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Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Hosseinzadeh, Parisa, Watson, Paris R., Craven, Timothy W., Li, Xinting, Rettie, Stephen, Pardo-Avila, Fátima, Bera, Asim K., Mulligan, Vikram Khipple, Lu, Peilong, Ford, Alexander S., Weitzner, Brian D., Stewart, Lance J., Moyer, Adam P., Di Piazza, Maddalena, Whalen, Joshua G., Greisen, Per, Christianson, David W., Baker, David·2021·Nature Communications
DOI 10.1038/s41467-021-23609-8
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Abstract

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational “anchor extension” methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC 50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

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Research works printed but not linked: 73

  1. Henninot, A., Collins, J. C. & Nuss, J. M. The current state of peptide drug discovery: back to the future? J. Med. Chem. 61, 1382–1414 (2018). · DOI · Paper page
  2. Reichart, F. et al. Selective targeting of integrin αvβ8 by a highly active cyclic peptide. J. Med. Chem. 62, 2024–2037 (2019). · DOI
  3. Vinogradov, A. A., Yin, Y. & Suga, H. Macrocyclic peptides as drug candidates: recent progress and remaining challenges. J. Am. Chem. Soc. 141, 4167–4181 (2019). · DOI · Paper page
  4. Touti, F., Gates, Z. P., Bandyopadhyay, A., Lautrette, G. & Pentelute, B. L. In-solution enrichment identifies peptide inhibitors of protein–protein interactions. Nat. Chem. Biol. 15, 410–418 (2019). · DOI
  5. Wuo, M. G. & Arora, P. S. Engineered protein scaffolds as leads for synthetic inhibitors of protein–protein interactions. Curr. Opin. Chem. Biol. 44, 16–22 (2018). · DOI
  6. Chan, L. Y., Craik, D. J. & Daly, N. L. Dual-targeting anti-angiogenic cyclic peptides as potential drug leads for cancer therapy. Sci. Rep. 6, 35347 (2016). · doi:10.1038/srep35347
  7. Rhodes, C. A. et al. Cell-permeable bicyclic peptidyl inhibitors against NEMO-IκB kinase interaction directly from a combinatorial library. J. Am. Chem. Soc. 140, 12102–12110 (2018). · DOI
  8. Zorzi, A., Deyle, K. & Heinis, C. Cyclic peptide therapeutics: past, present and future. Curr. Opin. Chem. Biol. 38, 24–29 (2017). · DOI · Paper page
  9. Liu, R., Li, X., Xiao, W. & Lam, K. S. Tumor-targeting peptides from combinatorial libraries. Adv. Drug Deliv. Rev. 110–111, 13–37 (2017). · DOI · Paper page
  10. Ashby, M., Petkova, A., Gani, J., Mikut, R. & Hilpert, K. Use of peptide libraries for identification and optimization of novel antimicrobial peptides. Curr. Top. Med. Chem. 17, 537–553 (2017). · DOI
  11. Passioura, T., Liu, W., Dunkelmann, D., Higuchi, T. & Suga, H. Display selection of exotic macrocyclic peptides expressed under a radically reprogrammed 23 amino acid genetic code. J. Am. Chem. Soc. 140, 11551–11555 (2018). · DOI
  12. Kawakami, T. & Murakami, H. Genetically encoded libraries of nonstandard peptides. J. Nucleic Acids 2012, 1–15 (2012). · DOI
  13. Kawamura, A. et al. Highly selective inhibition of histone demethylases by de novo macrocyclic peptides. Nat. Commun. 8, 14773 (2017). · DOI
  14. Kawakami, T., Murakami, H. & Suga, H. Messenger RNA-programmed incorporation of multiple N-methyl-amino acids into linear and cyclic peptides. Chem. Biol. 15, 32–42 (2008). · DOI · Paper page
  15. Huang, Y., Wiedmann, M. M. & Suga, H. RNA display methods for the discovery of bioactive macrocycles. Chem. Rev. 119, 10360–10391 (2019). · DOI · Paper page
  16. Gless, B. H. & Olsen, C. A. Direct peptide cyclization and one-pot modification using the MeDbz linker. J. Org. Chem. 83, 10525–10534 (2018). · DOI
  17. Mudd, G. E. et al. Identification and optimization of EphA2-selective bicycles for the delivery of cytotoxic payloads. J. Med. Chem. 63, 4107–4116 (2020). · DOI
  18. Jafari, M. R. et al. Discovery of light-responsive ligands through screening of a light-responsive genetically encoded library. ACS Chem. Biol. 9, 443–450 (2014). · DOI
