Meltome atlas—thermal proteome stability across the tree of life


We have used a mass spectrometry-based proteomic approach to compile an atlas of the thermal stability of 48,000 proteins across 13 species ranging from archaea to humans and covering melting temperatures of 30–90 °C. Protein sequence, composition and size affect thermal stability in prokaryotes and eukaryotic proteins show a nonlinear relationship between the degree of disordered protein structure and thermal stability. The data indicate that evolutionary conservation of protein complexes is reflected by similar thermal stability of their proteins, and we show examples in which genomic alterations can affect thermal stability. Proteins of the respiratory chain were found to be very stable in many organisms, and human mitochondria showed close to normal respiration at 46 °C. We also noted cell-type-specific effects that can affect protein stability or the efficacy of drugs. This meltome atlas broadly defines the proteome amenable to thermal profiling in biology and drug discovery and can be explored online at and

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Fig. 1: Meltome atlas.
Fig. 2: Meltome atlas across the tree of life.
Fig. 3: Global determinants of thermal protein stability.
Fig. 4: Molecular determinants of thermal protein stability (lysate data).
Fig. 5: Human meltome atlas.
Fig. 6: Heat stability of proteins of the mitochondrial respiratory chain and use of the meltome atlas in drug discovery.

Data availability

Supplementary Information is available in the online version of this manuscript. The mass spectrometry raw files, peptide and protein identification search engine output files and files showing the melting curves for all proteins have been deposited with the proteomeXchange consortium via the PRIDE partner repository under the project name ‘Meltome atlas—thermal proteome stability across the tree of life’ and the data set identifier PXD011929.

The melting curves for all proteins are also available in the online R Shiny App The human meltome data are also available at Files containing data sources for main text figures can be downloaded from Source data for Figs. 2–6 are presented with the paper.

Code availability

Code for data analysis is available for download at Bioconductor:


  1. 1.

    Leuenberger, P. et al. Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability. Science. 355, eaai7825 (2017).

    PubMed  Google Scholar 

  2. 2.

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

    PubMed  Google Scholar 

  3. 3.

    Chretien, D. et al. Mitochondria are physiologically maintained at close to 50 degrees C. PLoS Biol. 16, e2003992 (2018).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Rai, A. K., Chen, J. X., Selbach, M. & Pelkmans, L. Kinase-controlled phase transition of membraneless organelles in mitosis. Nature 559, 211–216 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).

    CAS  Google Scholar 

  6. 6.

    Becher, I. et al. Pervasive protein thermal stability variation during the cell cycle. Cell 173, 1495–1507 e1418 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Dai, L. et al. Modulation of protein-interaction states through the cell cycle. Cell 173, 1481–1494 e1413 (2018).

    CAS  PubMed  Google Scholar 

  8. 8.

    Tan, C. S. H. et al. Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells. Science 359, 1170–1177 (2018).

    CAS  PubMed  Google Scholar 

  9. 9.

    Reinhard, F. B. et al. Thermal proteome profiling monitors ligand interactions with cellular membrane proteins. Nat. Methods 12, 1129–1131 (2015).

    CAS  PubMed  Google Scholar 

  10. 10.

    Siddiqui, K. S. & Cavicchioli, R. Cold-adapted enzymes. Annu. Rev. Biochem. 75, 403–433 (2006).

    CAS  PubMed  Google Scholar 

  11. 11.

    Mateus, A. et al. Thermal proteome profiling in bacteria: probing protein state in vivo. Mol. Syst. Biol. 14, e8242 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Samaras, P. et al. ProteomicsDB: a multi-omics and multi-organism resource for life science research. Nucleic Acids Res. 48 (D1), D1153–D1163 (2019).

  13. 13.

    Schmidt, T. et al. ProteomicsDB. Nucleic Acids Res. 46, 1271–1281 (2018).

    Google Scholar 

  14. 14.

    Seashore-Ludlow, B., Axelsson, H. & Lundback, T. Perspective on CETSA Literature: toward more quantitative data interpretation. SLAS Disco. 25, 118–126 (2019). 2472555219884524.

    Google Scholar 

  15. 15.

    Van Derlinden, E. & Van Impe, J. F. Modeling growth rates as a function of temperature: model performance evaluation with focus on the suboptimal temperature range. Int. J. Food Microbiol. 158, 73–78 (2012).

    PubMed  Google Scholar 

  16. 16.

