, D.N., Pappin, D.J., Creasy, D.M. & Cottrell, J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    CAS  Article  Google Scholar 

  16. 16

    Rauniyar, N. & Yates, J.R. 3rd Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014).

    CAS  Article  Google Scholar 

  17. 17

    Atadja, P. Development of the pan-DAC inhibitor panobinostat (LBH589): successes and challenges. Cancer Lett. 280, 233–241 (2009).

    CAS  Article  Google Scholar 

  18. 18

    Moffat, J.G., Rudolph, J. & Bailey, D. Phenotypic screening in cancer drug discovery—past, present and future. Nat. Rev. Drug Discov. 13, 588–602 (2014).

    CAS  Article  Google Scholar 

  19. 19

    Paul, S.M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).

    CAS  Article  Google Scholar 

  20. 20

    Roberts, R.A. et al. Reducing attrition in drug development: smart loading preclinical safety assessment. Drug Discov. Today 19, 341–347 (2014).

    CAS  Article  Google Scholar 

  21. 21

    Anighoro, A., Bajorath, J. & Rastelli, G. Polypharmacology: challenges and opportunities in drug discovery. J. Med. Chem. 57, 7874–7887 (2014).

    CAS  Article  Google Scholar 

  22. 22

    Keiser, M.J. et al. Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009).

    CAS  Article  Google Scholar 

  23. 23

    Jalencasa, X. & Mestres, J. On the origins of drug polypharmacology. Med. Chem. Commun. 4, 80–87 (2013).

    Article  Google Scholar 

  24. 24

    Knight, Z.A., Lin, H. & Shokat, K.M. Targeting the cancer kinome through polypharmacology. Nat. Rev. Cancer 10, 130–137 (2010).

    CAS  Article  Google Scholar 

  25. 25

    Asial, I. et al. Engineering protein thermostability using a generic activity-independent biophysical screen inside the cell. Nat. Commun. 4, 2901 (2013).

    Article  Google Scholar 

  26. 26

    Miettinen, T.P. & Bjorklund, M. NQO2 is a reactive oxygen species generating off-target for acetaminophen. Mol. Pharm. 11, 4395–4404 (2014).

    CAS  Article  Google Scholar 

  27. 27

    Kruse, U. et al. Chemoproteomics-based kinome profiling and target deconvolution of clinical multi-kinase inhibitors in primary chronic lymphocytic leukemia cells. Leukemia 25, 89–100 (2011).

    CAS  Article  Google Scholar 

  28. 28

    Michalski, A. et al. Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol. Cell. Proteomics 10, M111.011015 (2011).

    Article  Google Scholar 

  29. 29

    Olsen, J.V. et al. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 4, 2010–2021 (2005).

    CAS  Article  Google Scholar 

  30. 30

    Dayon, L. et al. Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal. Chem. 80, 2921–2931 (2008).

    CAS  Article  Google Scholar 

  31. 31

    Unwin, R.D., Griffiths, J.R. & Whetton, A.D. Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS. Nat. Protoc. 5, 1574–1582 (2010).

    CAS  Article  Google Scholar 

  32. 32

    Ting, L., Rad, R., Gygi, S.P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).

    CAS  Article  Google Scholar 

  33. 33

    McAlister, G.C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).

    CAS  Article  Google Scholar 

  34. 34

    Ow, S.Y. et al. iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J. Proteome Res. 8, 5347–5355 (2009).

    CAS  Article  Google Scholar 

  35. 35

    Savitski, M.M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 12, 3586–3598 (2013).

    CAS  Article  Google Scholar 

  36. 36

    Savitski, M.M. et al. Targeted data acquisition for improved reproducibility and robustness of proteomic mass spectrometry assays. J. Am. Soc. Mass Spectrom. 21, 1668–1679 (2010).

    CAS  Article  Google Scholar 

  37. 37

    Savitski, M.M. et al. Delayed fragmentation and optimized isolation width settings for improvement of protein identification and accuracy of isobaric mass tag quantification on Orbitrap-type mass spectrometers. Anal. Chem. 83, 8959–8967 (2011).

