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.

  • Article
  • Published:

How proteins bind macrocycles

Abstract

The potential utility of synthetic macrocycles (MCs) as drugs, particularly against low-druggability targets such as protein-protein interactions, has been widely discussed. There is little information, however, to guide the design of MCs for good target protein–binding activity or bioavailability. To address this knowledge gap, we analyze the binding modes of a representative set of MC–protein complexes. The results, combined with consideration of the physicochemical properties of approved macrocyclic drugs, allow us to propose specific guidelines for the design of synthetic MC libraries with structural and physicochemical features likely to favor strong binding to protein targets as well as good bioavailability. We additionally provide evidence that large, natural product–derived MCs can bind targets that are not druggable by conventional, drug-like compounds, supporting the notion that natural product–inspired synthetic MCs can expand the number of proteins that are druggable by synthetic small molecules.

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

Figure 1: Properties of MCs in the test set compared to MC drugs and to all oral drugs.
Figure 2: MC binding modes.
Figure 3: Extent and character of the protein-MC binding interface.
Figure 4: FTMap analysis of MC binding sites.
Figure 5: Comparison of binding modes for distinct MCs that bind at a common target site.

Similar content being viewed by others

Accession codes

Accessions

Protein Data Bank

References

  1. Hopkins, A.L. & Groom, C.R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002).

    CAS  PubMed  Google Scholar 

  2. Berg, T. Small-molecule inhibitors of protein-protein interactions. Curr. Opin. Drug Discov. Devel. 11, 666–674 (2008).

    CAS  PubMed  Google Scholar 

  3. Buchwald, P. Small-molecule protein-protein interaction inhibitors: therapeutic potential in light of molecular size, chemical space, and ligand binding efficiency considerations. IUBMB Life 62, 724–731 (2010).

    CAS  PubMed  Google Scholar 

  4. Whitty, A. & Kumaravel, G. Between a rock and a hard place? Nat. Chem. Biol. 2, 112–118 (2006).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  6. Hajduk, P.J. & Greer, J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat. Rev. Drug Discov. 6, 211–219 (2007).

    CAS  PubMed  Google Scholar 

  7. Scott, D.E. et al. Using a fragment-based approach to target protein-protein interactions. ChemBioChem 14, 332–342 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Morelli, X., Bourgeas, R. & Roche, P. Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2P2I). Curr. Opin. Chem. Biol. 15, 475–481 (2011).

    CAS  PubMed  Google Scholar 

  9. Basse, M.J. et al. 2P2Idb: a structural database dedicated to orthosteric modulation of protein-protein interactions. Nucleic Acids Res. 41, D824–D827 (2013).

    CAS  PubMed  Google Scholar 

  10. Singh, J., Petter, R.C., Baillie, T.A. & Whitty, A. The resurgence of covalent drugs. Nat. Rev. Drug Discov. 10, 307–317 (2011).

    CAS  PubMed  Google Scholar 

  11. Walensky, L.D. et al. Activation of apoptosis in vivo by a hydrocarbon-stapled BH3 helix. Science 305, 1466–1470 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Murray, J.K. & Gellman, S.H. Targeting protein-protein interactions: lessons from p53/MDM2. Biopolymers 88, 657–686 (2007).

    CAS  PubMed  Google Scholar 

  13. Raj, M., Bullock, B.N. & Arora, P.S. Plucking the high hanging fruit: a systematic approach for targeting protein-protein interactions. Bioorg. Med. Chem. 21, 4051–4057 (2013).

    CAS  PubMed  Google Scholar 

  14. Driggers, E.M., Hale, S.P., Lee, J. & Terrett, N.K. The exploration of macrocycles for drug discovery—an underexploited structural class. Nat. Rev. Drug Discov. 7, 608–624 (2008).

    CAS  PubMed  Google Scholar 

  15. Wessjohann, L.A., Ruijter, E., Garcia-Rivera, D. & Brandt, W. What can a chemist learn from nature's macrocycles?—a brief, conceptual view. Mol. Divers. 9, 171–186 (2005).

    CAS  PubMed  Google Scholar 

  16. Marsault, E. & Peterson, M.L. Macrocycles are great cycles: applications, opportunities, and challenges of synthetic macrocycles in drug discovery. J. Med. Chem. 54, 1961–2004 (2011).