    Le, T. T. et al. Hydrophilic lecithins protect milk proteins against heat-induced aggregation. Colloids Surf. B. 60, 167–173 (2007).

    CAS  Google Scholar 

  17. 17.

    Guruharsha, K. G. et al. A protein complex network of Drosophila melanogaster. Cell 147, 690–703 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Keseler, I. M. et al. The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res. 45, 543–550 (2017).

    Google Scholar 

  19. 19.

    Mukhopadhyay, A., Ray, S. & De, M. Detecting protein complexes in a PPI network: a gene ontology based multi-objective evolutionary approach. Mol. Biosyst. 8, 3036–3048 (2012).

    CAS  PubMed  Google Scholar 

  20. 20.

    Ori, A. et al. Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biol. 17, 47 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Sterner, R. & Liebl, W. Thermophilic adaptation of proteins. Crit. Rev. Biochem. Mol. Biol. 36, 39–106 (2001).

    CAS  PubMed  Google Scholar 

  22. 22.

    Jaenicke, R. & Bohm, G. The stability of proteins in extreme environments. Curr. Opin. Struct. Biol. 8, 738–748 (1998).

    CAS  PubMed  Google Scholar 

  23. 23.

    Nick Pace, C., Scholtz, J. M. & Grimsley, G. R. Forces stabilizing proteins. FEBS Lett. 588, 2177–2184 (2014).

    CAS  PubMed  Google Scholar 

  24. 24.

    Zeldovich, K. B., Berezovsky, I. N. & Shakhnovich, E. I. Protein and DNA sequence determinants of thermophilic adaptation. PLoS Comput. Biol. 3, e5 (2007).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Zhou, X. X., Wang, Y. B., Pan, Y. J. & Li, W. F. Differences in amino acids composition and coupling patterns between mesophilic and thermophilic proteins. Amino Acids 34, 25–33 (2008).

    CAS  PubMed  Google Scholar 

  26. 26.

    Weingarten, M. D., Lockwood, A. H., Hwo, S. Y. & Kirschner, M. W. A protein factor essential for microtubule assembly. Proc. Natl Acad. Sci. USA 72, 1858–1862 (1975).

    CAS  PubMed  Google Scholar 

  27. 27.

    Weerkamp, F. et al. Flow cytometric immunobead assay for the detection of BCR-ABL fusion proteins in leukemia patients. Leukemia 23, 1106–1117 (2009).

    CAS  PubMed  Google Scholar 

  28. 28.

    Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol. Cell Proteom. 11, M111 014050 (2012).

    Google Scholar 

  29. 29.

    Zhou, B. et al. Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562. Genome Res. 29, 472–484 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Gioia, L., Siddique, A., Head, S. R., Salomon, D. R. & Su, A. I. A genome-wide survey of mutations in the Jurkat cell line. BMC Genomics 19, 334 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Zecha, J. et al. Peptide level turnover measurements enable the study of proteoform dynamics. Mol. Cell Proteom. 17, 974–992 (2018).

    CAS  Google Scholar 

  32. 32.

    Chang, W. K., Yang, K. D., Chuang, H., Jan, J. T. & Shaio, M. F. Glutamine protects activated human T cells from apoptosis by up-regulating glutathione and Bcl-2 levels. Clin. Immunol. 104, 151–160 (2002).

    CAS  PubMed  Google Scholar 

  33. 33.

    Dundas, S. R., Lawrie, L. C., Rooney, P. H. & Murray, G. I. Mortalin is over-expressed by colorectal adenocarcinomas and correlates with poor survival. J. Pathol. 205, 74–81 (2005).

    CAS  PubMed  Google Scholar 

  34. 34.

    Kwiatkowski, N. et al. Targeting transcription regulation in cancer with a covalent CDK7 inhibitor. Nature 511, 616–620 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Liu, H. et al. Impact of IgG Fc-oligosaccharides on recombinant monoclonal antibody structure, stability, safety, and efficacy. Biotechnol. Prog. 33, 1173–1181 (2017).

    CAS  PubMed  Google Scholar 

  36. 36.

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

    Google Scholar 

  37. 37.

    Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).

    CAS  PubMed  Google Scholar 

  39. 39.

    Becher, I. et al. Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat. Nat. Chem. Biol. 12, 908–910 (2016).

    CAS  PubMed  Google Scholar 

  40. 40.

    Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).

    CAS  PubMed  Google Scholar 

  41. 41.