    CAS  Article  Google Scholar 

  38. 38

    Lemeer, S., Hahne, H., Pachl, F. & Kuster, B. Software tools for MS-based quantitative proteomics: a brief overview. Methods Mol. Biol. 893, 489–499 (2012).

    CAS  Article  Google Scholar 

  39. 39

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  Article  Google Scholar 

  40. 40

    Cox, J. et al. A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat. Protoc. 4, 698–705 (2009).

    CAS  Article  Google Scholar 

  41. 41

    Colaert, N. et al. Thermo-msf-parser: an open source Java library to parse and visualize Thermo Proteome Discoverer msf files. J. Proteome Res. 10, 3840–3843 (2011).

    CAS  Article  Google Scholar 

  42. 42

    Wilhelm, M., Kirchner, M., Steen, J.A. & Steen, H. mz5: space- and time-efficient storage of mass spectrometry data sets. Mol. Cell. Proteomics 11, O111.011379 (2012).

    Article  Google Scholar 

  43. 43

    Savitski, M.M., Mathieson, T., Becher, I. & Bantscheff, M. H-score, a mass accuracy driven rescoring approach for improved peptide identification in modification rich samples. J. Proteome Res. 9, 5511–5516 (2010).

    CAS  Article  Google Scholar 

  44. 44

    Nielsen, M.L., Savitski, M.M. & Zubarev, R.A. Improving protein identification using complementary fragmentation techniques in Fourier transform mass spectrometry. Mol. Cell. Proteomics 4, 835–845 (2005).

    CAS  Article  Google Scholar 

  45. 45

    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).

    CAS  Article  Google Scholar 

  46. 46

    Kocher, T., Pichler, P., Swart, R. & Mechtler, K. Analysis of protein mixtures from whole-cell extracts by single-run nanoLC-MS/MS using ultralong gradients. Nat. Protoc. 7, 882–890 (2012).

    Article  Google Scholar 

  47. 47

    Chittur, S.V., Sangster-Guity, N. & McCormick, P.J. Histone deacetylase inhibitors: a new mode for inhibition of cholesterol metabolism. BMC Genomics 9, 507 (2008).

    Article  Google Scholar 

Download references


We thank M. Jundt, K. Kammerer, M. Klös-Hudak, M. Paulmann and T. Rudi for expert technical assistance; F. Weisbrodt for help with the figures; and R. Heinkel for expert advice regarding packaging of the isobarQuant software. We are grateful to G. Neubauer for discussions and support.

Author information




H.F., D.C., T.M., F.B.M.R., W.H. and M.M.S. conceived the project and wrote the manuscript; M.M.S., F.B.M.R., T.W., M.B. and G.D. designed the mass spectrometry experiments; T.W. and I.T. conducted and supervised the experiments; H.F., T.M., D.C., G.M.A.S., T.W., C.D., F.B.M.R. and M.M.S. analyzed proteomics data; T.M. and G.M.A.S. developed the isobarQuant package; D.C., H.F., C.D., S.G. and W.H. developed the TPP package; T.W., I.T., G.M.A.S., M.B. and G.D. contributed to the manuscript.

Corresponding authors

Correspondence to Friedrich B M Reinhard or Wolfgang Huber or Mikhail M Savitski.

Ethics declarations

Competing interests

H.F., T.M., G.M.A.S., T.W., I.T., C.D., S.G., M.B., G.D., F.B.M.R. and M.M.S. are employees and/or shareholders of Cellzome and GlaxoSmithKline.

Supplementary information

Supplementary Text and Figures

Supplementary Methods and Supplementary Manual (PDF 3363 kb)

Supplementary Data 1

Results output of the TPP package of the analysis of the TPP-TR panobinostat experiment. (XLSX 9553 kb)

Supplementary Data 2

Results output of the TPP package of the analysis of the TPP-CCR panobinostat experiment. (XLSX 2330 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Franken, H., Mathieson, T., Childs, D. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat Protoc 10, 1567–1593 (2015).

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


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