  19. Guéret, S. M. et al. Macrocyclic modalities combining peptide epitopes and natural product fragments. J. Am. Chem. Soc. 142, 4904–4915 (2020). · DOI
  20. Siegert, T. R., Bird, M. J., Makwana, K. M. & Kritzer, J. A. Analysis of loops that mediate protein–protein interactions and translation into submicromolar inhibitors. J. Am. Chem. Soc. 138, 12876–12884 (2016). · DOI
  21. Sedan, Y., Marcu, O., Lyskov, S. & Schueler-Furman, O. Peptiderive server: derive peptide inhibitors from protein-protein interactions. Nucleic Acids Res 44, W536–W541 (2016). · DOI
  22. 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. 54, 8896–8927 (2015). · DOI · Paper page
  23. Satoh, T. et al. Bioactive peptide design based on protein surface epitopes: a cyclic heptapeptide mimics CD4 domain 1 CC′ loop and inhibits CD4 biological function. J. Biol. Chem. 272, 12175–12180 (1997). · doi:10.1074/jbc.272.18.12175
  24. Rooklin, D. et al. Targeting unoccupied surfaces on protein–protein interfaces. J. Am. Chem. Soc. 139, 15560–15563 (2017). · DOI
  25. Damaskos, C. et al. Histone deacetylase inhibitors: an attractive therapeutic strategy against breast cancer. Anticancer Res 37, 35–46 (2017). · DOI · Paper page
  26. Li, Y. & Seto, E. HDACs and HDAC inhibitors in cancer development and therapy. Cold Spring Harb. Perspect. Med. 6, a026831 (2016). · DOI · Paper page
  27. Haberland, M., Montgomery, R. L. & Olson, E. N. The many roles of histone deacetylases in development and physiology: implications for disease and therapy. Nat. Rev. Genet. 10, 32–42 (2009). · DOI · Paper page
  28. Lombardi, P. M., Cole, K. E., Dowling, D. P. & Christianson, D. W. Structure, mechanism, and inhibition of histone deacetylases and related metalloenzymes. Curr. Opin. Struct. Biol. 21, 735–743 (2011). · DOI · Paper page
  29. Kim, B. & Hong, J. An overview of naturally occurring histone deacetylase inhibitors. Curr. Top. Med. Chem. 14, 2759–2782 (2015). · DOI
  30. Salvador, L. A. & Luesch, H. Discovery and mechanism of natural products as modulators of histone acetylation. Curr. Drug Targets 13, 1029–1047 (2012). · DOI
  31. Cole, K. E., Dowling, D. P., Boone, M. A., Phillips, A. J. & Christianson, D. W. Structural basis of the antiproliferative activity of largazole, a depsipeptide inhibitor of the histone deacetylases. J. Am. Chem. Soc. 133, 12474–12477 (2011). · DOI · Paper page
  32. Bowers, A. A. et al. Synthesis and histone deacetylase inhibitory activity of largazole analogs: alteration of the zinc-binding domain and macrocyclic scaffold. Org. Lett. 11, 1301–1304 (2009). · DOI
  33. Hosseinzadeh, P. et al. Comprehensive computational design of ordered peptide macrocycles. Science 358, 1461–1466 (2017). · DOI · Paper page
  34. Ladbury, J. E. Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem. Biol. 3, 973–980 (1996). · DOI · Paper page
  35. Bartlett, G. J., Choudhary, A., Raines, R. T. & Woolfson, D. N. n→π* interactions in proteins. Nat. Chem. Biol. 6, 615–620 (2010). · DOI · Paper page
  36. Porter, N. J. & Christianson, D. W. Binding of the microbial cyclic tetrapeptide trapoxin A to the class I histone deacetylase HDAC8. ACS Chem. Biol. 12, 2281–2286 (2017). · DOI
  37. Procko, E. et al. Computational design of a protein-based enzyme inhibitor. J. Mol. Biol. 425, 3563–3575 (2013). · DOI
  38. Mulligan, V. K. et al. Computationally-designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1. Proc. Natl Acad. Sci. 118, 12 (2021). · DOI
  39. Guardiola, S. et al. Target-templated de novo design of macrocyclic d-/l-peptides: discovery of drug-like inhibitors of PD-1. Chem. Sci. (2021) https://doi.org/10.1039/D1SC01031J. · DOI
  40. Kim, H.-J. & Bae, S.-C. Histone deacetylase inhibitors: molecular mechanisms of action and clinical trials as anti-cancer drugs. Am. J. Transl. Res. 3, 166–179 (2011).
