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.
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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.
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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.
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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.
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Huang, J.X., Lee, G., Cavanaugh, K.E. et al. High throughput discovery of functional protein modifications by Hotspot Thermal Profiling. Nat Methods 16, 894–901 (2019). https://doi.org/10.1038/s41592-019-0499-3
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