    CAS  PubMed  Google Scholar 

  17. Mallinson, J. & Collins, I. Macrocycles in new drug discovery. Future Med. Chem. 4, 1409–1438 (2012).

    CAS  PubMed  Google Scholar 

  18. Yu, X. & Sun, D. Macrocyclic drugs and synthetic methodologies toward macrocycles. Molecules 18, 6230–6268 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Giordanetto, F. & Kihlberg, J. Macrocyclic drugs and clinical candidates: what can medicinal chemists learn from their properties? J. Med. Chem. 57, 278–295 (2014).

    CAS  PubMed  Google Scholar 

  20. Ganesan, A. The impact of natural products upon modern drug discovery. Curr. Opin. Chem. Biol. 12, 306–317 (2008).

    CAS  PubMed  Google Scholar 

  21. Lipinski, C.A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235–249 (2000).

    CAS  PubMed  Google Scholar 

  22. Vistoli, G., Pedretti, A. & Testa, B. Assessing drug-likeness—what are we missing? Drug Discov. Today 13, 285–294 (2008).

    CAS  PubMed  Google Scholar 

  23. Brandt, W., Haupt, V.J. & Wessjohann, L.A. Chemoinformatic analysis of biologically active macrocycles. Curr. Top. Med. Chem. 10, 1361–1379 (2010).

    CAS  PubMed  Google Scholar 

  24. Bockus, A.T., McEwen, C.M. & Lokey, R.S. Form and function in cyclic peptide natural products: a pharmacokinetic perspective. Curr. Top. Med. Chem. 13, 821–836 (2013).

    CAS  PubMed  Google Scholar 

  25. White, T.R. et al. On-resin N-methylation of cyclic peptides for discovery of orally bioavailable scaffolds. Nat. Chem. Biol. 7, 810–817 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Brenke, R. et al. Fragment-based identification of druggable 'hot spots' of proteins using Fourier domain correlation techniques. Bioinformatics 25, 621–627 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Landon, M.R., Lancia, D.R. Jr., Yu, J., Thiel, S.C. & Vajda, S. Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. J. Med. Chem. 50, 1231–1240 (2007).

    CAS  PubMed  Google Scholar 

  28. Kozakov, D. et al. Structural conservation of druggable hot spots in protein-protein interfaces. Proc. Natl. Acad. Sci. USA 108, 13528–13533 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).

    CAS  PubMed  Google Scholar 

  30. Jorgensen, W.L. The many roles of computation in drug discovery. Science 303, 1813–1818 (2004).

    CAS  PubMed  Google Scholar 

  31. Lo Conte, L., Chothia, C. & Janin, J. The atomic structure of protein-protein recognition sites. J. Mol. Biol. 285, 2177–2198 (1999).

    CAS  PubMed  Google Scholar 

  32. Nayal, M. & Honig, B. On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins 63, 892–906 (2006).

    CAS  PubMed  Google Scholar 

  33. Ciulli, A., Williams, G., Smith, A.G., Blundell, T.L. & Abell, C. Probing hot spots at protein-ligand binding sites: a fragment-based approach using biophysical methods. J. Med. Chem. 49, 4992–5000 (2006).

    CAS  PubMed  Google Scholar 

  34. DeLano, W.L. Unraveling hot spots in binding interfaces: progress and challenges. Curr. Opin. Struct. Biol. 12, 14–20 (2002).

    CAS  PubMed  Google Scholar 

  35. Landon, M.R., Lancia, D.R. Jr., Yu, J., Thiel, S.C. & Vajda, S. Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. J. Med. Chem. 50, 1231–1240 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Chuang, G.Y. et al. Binding hot spots and amantadine orientation in the influenza A virus M2 proton channel. Biophys. J. 97, 2846–2853 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Hall, D.H. et al. Robust identification of binding hot spots using continuum electrostatics: application to hen egg-white lysozyme. J. Am. Chem. Soc. 133, 20668–20671 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Buhrman, G. et al. Analysis of binding site hot spots on the surface of Ras GTPase. J. Mol. Biol. 413, 773–789 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Zerbe, B.S., Hall, D.R., Vajda, S., Whitty, A. & Kozakov, D. Relationship between hot spot residues and ligand binding hot spots in protein-protein interfaces. J. Chem. Inf. Model. 52, 2236–2244 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Hall, D.R., Ngan, C.H., Zerbe, B.S., Kozakov, D. & Vajda, S. Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. J. Chem. Inf. Model. 52, 199–209 (2012).

