In-solution enrichment identifies peptide inhibitors of protein–protein interactions

Abstract

The use of competitive inhibitors to disrupt protein–protein interactions (PPIs) holds great promise for the treatment of disease. However, the discovery of high-affinity inhibitors can be a challenge. Here we report a platform for improving the affinity of peptide-based PPI inhibitors using non-canonical amino acids. The platform utilizes size exclusion-based enrichment from pools of synthetic peptides (1.5–4 kDa) and liquid chromatography-tandem mass spectrometry-based peptide sequencing to identify high-affinity binders to protein targets, without the need for ‘reporter’ or ‘encoding’ tags. Using this approach—which is inherently selective for high-affinity binders—we realized gains in affinity of up to ~100- or ~30-fold for binders to the oncogenic ubiquitin ligase MDM2 or HIV capsid protein C-terminal domain, which inhibit MDM2–p53 interaction or HIV capsid protein C-terminal domain dimerization, respectively. Subsequent macrocyclization of select MDM2 inhibitors rendered them cell permeable and cytotoxic toward cancer cells, demonstrating the utility of the identified compounds as functional PPI inhibitors.

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Fig. 1: Affinity selection platform for the maturation of known peptide binders.
Fig. 2: Affinity selection identifies hotspot residues for MDM2 binding.
Fig. 3: Affinity selection identifies potent variants containing non-canonical amino acids.
Fig. 4: Discovery of potent mirror image knottin derived peptide binders to MDM2.
Fig. 5: Macrocyclic variants were cell penetrating and active against oncogenic cells overexpressing MDM2.

Data availability

The authors declare that all data supporting the findings of this study are available within the manuscript, its Supplementary Information, and Supplementary Notes.

Change history

  • 13 May 2019

    In the version of this article originally published, the peptide sequences of compounds 90, 92 and 93 in Fig. 5b and Supplementary Table 7 contained several errors. In Fig. 5b, position 6 of compound 90 should be Tyr instead of Phe. In both Fig. 5b and Supplementary Table 7, position 9 of compounds 92 and 93 should be Gln instead of Glu. Additionally, the surname of co-author Anupam Bandyopadhyay was incorrectly spelled as Bandyopdhyay. The errors have been corrected in the HTML and PDF versions of the paper and in the Supplementary Information PDF.

References

  1. 1.

    Wells, J. A. & McClendon, C. L. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature 450, 1001–1009 (2007).

  2. 2.

    Petta, I., Lievens, S., Libert, C., Tavernier, J. & De Bosscher, K. Modulation of protein–protein interactions for the development of novel therapeutics. Mol. Ther. 24, 707–718 (2016).

  3. 3.

    Modell, A. E., Blosser, S. L. & Arora, P. S. Systematic targeting of protein–protein interactions. Trends Pharmacol. Sci. 37, 702–713 (2016).

  4. 4.

    Pelay-Gimeno, M., Glas, A., Koch, O. & Grossmann, T. N. Structure-based design of inhibitors of protein–protein interactions: mimicking peptide binding epitopes. Angew. Chem. Int. Ed. Engl. 54, 8896–8927 (2015).

  5. 5.

    Valeur, E. et al. New modalities for challenging targets in drug discovery. Angew. Chem. Int. Ed. Engl. 56, 10294–10323 (2017).

  6. 6.

    Grossmann, T. N. et al. Inhibition of oncogenic Wnt signaling through direct targeting of β-catenin. Proc. Natl Acad. Sci. USA 109, 17942–17947 (2012).

  7. 7.

    Spokoyny, A. M. et al. A perfluoroaryl-cysteine SNAr chemistry approach to unprotected peptide stapling. J. Am. Chem. Soc. 135, 5946–5949 (2013).

  8. 8.

    Lautrette, G., Touti, F., Lee, H. G., Dai, P. & Pentelute, B. L. Nitrogen arylation for macrocyclization of unprotected peptides. J. Am. Chem. Soc. 138, 8340–8343 (2016).

  9. 9.

