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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:


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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, 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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Peer review information Editor recognition statement: Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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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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