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Huang et al. reply

Nature Methods volume 18, pages 763–767 (2021)Cite this article

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The Original Article was published on 17 June 2021

The Original Article was published on 17 June 2021

replying to C. M. Potel et al. Nature Methods https://doi.org/10.1038/s41592-021-01177-5 (2021)

replying to I. R. Smith et al. Nature Methods https://doi.org/10.1038/s41592-021-01178-4 (2021)

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Fig. 1: Reanalysis of the dataset from Potel et al. using our published data analysis pipeline.
Fig. 2: Comparison of global profiles and phosphomodiform-specific Tm profiles.
Fig. 3: Comparisons of our variant HTP workflows.

Data availability

All raw LC–MS/MS proteomic data that support the findings of this study were deposited to the Proteome Xchange Consortium through MassIVE with identifier MSV000086871, accessible via https://doi.org/10.25345/C5P79J.

References

  1. Potel, C. M. et al. Impact of phosphorylation on thermal stability of proteins. Nat. Methods https://doi.org/10.1038/s41592-021-01177-5 (2021).

  2. Smith, I. R. et al. Identification of phosphosites that alter thermal stability. Nat. Methods https://doi.org/10.1038/s41592-021-01178-4 (2021).

  3. Huang, J. X. et al. High throughput discovery of functional protein modifications by Hotspot Thermal Profiling. Nat. Methods 16, 894–901 (2019).

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  4. Wu, R. et al. Correct interpretation of comprehensive phosphorylation dynamics requires normalization by protein expression changes. Mol. Cell. Proteomics 10, M111.009654 (2011).

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  5. Stein, B. D. et al. Diverse hotspot thermal profiling methods detect phosphorylation-dependent changes in protein stability. Preprint at bioRxiv https://doi.org/10.1101/2021.05.01.441686 (2021).

  6. Hogrebe, A. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 9, 1045 (2018).

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Acknowledgements

We thank D. Huo and T. Harrison for discussions surrounding data analysis and L. Cantley for access to equipment. Financial support for this work is from the NCI (R00CA175399 to R.E.M.).

Author information

Author notes

  1. These authors contributed equally: Jun X. Huang, David Wu.

Authors and Affiliations

  1. Department of Chemistry, The University of Chicago, Chicago, IL, USA

    Jun X. Huang & Raymond E. Moellering

  2. Institute for Genomics and Systems Biology, The University of Chicago, Chicago, IL, USA

    Jun X. Huang & Raymond E. Moellering

  3. Meyer Cancer Center, Department of Medicine, Weill Cornell Medical College, New York, NY, USA

    David Wu & Benjamin D. Stein

Authors
  1. Jun X. Huang
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  2. David Wu
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  4. Raymond E. Moellering
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Contributions

J.X.H. designed and performed cell-based experiments and MS experiments and analyzed data. D.W. designed, performed and analyzed MS experiments. B.D.S. designed and supervised the study and performed and analyzed experiments. R.E.M. conceived and supervised the study, designed and analyzed experiments and wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Benjamin D. Stein or Raymond E. Moellering.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Effect of applying alternative statistical filters on the perceived fraction of significantly altered ΔTm values.

The percentage of ΔTm values with p-value < 0.05 are 25% (Huang) and 29% (Matteus), when analyzed using our published analysis pipeline. If a population-level, multiple testing correction adjustment is arbitrarily included to calculate q-values, the percentages of ‘significant’ ΔTm values are reduced. Specifically, application of q-value < 0.05 produces 11% (Huang) and 10% (Matteus) and q-value < 0.01 produces 4.1% (Huang) and 2.9% (Matteus) significantly shifted ΔTm values. These data demonstrate that application of a more stringent significance filter will, of course, produce ‘fewer significant sites.’ However, the primary claims made in the Matteus commentary rely on the improper comparison of the output of an analysis that is similar to the right column for their dataset with the output of the left column for out dataset, which is misleading. Application of the same analysis pipeline and significance criteria to each dataset yields a near identical percentage of the proteome that is perturbed by phosphorylation.

Extended Data Fig. 2 Relationships between local phosphosite environment and altered phosphomodiform stability.

a–c, Distributions of ΔTm values for tryptic peptides containing indicated phosphoamino acids or coincidental combinations thereof for Huang et al 2019 EL-HTP (a), LE-HTP (b), and LFE-HTP (c). d–l, Comparisons of ΔTm values and predicted secondary structure elements (d-f), ordered structural elements surrounding the phosphosite of interest (g-i) and solvent accessibility (j-l) of the three datasets respectively. For secondary structure (e-f), disordered regions (h-h) and solvent accessibility (k-l) analyses performed for LE-HTP and LFE-HTP datasets, only proteins detected in the original Huang et al., 2019 publication are included in the analyses. For e) and k), the number of values obtained for ‘Sheet*’ and ‘Buried*’ are too few (<15) to allow for meaningful statistical analyses. For d-l), ***P ≤ 0.0001, **P ≤ 0.001, *P ≤ 0.05, two-sided t-test. Box plots (median, 1–99%) are shown in a-l), with outliers shown as data points.

Extended Data Fig. 3 Representative LFE-HTP Tm correlation plots.

Representative replicate Tm correlation plots from LFE-HTP analysis of bulk, unmodified (a) and phosphoproteome (b).

Extended Data Fig. 4 Representative phosphomodiforms that display similar ΔTm values and melting profiles but different reported statistical significance based on alternative analysis pipelines.

a,b, Median KIF11 bulk protein (black) and phosphomodiform pT926 (red) curves (a) reported by Matteus et al. show a reported ΔTm value of −3.2 °C, which is near identical to the statistically significant ΔTmvalue of −4.1 °C (P = 0.002) reported in our dataset (b) (n = 4). However, KIF11_pT926 is deemed statistically insignificant (mean P = 0.44) using the stringent statistical filtering pipeline reported in the Matteus et al. commentary. c-d, EL-, LE-, and LFE-HTP Tm curves of bulk, unmodified protein (black) and indicated phosphomodiforms (red) from 4EBP1 (C) and GAPDH. Only melting curve-fits with R2 > 0.8 are plotted, including cases where only one curve for specific bulk or phosphosites were detected and passed these criteria (denoted by an *). Curves and error bars in b–d correspond to mean and s.e.m.; ΔTm P-values calculated with two-sided t-test except in a which are values directly reported by Matteus et al using their analysis pipeline.

Supplementary information

Supplementary Notes 1 and 2

Supplementary Note 1: Substantive differences in the protocol presented in the Smith et al.2 commentary relative to Huang et al.3 (EL-HTP). Supplementary Note 2: Substantive differences in the protocol presented in the Potel et al.1 commentary relative to Huang et al.3 (EL-HTP).

Reporting Summary

Supplementary Table 1

Bulk Tm values from LE-HTP.

Supplementary Table 2

Phosphosite Tm values from LE-HTP.

Supplementary Table 3

Bulk Tm values from LFE-HTP.

Supplementary Table 4

Phosphosite Tm values from LFE-HTP.

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Huang, J.X., Wu, D., Stein, B.D. et al. Huang et al. reply. Nat Methods 18, 763–767 (2021). https://doi.org/10.1038/s41592-021-01179-3

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