    Renfrew, P. D., Choi, E. J., Bonneau, R. & Kuhlman, B. Incorporation of noncanonical amino acids into Rosetta and use in computational protein–peptide interface design. PLoS One 7, 1–15 (2012).

  10. 10.

    Drew, K. et al. Adding diverse noncanonical backbones to Rosetta: enabling peptidomimetic design. PLoS One 8, 1–17 (2013).

  11. 11.

    Rognan, D., Scapozza, L. & Folkers, G. & Daser A. Rational design of nonnatural peptides as high-affinity ligands for the HLA-B*2705 human leukocyte antigen. Proc. Natl Acad. Sci. USA 92, 753–757 (1995)..

  12. 12.

    Zhan, C. et al. An ultrahigh affinity d-peptide antagonist Of MDM2. J. Med. Chem. 55, 6237–6241 (2012).

  13. 13.

    Zhou, H.-B. et al. Structure-based design of high-affinity macrocyclic peptidomimetics to block the menin-MLL1 protein–protein interaction. J. Med. Chem. 1, 1113–1123 (2012).

  14. 14.

    Kritzer, J. A., Luedtke, N. W., Harker, E. A. & Schepartz, A. A rapid library screen for tailoring β-peptide structure and function. J. Am. Chem. Soc. 127, 14584–14585 (2005).

  15. 15.

    Upadhyaya, P. et al. Inhibition of Ras signaling by blocking Ras-effector interactions with cyclic peptides. Angew. Chem. Int. Ed. Engl. 54, 7602–7606 (2015).

  16. 16.

    Annis, D. A., Nazef, N., Chuang, C. C., Scott, M. P. & Nash, H. M. A general technique to rank protein–ligand binding affinities and determine allosteric versus direct binding site competition in compound mixtures. J. Am. Chem. Soc. 126, 15495–15503 (2004).

  17. 17.

    Zuckermann, R. N., Kerr, J. M., Siani, M. A., Banville, S. C. & Santi, D. V. Identification of highest-affinity ligands by affinity selection from equimolar peptide mixtures generated by robotic synthesis. Proc. Natl Acad. Sci. USA 89, 4505–4509 (1992).

  18. 18.

    Annis, D. A., Nickbarg, E., Yang, X., Ziebell, M. R. & Whitehurst, C. E. Affinity selection-mass spectrometry screening techniques for small molecule drug discovery. Curr. Opin. Chem. Biol. 11, 518–526 (2007).

  19. 19.

    Comess, K. M. et al. An ultraefficient affinity-based high-throughout screening process: application to bacterial cell wall biosynthesis enzyme MurF. J. Biomol. Screen. 11, 743–754 (2006).

  20. 20.

    Dunayevskiy, Y. M., Lai, J.-J., Quinn, C., Talley, F. & Vouros, P. Mass spectrometric identification of ligands selected from combinatorial libraries using gel filtration. Rapid Commun. Mass Spectrom. 11, 1178–1184 (1997).

  21. 21.

    Huyer, G. et al. Affinity selection from peptide libraries to determine substrate specificity of protein tyrosine phosphatases. Anal. Biochem. 258, 19–30 (1998).

  22. 22.

    Vinogradov, A. A. et al. Library design-facilitated high-throughput sequencing of synthetic peptide libraries. ACS Comb. Sci. 19, 694–701 (2017).

  23. 23.

    Muckenschnabel, I., Falchetto, R., Mayr, L. M. & Filipuzzi, I. SpeedScreen: label-free liquid chromatography-mass spectrometry-based high-throughput screening for the discovery of orphan protein ligands. Anal. Biochem. 324, 241–249 (2004).

  24. 24.

    O’Connell, T. N., Ramsay, J., Rieth, S. F., Shapiro, M. J. & Stroh, J. G. Solution-based indirect affinity selection mass spectrometry—a general tool for high-throughput screening of pharmaceutical compound libraries. Anal. Chem. 86, 7413–7420 (2014).

  25. 25.

    Kussie, P. H. et al. Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain. Science 274, 948–953 (1996).

  26. 26.

    Vassilev, L. T. et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303, 844–848 (2004).

  27. 27.

