Mass spectrometry enables global analysis of posttranslationally modified proteoforms from biological samples, yet we still lack methods to systematically predict, or even prioritize, which modification sites may perturb protein function. Here we describe a proteomic method, Hotspot Thermal Profiling, to detect the effects of site-specific protein phosphorylation on the thermal stability of thousands of native proteins in live cells. This massively parallel biophysical assay unveiled shifts in overall protein stability in response to site-specific phosphorylation sites, as well as trends related to protein function and structure. This method can detect intrinsic changes to protein structure as well as extrinsic changes to protein–protein and protein–metabolite interactions resulting from phosphorylation. Finally, we show that functional ‘hotspot’ protein modification sites can be discovered and prioritized for study in a high-throughput and unbiased fashion. This approach is applicable to diverse organisms, cell types and posttranslational modifications.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $20.17 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Primary data for proteomic analyses are available for download at: ftp://massive.ucsd.edu/MSV000083786; https://doi.org/10.25345/C5CS7H. Raw data used in this work that are not included in supplementary tables or raw mass spectrometry files are available upon request, which includes primary and processed microscopy image files. A Nature Research Reporting Summary is available.
Agapakis, C. M., Boyle, P. M. & Silver, P. A. Natural strategies for the spatial optimization of metabolism in synthetic biology. Nat. Chem. Biol. 8, 527–535 (2012).
Yu, C. S., Chen, Y. C., Lu, C. H. & Hwang, J. K. Prediction of protein subcellular localization. Proteins 64, 643–651 (2006).
Gawron, D., Ndah, E., Gevaert, K. & Van Damme, P. Positional proteomics reveals differences in N-terminal proteoform stability. Mol. Syst. Biol. 12, 858 (2016).
Walsh, C. Posttranslational Modification of Proteins: Expanding Nature’s Inventory (Roberts and Company, 2006).
Hunter, T. Tyrosine phosphorylation: thirty years and counting. Curr. Opin. Cell Biol. 21, 140–146 (2009).
White, F. M. & Wolf-Yadlin, A. Methods for the analysis of protein phosphorylation-mediated cellular signaling networks. Annu. Rev. Anal. Chem. (Palo Alto Calif.) 9, 295–315 (2016).
Martin, L., Latypova, X. & Terro, F. Post-translational modifications of tau protein: implications for Alzheimer’s disease. Neurochem. Int. 58, 458–471 (2011).
Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).
Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).
Olsen, J. V. & Mann, M. Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell. Proteom. 12, 3444–3452 (2013).
Walther, T. C. & Mann, M. Mass spectrometry-based proteomics in cell biology. J. Cell Biol. 190, 491–500 (2010).
Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol. 33, 990–995 (2015).
Olsen, J. V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3 (2010).
Tsai, C. F. et al. Large-scale determination of absolute phosphorylation stoichiometries in human cells by motif-targeting quantitative proteomics. Nat. Commun. 6, 6622 (2015).
Huber, K. V. et al. Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat. Methods 12, 1055–1057 (2015).
Martinez Molina, D. et al. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341, 84–87 (2013).
Piazza, I. et al. A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172, 358–372.e23 (2018).
Savitski, M. M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).
Tan, C. S. H. et al. Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells. Science 359, 1170–1177 (2018).
Smith, L. M. & Kelleher, N. L. Consortium for Top Down Proteomics Proteoform: a single term describing protein complexity. Nat. Methods 10, 186–187 (2013).
Fermin, D., Walmsley, S. J., Gingras, A. C., Choi, H. & Nesvizhskii, A. I. LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol. Cell. Proteom. 12, 3409–3419 (2013).
McGowan, C. H. & Russell, P. Human Wee1 kinase inhibits cell division by phosphorylating p34cdc2 exclusively on Tyr15. EMBO J. 12, 75–85 (1993).
Watanabe, N., Broome, M. & Hunter, T. Regulation of the human WEE1Hu CDK tyrosine 15-kinase during the cell cycle. EMBO J. 14, 1878–1891 (1995).
Blangy, A. et al. Phosphorylation by p34cdc2 regulates spindle association of human Eg5, a kinesin-related motor essential for bipolar spindle formation in vivo. Cell 83, 1159–1169 (1995).
Azimi, A. et al. Targeting CDK2 overcomes melanoma resistance against BRAF and Hsp90 inhibitors. Mol. Syst. Biol. 14, e7858 (2018).
Gnad, F. et al. PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol. 8, R250 (2007).
Jimenez, J. L., Hegemann, B., Hutchins, J. R., Peters, J. M. & Durbin, R. A systematic comparative and structural analysis of protein phosphorylation sites based on the mtcPTM database. Genome Biol. 8, R90 (2007).
Kallberg, M. et al. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 7, 1511–1522 (2012).
Lee, T. Y. et al. dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res. 34, D622–D627 (2006).
Beretta, L., Gingras, A. C., Svitkin, Y. V., Hall, M. N. & Sonenberg, N. Rapamycin blocks the phosphorylation of 4E-BP1 and inhibits cap-dependent initiation of translation. EMBO J. 15, 658–664 (1996).
Sekiyama, N. et al. Molecular mechanism of the dual activity of 4EGI-1: dissociating eIF4G from eIF4E but stabilizing the binding of unphosphorylated 4E-BP1. Proc. Natl Acad. Sci. USA 112, E4036–E4045 (2015).
Gingras, A. C. et al. Regulation of 4E-BP1 phosphorylation: a novel two-step mechanism. Genes Dev. 13, 1422–1437 (1999).
Garakani, K., Shams, H. & Mofrad, M. R. K. Mechanosensitive conformation of vinculin regulates its binding to MAPK1. Biophys. J. 112, 1885–1893 (2017).
Humphries, J. D. et al. Vinculin controls focal adhesion formation by direct interactions with talin and actin. J. Cell Biol. 179, 1043–1057 (2007).
Chorev, D. S. et al. Conformational states during vinculin unlocking differentially regulate focal adhesion properties. Sci. Rep. 8, 2693 (2018).
Huang, J. X., Lee, G. & Moellering, R. E. Discovery and interrogation of functional protein modifications by Hotspot Thermal Profiling. Protoc. Exch. https://doi.org/10.21203/rs.2.10602/v1 (2019).
Chang, J. W., Lee, G., Coukos, J. S. & Moellering, R. E. Profiling reactive metabolites via chemical trapping and targeted mass spectrometry. Anal. Chem. 88, 6658–6661 (2016).
We thank M. Rust for discussions surrounding the manuscript and G. Li for discussions regarding microscopy. We are grateful for financial support for this work from the following: Kwanjeong Educational Fellowship (to G.L.); NIH training grant no. GM007183 and National Academy of Sciences Ford Foundation Fellowship (to K.E.C); NIGMS 1R01GM104032-01A1 (to M.L.G.); NCI R00CA175399 and NIGMS DP2GM128199-01 (to R.E.M.); The Dale F. Frey Award for Breakthrough Scientists from the Damon Runyon Cancer Research Foundation (to R.E.M.) and The University of Chicago.
The authors declare no competing interests.
Peer review information: Allison Doerr was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Schematic showing the total number of detected peptides and phosphopeptides in the unmodified, bulk proteome and phosphoproteome, and the data processing workflow. Stringent filters for qualitative, quantitative, and false localization analyses were used, which resulted in a total of 2,883 high quality ΔTm’s.
a) Reproducibility of Tm values assessed with replicate experiments in bulk, unmodified proteome (linear regression). Only Tm values calculated from melting curves with R2 > 0.8 are used for analysis in this paper. Data generated from n = 12 MS technical replicates from n = 6 independent biological replicates. b),Schematic that shows how peptide ΔTm s.d. is calculated for a typical MS technical run. Peptide ΔTm s.d. is the median deviation for a tryptic peptide Tm value relative to its aggregate protein-level Tm and this value demonstrates the “base error” of the HTP workflow. Reporter ion intensities of detected peptides were combined and converted to relative abundance to generate protein composite melt curve (red line) for each protein. c–f) The average deviation for any given tryptic peptide Tm value relative to its aggregate protein-level Tm is 1.4 °C from four representative datasets.
a, b) Pan phospho-serine, threonine and tyrosine antibody stained western blot (A) on whole cell lysate following indicated temperature pulse for three minutes. Densitometry quantification of immunoblot signal is shown in (B). Data points shown are mean and S.E.M., n = 2 independent biological replicates).
Supplementary Figure 4 Schematic that showing how phosphopeptides containing the same phosphosites are processed.
Unique phosphopeptides that map to a common phosphosite (i.e. different tryptic status or charge state) are collapsed to a single phosphosite reporter, and subsequently to a phosphomodiform Tm value.
Supplementary Figure 5 Acute glucose withdrawal metabolomics and other GAPDH phosphomodiform measurements.
a) Representative HTP curve of GAPDH pT154 phosphomodiform showing no significant ΔTm relative to the bulk, unmodified GAPDH. Tm curve corresponds to mean and S.E.M. from n = 6 (bulk, unmodified) and 5 (phospho) independent biological replicates. b) Relative intracellular GAP level after 30 minutes of acute glucose withdrawal (n = 2 biological replicates, four technical replicates each). c) Crystal structure showing close proximity of T182 to S210 and GAP binding pocket. PDB accession: 1ZNQ. d) Representative Michaelis-Menten kinetic measurement of wild-type, T182A, and T182D mutant GAPDH (n = 3 biological replicates). e) Graph of KM toward GAP for wild-type (WT), T182A and T182D GAPDH enzymes (mean and S.E.M., two-sided t-test).
Supplementary Figs. 1–5 and Supplementary Protocol.
List of bulk, unmodified protein-level Tm measurements. Included in this table are protein IDs (Uniprot accession and description), aggregate unmodified, bulk Tm values from duplicate technical replicates of n = 6 biological replicates.
List of phosphorylation site-specific Tm and ΔTm measurements. Included in this table are phosphomodiform IDs (Gene_pSite), representative peptide sequences, which includes phosphosite location, localization score, Tm and ΔTm measurements from duplicate technical replicates of n = 5 biological replicates. If multiple tryptic peptides were detected for a unique phosphomodiform, one representative peptide is shown. ΔTm values are listed for all peptides in the table, but only phosphomodiforms that satisfy criteria in Supplementary Fig. 1 were included in the final ΔTm database.