    Choorapoikayil, S., Overvoorde, J. & den Hertog, J. Deriving cell lines from zebrafish embryos and tumors. Zebrafish 10, 316–325 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Ruprecht, B., Zecha, J., Zolg, D. P. & Kuster, B. High pH reversed-phase micro-columns for simple, sensitive, and efficient fractionation of proteome and (TMT labeled) phosphoproteome digests. Methods Mol. Biol. 1550, 83–98 (2017).

    CAS  PubMed  Google Scholar 

  43. 43.

    Yu, P. et al. Trimodal mixed mode chromatography that enables efficient offline two-dimensional peptide fractionation for proteome analysis. Anal. Chem. 89, 8884–8891 (2017).

    CAS  PubMed  Google Scholar 

  44. 44.

    Ruprecht, B. et al. Hydrophilic strong anion exchange (hSAX) chromatography enables deep fractionation of tissue proteomes. Methods Mol. Biol. 1550, 69–82 (2017).

    CAS  PubMed  Google Scholar 

  45. 45.

    Ruprecht, B. et al. in Proteomics: Methods and Protocols (eds Comai, L. et al.) 69–82 (Springer, 2017).

  46. 46.

    Hahne, H. et al. DMSO enhances electrospray response, boosting sensitivity of proteomic experiments. Nat. Methods 10, 989–991 (2013).

    CAS  PubMed  Google Scholar 

  47. 47.

    Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    PubMed  Google Scholar 

  48. 48.

    Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    PubMed  Google Scholar 

  49. 49.

    Yachdav, G. et al. PredictProtein-an open resource for online prediction of protein structural and functional features. Nucleic Acids Res. 42, 337–343 (2014).

    Google Scholar 

  50. 50.

    Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).

    CAS  PubMed  Google Scholar 

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The data reported here are tabulated in the main paper and Supplementary Information. We thank P. Giansanti for providing the mouse samples; A. Hubauer and S. Petzoldt for technical assistance; M. Übelacker for assistance with bacterial cultivation; I. Cornella-Taracido (Merck Research Laboratories, Boston, MA), C. Siegl, S. Dieter and H. Glimm (NCT Heidelberg, Germany); A. Augustin (Hoffmann-La Roche, Basel, Switzerland) and colleagues for providing access to their meltome data; and M. Bernhofer, J. Reeb and B. Rost for computing and providing protein structure predictions. A.J. is grateful for funding from the Alexander von Humboldt foundation, J.Z. for funding from the German Consortium for Translational Cancer Research, P.S. for funding from SAP, J.M. for funding from the German Science Foundation (DFG-SFB924) and M.W. for funding from the ProteomeTools project (BMBF grant no. 031L0008A).

Author information




A.J. planned and performed experiments, contributed to data analysis, interpretation and manuscript writing. N.K. contributed to data analysis, interpretation and manuscript writing, wrote the shiny app. T.H. contributed to data analysis, interpretation and manuscript writing. M. Moerch contributed to planning and performing experiments. J.Z. performed and analyzed experiments. N.L. performed experiments. Y.B. contributed to data acquisition. E.M. and M. Maschberger performed experiments. G.S. contributed to data acquisition. I.B. and C.D. performed experiments. P.S. contributed to building the data repository ProteomicsDB. J.M. performed experiments. B.S. performed experiments. A.A. contributed to data analysis and interpretation. T.W. performed experiments. M.B. contributed to data analysis and interpretation. M.W. contributed to building the data repository ProteomicsDB. M.K. contributed to data analysis and interpretation. S.L. contributed to data analysis and interpretation. W.L. contributed to data analysis and interpretation. H.H. conceived the idea, supervised the studies, contributed to data interpretation and manuscript writing. M.M.S. conceived the idea, supervised the studies and contributed to data interpretation and manuscript writing. B.K. conceived the idea, supervised the studies and contributed to data interpretation and manuscript writing.

Corresponding authors

Correspondence to Hannes Hahne or Mikhail M. Savitski or Bernhard Kuster.

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Competing interests

H.H. is cofounder, shareholder and CEO of OmicScouts GmbH. M.W. and B.K. are cofounders and shareholders of OmicScouts GmbH and msAId GmbH. M.W. and B.K. have no operational role in either company. T.W. and M.B. are employees and shareholders of GlaxoSmithKline. M.M.S. is a shareholder of GlaxoSmithKline.

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Jarzab, A., Kurzawa, N., Hopf, T. et al. Meltome atlas—thermal proteome stability across the tree of life. Nat Methods 17, 495–503 (2020).

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