    Li, C. et al. Systematic mutational analysis of peptide inhibition of the p53–MDM2/MDMX interactions. J. Mol. Biol. 398, 200–213 (2010).

  28. 28.

    Böttger, A. et al. Molecular characterization of the hdm2–p53 interaction. J. Mol. Biol. 269, 744–756 (1997).

  29. 29.

    Phan, J. et al. Structure-based design of high affinity peptides inhibiting the interaction of p53 with MDM2 and MDMX. J. Biol. Chem. 285, 2174–2183 (2010).

  30. 30.

    Pazgier, M. et al. Structural basis for high-affinity peptide inhibition of p53 interactions with MDM2 and MDMX. Proc. Natl Acad. Sci. USA 106, 4665–4670 (2009).

  31. 31.

    Liu, R., Li, X. & Lam, K. S. Combinatorial chemistry in drug discovery. Curr. Opin. Chem. Biol. 38, 117–126 (2017).

  32. 32.

    Ferrer, M. et al. Selection of gp41-mediated HIV-1 cell entry inhibitors from biased combinatorial libraries of non-natural binding elements. Nat. Struct. Biol. 6, 953–960 (1999).

  33. 33.

    Wang, X., Peng, L., Liu, R., Xu, B. & Lam, K. S. Applications of topologically segregated bilayer beads in combinatorial libraries. J. Pept. Res. 65, 130–138 (2005).

  34. 34.

    Lam, K. S. et al. A new type of synthetic peptide library for identifying ligand-binding activity. Nature 354, 82–84 (1991).

  35. 35.

    Lam, K. S. & Lebl, M. Selectide technology: bead-binding screening. Methods 6, 372–380 (1994).

  36. 36.

    Chen, C. L., Strop, P., Lebl, M. & Lam, K. S. Synthesis of libraries for bead-binding screening. Methods Enzymol. 267, 211–219 (1996).

  37. 37.

    Alluri, P. G., Reddy, M. M., Bachhawat-Sikder, K., Olivos, H. J. & Kodadek, T. Isolation of protein ligands from large peptoid libraries. J. Am. Chem. Soc. 125, 13995–14004 (2003).

  38. 38.

    Reddy, M. M., Bachhawat-Sikder, K. & Kodadek, T. Transformation of low-affinity lead compounds into high-affinity protein capture agents. Chem. Biol. 11, 1127–1137 (2004).

  39. 39.

    Zondlo, S. C., Lee, A. E. & Zondlo, N. J. Determinants of specificity of MDM2 for the activation domains of p53 and p65: Proline27 disrupts the MDM2-binding motif of p53. Biochemistry 45, 11945–11957 (2006).

  40. 40.

    Furman, J. L., Chiu, M. & Hunter, M. J. Early engineering approaches to improve peptide developability and manufacturability. AAPS J. 17, 111–120 (2015).

  41. 41.

    Chang, Y. S. et al. Stapled α-helical peptide drug development: a potent dual inhibitor of MDM2 and MDMX for p53-dependent cancer therapy. Proc. Natl Acad. Sci. USA 110, E3445–E3454 (2013).

  42. 42.

    Adamson, C. S. & Freed, E. O. Anti-HIV-1 therapeutics: from FDA-approved drugs to hypothetical future targets. Mol. Interv. 9, 70–74 (2009).

  43. 43.

    Bartonova, V. et al. Residues in the HIV-1 capsid assembly inhibitor binding site are essential for maintaining the assembly-competent quaternary structure of the capsid protein. J. Biol. Chem. 283, 32024–32033 (2008).

  44. 44.

    Ternois, F., Sticht, J., Duquerroy, S., Kräusslich, H. G. & Rey, F. A. The HIV-1 capsid protein C-terminal domain in complex with a virus assembly inhibitor. Nat. Struct. Mol. Biol. 12, 678–682 (2005).

  45. 45.

    Heitz, a, Le-Nguyen, D. & Chiche, L. Min-21 and min-23, the smallest peptides that fold like a cystine-stabilized β-sheet motif: design, solution structure, and thermal stability. Biochemistry 38, 10615–10625 (1999).

  46. 46.