    CAS  PubMed  Google Scholar 

  41. Golden, M.S. et al. Comprehensive experimental and computational analysis of binding energy hot spots at the NF-κB essential modulator/IKKβ protein-protein interface. J. Am. Chem. Soc. 135, 6242–6256 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Roe, S.M. et al. Structural basis for inhibition of the Hsp90 molecular chaperone by the antitumor antibiotics radicicol and geldanamycin. J. Med. Chem. 42, 260–266 (1999).

    CAS  PubMed  Google Scholar 

  43. Hajduk, P.J., Huth, J.R. & Fesik, S.W. Druggability indices for protein targets derived from NMR-based screening data. J. Med. Chem. 48, 2518–2525 (2005).

    CAS  PubMed  Google Scholar 

  44. Landon, M.R. et al. Novel druggable hot spots in avian influenza neuraminidase H5N1 revealed by computational solvent mapping of a reduced and representative receptor ensemble. Chem. Biol. Drug Des. 71, 106–116 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Rand, A.C. et al. Optimizing PK properties of cyclic peptides: the effect of side chain substitutions on permeability and clearance. Medchemcomm. 3, 1282–1289 (2012).

    CAS  PubMed  Google Scholar 

  46. Hopkins, A.L., Groom, C.R. & Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430–431 (2004).

    PubMed  Google Scholar 

  47. Hajduk, P.J. Fragment-based drug design: how big is too big? J. Med. Chem. 49, 6972–6976 (2006).

    CAS  PubMed  Google Scholar 

  48. Veber, D.F. et al. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002).

    CAS  PubMed  Google Scholar 

  49. Wu, C.Y. & Benet, L.Z. Predicting drug disposition via application of BCS: transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm. Res. 22, 11–23 (2005).

    CAS  PubMed  Google Scholar 

  50. Vieth, M. et al. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 47, 224–232 (2004).

    CAS  PubMed  Google Scholar 

  51. Lee, B. & Richards, F.M. The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol. 55, 379–400 (1971).

    CAS  PubMed  Google Scholar 

  52. Hartshorn, M.J. et al. Diverse, high-quality test set for the validation of protein-ligand docking performance. J. Med. Chem. 50, 726–741 (2007).

    CAS  PubMed  Google Scholar 

  53. Kozakov, D. et al. Structural conservation of druggable hot spots in protein-protein interfaces. Proc. Natl. Acad. Sci. USA 108, 13528–13533 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Hall, D.R., Ngan, C.H., Zerbe, B.S., Kozakov, D. & Vajda, S. Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. J. Chem. Inf. Model. 52, 199–209 (2012).

    CAS  PubMed  Google Scholar 

  55. Mann, H. & Whitney, D. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).

    Google Scholar 

  56. Anderson, T. & Darling, D. Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann. Math. Stat. 23, 193–212 (1952).

    Google Scholar 

Download references

Acknowledgements

This research was supported by US National Institutes of Health grants GM094551 to A.W., S.V. and J.A.P. and GM064700 to S.V. and NIH diversity supplement GM094551-01S1 to E.A.V.

Author information

Authors and Affiliations

Authors

Contributions

A.W., S.V. and D.K. conceived of and directed the study; E.A.V. performed the calculations and analysis, with advice from D.B.; S.C., under the supervision of J.A.P., analyzed the physicochemical properties of the MC drugs; and A.W. and E.A.V. wrote the manuscript with input from S.V., D.K. and S.C.

Corresponding authors

Correspondence to Dima Kozakov, Sandor Vajda or Adrian Whitty.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–6 and Supplementary Tables 1–7. (PDF 2233 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Villar, E., Beglov, D., Chennamadhavuni, S. et al. How proteins bind macrocycles. Nat Chem Biol 10, 723–731 (2014). https://doi.org/10.1038/nchembio.1584

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio.1584

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing