Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

High-throughput identification of dominant negative polypeptides in yeast

Abstract

Dominant negative polypeptides can inhibit protein function by binding to a wild-type subunit or by titrating a ligand. Here we use high-throughput sequencing of libraries composed of fragments of yeast genes to identify polypeptides that act in a dominant negative manner, in that they are depleted during cell growth. The method can uncover numerous inhibitory polypeptides for a protein and thereby define small inhibitory regions, even pinpointing individual residues with critical functional roles.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Dominant negative polypeptides can be identified in high throughput and correspond to known protein domains.
Fig. 2: Identification of conditional dominant negative polypeptides in the essential heat-shock transcription factor Hsf1.
Fig. 3: Dominant negative inhibitors identified in a full-genome screen.

Similar content being viewed by others

Data availability

High-throughput sequencing reads have been submitted to NCBI SRA under accession numbers SRR8573191SRR8573214.

References

  1. Herskowitz, I. Nature 329, 219–222 (1987).

    Article  CAS  PubMed  Google Scholar 

  2. Akada, R., Yamamoto, J. & Yamashita, I. Mol. Gen. Genet. 254, 267–274 (1997).

    Article  CAS  PubMed  Google Scholar 

  3. Ramer, S. W., Elledge, S. J. & Davis, R. W. Proc. Natl. Acad. Sci. USA 89, 11589–11593 (1992).

    Article  CAS  PubMed  Google Scholar 

  4. Stevenson, L. F., Kennedy, B. K. & Harlow, E. Proc. Natl. Acad. Sci. USA 98, 3946–3951 (2001).

    Article  CAS  PubMed  Google Scholar 

  5. Boyer, J. et al. Genome Biol. 5, R72 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Saldanha, A. J., Brauer, M. J. & Botstein, D. Mol. Biol. Cell 15, 4089–4104 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lim, M. K. et al. Biochem. J. 435, 641–649 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Chan, K. K. et al. Biochemistry 48, 5518–5531 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Voellmy, R. Methods 35, 199–207 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Liu, X. D., Liu, P. C., Santoro, N. & Thiele, D. J. EMBO J. 16, 6466–6477 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rabindran, S. K. et al. Science 259, 230–234 (1993).

    Article  CAS  PubMed  Google Scholar 

  12. Chen, Y., Barlev, Na, Westergaard, O. & Jakobsen, B. K. EMBO J. 12, 5007–5018 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Flick, K. E., Gonzalez, L., Harrison, C. J. & Nelson, H. C. M. J. Biol. Chem. 269, 12475–12481 (1994).

    CAS  Google Scholar 

  14. Yoshikawa, K. et al. Yeast 28, 349–361 (2011).

    Article  CAS  PubMed  Google Scholar 

  15. Armache, J.-P. et al. Proc. Natl. Acad. Sci. USA 107, 19748–19753 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Michaels, J. E. A., Schimmel, P., Shiba, K. & Miller, W. T. Proc. Natl. Acad. Sci. USA 93, 14452–14455 (1996).

    Article  CAS  PubMed  Google Scholar 

  17. Chow, C. Y. et al. Am. J. Hum. Genet. 84, 85–88 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Neudegger, T., Verghese, J., Hayer-Hartl, M., Hartl, F. U. & Bracher, A. Nat. Struct. Mol. Biol. 23, 140–146 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Quinlan, A. R. Curr. Protoc. Bioinformatics 47, 11.12.1–11.12.34 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

We thank R. Waterston and E. Eichler for comments on the manuscript. The work was supported by NIH grant GM114166 to C.Q. and S.F., and NIH grant P41 GM103533 to S.F.

Author information

Authors and Affiliations

Authors

Contributions

M.W.D., C.Q. and S.F. conceived and interpreted experiments, and wrote the manuscript. M.W.D. conducted experiments and data analysis.

Corresponding author

Correspondence to Stanley Fields.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Reproducibility of individual fragment depletions and coverage values across Ura3 protein.

a, Bar plots showing library diversity pre- and post-induction. b, Scatter plot showing the enrichment scores (log2(frequencyout/frequencyin)) of each fragment of Ura3 across two biological replicates. Darker shades of blue indicate a higher density of points. c, Scatter plot showing frequencies of each Ura3 fragment in the output cell populations in two replicates. The strong density of points in the lower left corner of the plot indicates fragments that fully dropped out during the selection in media lacking uracil. d, Overlaid fragment coverage plots (as in Fig. 2b) generated from each of three replicates (shades of gray). e, Correlations of fragment depletion values between replicates from all experiments. f, Coverage plot as in Fig. 1b, calculated with different thresholds identifying depleted fragments.

Supplementary Figure 2 The smallest dominant negative peptides from Ura3.

Plot showing the 15 smallest and most depleted fragments from Ura3; y-axis shows the fragment enrichment score and x-axis indicates position in the Ura3 protein. The size (in amino acids) is shown beside each fragment, but all fragments contain peptide adaptors (see Methods), such that the full translated products range from 39 to 42 amino acids.

Supplementary Figure 3 Deleterious effects of dominant negative polypeptides of Ura3 are specific to media lacking uracil.

Growth rates of putative dominant negative and control fragments from the Ura3 selection experiment. Lines represent median values. a, Growth effects in the Ura3-dependent condition of media lacking uracil (reproduced from Fig. 1d for clarity). b, Growth effects of the same fragments in the Ura3-independent condition of media containing uracil (N = 11, 9, 11, 11 in order of plot).

Supplementary Figure 4 Analysis of cDNA fragment library.

a, Histogram showing the number of fragments per gene in the yeast genome. b, Histogram showing the sequencing depth for each putative dominant negative fragment. c, For each sequenced fragment, a slope was calculated on the basis of that fragment’s frequency before induction (input), after 1 d of growth selection and after 2 d of selection.

Supplementary Figure 5 Scheme for full-genome dominant negative fragment identification.

a, A yeast cDNA library generated from cells growing at 30 °C was used to generate a library of fragments cloned into a vector with galactose-inducible expression (as in Fig. 1a). After induced yeast cells containing this library have grown overnight, fragments from the output population are sequenced along with fragments from the pre-induction population to produce enrichment scores as in Fig. 1a. After identifying the reading frame and functional effect (depleted or neutral) of each fragment from the cDNA library, we aligned these fragments back to yeast genes. Fragment pileups from the in-frame depleted (red) were compared to pileups generated from two control sets: (1) in-frame neutral fragments (pink) and (2) out-of-frame depleted fragments (gray). Genes with dominant negative regions were identified as having a minimum of four (dashed line) overlapping in-frame depleted fragments, and as having regions where coverage of the control sets do not exceed the coverage of the in-frame depleted set. b, An example showing a gene with three putative dominant negative regions; in-frame depleted fragments are enriched only in the two regions near the C terminus.

Supplementary Figure 6 Scoring of genes with dominant negative regions from cDNA library.

a, Plot showing the 157 genes found to have a dominant negative regions ranked by the total number of individual dominant negative fragments found in those genes. b, Knockout growth effects are shown for the 157 genes with an identified dominant negative region compared to all tested genes; the dominant negative set shows a small enrichment for genes with deleterious effects on growth (one-sided t-test P = 0.024).

Supplementary Figure 7 Dominant negative coverage in protein components of the yeast ribosome.

Coverage charts (as in Fig. 3a) are shown for all ribosomal proteins in the yeast cDNA library screen identified to contain dominant negative regions. a, Those present in the cryo-EM structure of the translating yeast ribosome (PDB 4V6I) are shown. b,c, Those absent in the structure (b) or that derive from the mitochondrial ribosome (c) are also shown.

Supplementary Figure 8 Dominant negative regions in ribosomal proteins.

Ribosomal proteins were mapped onto the cryo-EM structure of the translating ribosome (PDB 4V6I), with the small subunit (40S) shown in gray, the large subunit (60S) in darker gray, the 18S and 25S rRNAs in gold, the P-site tRNA in blue, and proteins with dominant negative polypeptides in red. Around the structure are depletion plots that show domains in these proteins with overlapping dominant negative polypeptides. The y-axis of each depletion plot shows the coverage of overlapping fragments for in-frame depleted (red), in-frame neutral (pink), out-of-frame depleted (dark gray) and total fragments in all frames (light gray) (Methods, Supplementary Data 1).

Supplementary Figure 9 Several tRNA synthetases show overlapping dominant negative fragments in their C termini.

Coverage charts (as in Fig. 3b) are shown for all tRNA synthetase proteins in the yeast cDNA library screen identified as containing dominant negative regions. For these examples and the threonyl-tRNA synthetase shown in Fig. 3b, the dominant negative regions were found in the C-terminal end of the protein.

Supplementary Figure 10 Deleterious effects of dominant negative polypeptides are exacerbated in media in which the target gene is essential.

a, Coverage charts from the cDNA library selection in minimal media are shown for three genes related to amino acid metabolism. Our analysis identified each of these genes as having dominant negative regions. A representative fragment (indicated by red rectangles below coverage charts) was selected for each gene, and the polypeptide fragment was overexpressed in yeast cells. bd, Bar plots showing growth rates of cells containing each dominant negative fragment grown in three types of media, each lacking an amino acid related to the expected function of the target protein: (b) threonine; (c) adenine; and (d) phenylalanine. The leftmost pair of bars corresponds to the protein fragment whose function corresponds to the amino acid left out of the media (e.g., a fragment from Thr1 is the first bar shown in b). For each media type, fragments not expected to affect growth were tested alongside an empty vector as control. N = 3 for all strains; bar height corresponds to mean, and error bars correspond to s.e.m. Significance from two-sided t-test indicated by asterisks (Thr1 P = 0.038, Ade3 P = 0.0052, Aro1 P = 0.073; ns, not significant).

Supplementary Figure 11 Structural features of dominant negative fragments contributing to inhibitory activity of yeast phosphoglycerate kinase.

a, Coverage chart for yeast Pgk1 showing two dominant negative regions, as well as several local minima within these regions. b, Crystal structure of the yeast Pgk1 protein (PDB 1QPG), with N-terminal domain shown in light gray and C-terminal domain shown in dark gray. Short segments colored in red correspond to the local minima in the coverage chart (dashed lines); these are found in close physical proximity and in similar locations within four β-strands.

Supplementary information

Supplementary Information

Supplementary Figures 1–11

Reporting Summary

Supplementary Data 1

Coverage charts for genes with dominant negative regions identified in the cDNA library screen.

Supplementary Table 1

Slopes of dominant negative fragments.

Supplementary Table 2

Total number of in-frame depleted fragments found in each of the 157 genes with dominant negative regions.

Supplementary Table 3

Extended list of 414 genes with dominant negative regions. We generated this gene list by relaxing the cutoff of overlapping in-frame depleted fragments from 4 to 2.

Supplementary Table 4

Functional annotations for 157 genes with dominant negative regions.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dorrity, M.W., Queitsch, C. & Fields, S. High-throughput identification of dominant negative polypeptides in yeast. Nat Methods 16, 413–416 (2019). https://doi.org/10.1038/s41592-019-0368-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-019-0368-0

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research