    Celie, P. H. N. et al. UV-induced ligand exchange in MHC class I protein crystals. J. Am. Chem. Soc. 131, 12298–12304 (2009).

  47. 47.

    Zhang, H. et al. A cell-penetrating helical peptide as a potential HIV-1 inhibitor. J. Mol. Biol. 378, 565–580 (2008).

  48. 48.

    Wachter, F. et al. Mechanistic validation of a clinical lead stapled peptide that reactivates p53 by dual HDM2 and HDMX targeting. Oncogene 36, 2184–2190 (2017).

  49. 49.

    Goodnow, R. A., Dumelin, C. E. & Keefe, A. D. DNA-encoded chemistry: enabling the deeper sampling of chemical space. Nat. Rev. Drug Discov. 16, 131–147 (2017).

  50. 50.

    Rabideau, A. E., Liao, X. & Pentelute, B. L. Delivery of mirror image polypeptides into cells. Chem. Sci. 6, 648–653 (2015).

  51. 51.

    Vinogradov, A. A., Choo, Z. N., Totaro, K. A. & Pentelute, B. L. Macrocyclization of unprotected peptide isocyanates. Org. Lett. 18, 1226–1229 (2016).

  52. 52.

    Rodenko, B. et al. Class I major histocompatibility complexes loaded by a periodate trigger. J. Am. Chem. Soc. 131, 12305–12313 (2009).

  53. 53.

    Mijalis, A. J. et al. A fully automated flow-based approach for accelerated peptide synthesis. Nat. Chem. Biol. 13, 464–466 (2017).

  54. 54.

    Kim, Y.-W., Grossmann, T. N. & Verdine, G. L. Synthesis of all-hydrocarbon stapled α-helical peptides by ring-closing olefin metathesis. Nat. Protoc. 6, 761 (2011).

  55. 55.

    Illien, F. et al. Quantitative fluorescence spectroscopy and flow cytometry analyses of cell-penetrating peptides internalization pathways: optimization, pitfalls, comparison with mass spectrometry quantification. Sci. Rep. 6, 1–13 (2016).

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Acknowledgements

The Bettencourt Schueller Foundation is gratefully acknowledged for postdoctoral support to F.T. The Human Frontier Science Program Organization is thanked for a cross-disciplinary fellowship (LT000745/2014-C) to F.T. This work was supported in part by Servier, the Defense Advanced Research Projects Agency (award no. 023504-001 to B.L.P.), the NIH National Institute of General Medical Sciences (grant no. 5-R01-GM110535 to B.L.P.), a Bristol-Myers Squibb unrestricted grant in Synthetic Organic Chemistry (B.L.P.), and a Novartis Early Career Award (B.L.P). F. Lefoulon (Servier) is thankfully acknowledged for his suggestions and comments on the manuscript. FACS experiments were performed at the MIT Koch Institute Flow Cytometry Core. The authors thank the Biophysical Instrumentation Facility at MIT for providing access to the Octet Bio-Layer Interferometry System (NIH S10OD016326) and D. Pheasant for her technical assistance and B. Dass (Pall Fortebio) for his help with data analysis. We gratefully acknowledge A. Rabideau for peptide synthesis and characterization training, A. Quartararo for help with the Orbitrap LC–MS instrument, and A. Vinogradov and M. Simon for scientific discussions.

Author information

F.T. and B.L.P. conceived the study with input from Z.P.G. F.T. and B.L.P. directed the project and designed experiments. F.T. performed most experiments. Z.P.G. and A.B. contributed to OBOC screen design and experiments, and G.L. contributed to library purification and binder validation. F.T., Z.P.G., and B.L.P. wrote the manuscript. All authors contributed to the analysis, interpretation, and validation of the data.

Correspondence to Fayçal Touti or Bradley L. Pentelute.

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Supplementary information

Supplementary Information

Supplementary Tables 1–7, Supplementary Figures 1–39

Reporting Summary

Supplementary Note 1

Library 1 binder validation traces (Fig. 2 and Supplementary Fig. 13).

Supplementary Note 2

HPSEC and LC–MS traces for model binders; LC–MS characterization of numbered compounds.

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