  41. Patgiri, A., Jochim, A. L. & Arora, P. S. A hydrogen bond surrogate approach for stabilization of short peptide sequences in α-helical conformation. Acc. Chem. Res. 41, 1289–1300 (2008). · DOI · Paper page
  42. DeLorbe, J. E. et al. Thermodynamic and structural effects of conformational constraints in protein−ligand interactions. Entropic paradoxy associated with ligand preorganization. J. Am. Chem. Soc. 131, 16758–16770 (2009). · DOI
  43. Lokesh, G. L., Muralidhara, B. K., Negi, S. S. & Natarajan, A. Thermodynamics of phosphopeptide tethering to BRCT: the structural minima for inhibitor design. J. Am. Chem. Soc. 129, 10658–10659 (2007). · DOI
  44. Wallraven, K. et al. Adapting free energy perturbation simulations for large macrocyclic ligands: how to dissect contributions from direct binding and free ligand flexibility. Chem. Sci. 11, 2269–2276 (2020). · DOI
  45. Kamenik, A. S., Lessel, U., Fuchs, J. E., Fox, T. & Liedl, K. R. Peptidic macrocycles - conformational sampling and thermodynamic characterization. J. Chem. Inf. Model. 58, 982–992 (2018). · DOI
  46. Yan, Y., Zhang, D. & Huang, S.-Y. Efficient conformational ensemble generation of protein-bound peptides. J. Cheminformatics 9, 59 (2017). · DOI
  47. London, N., Movshovitz-Attias, D. & Schueler-Furman, O. The structural basis of peptide-protein binding strategies. Struct. Lond. Engl. 18, 188–199 (2010). 1993.
  48. Ahmed, M. H. et al. Bound water at protein-protein Interfaces: partners, roles and hydrophobic bubbles as a conserved motif. PLoS ONE 6, e24712 (2011). · DOI
  49. Malde, A. K., Hill, T. A., Iyer, A. & Fairlie, D. P. Crystal structures of protein-bound cyclic peptides. Chem. Rev. 119, 9861–9914 (2019). · DOI
  50. Dagliyan, O., Proctor, E. A., D’Auria, K. M., Ding, F. & Dokholyan, N. V. Structural and dynamic determinants of protein-peptide recognition. Struct. Lond. Engl. 19, 1837–1845 (2011). 1993.
  51. Antes, I. DynaDock: a new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins Struct. Funct. Bioinforma. 78, 1084–1104 (2010). · DOI · Paper page
  52. Alogheli, H., Olanders, G., Schaal, W., Brandt, P. & Karlén, A. Docking of macrocycles: comparing rigid and flexible docking in glide. J. Chem. Inf. Model. 57, 190–202 (2017). · DOI
  53. Wang, D. Computational studies on the histone deacetylases and the design of selective histone deacetylase inhibitors. Curr. Top. Med. Chem. 9, 241–256 (2009). · DOI
  54. Bhardwaj, G. et al. Accurate de novo design of hyperstable constrained peptides. Nature 538, 329–335 (2016). · DOI · Paper page
  55. Ford, A. S., Weitzner, B. D. & Bahl, C. D. Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation. Protein Sci. 29, 43–51 (2020). · DOI
  56. Jurrus, E. et al. Improvements to the APBS biomolecular solvation software suite: Improvements to the APBS Software Suite. Protein Sci. 27, 112–128 (2018). · DOI · Paper page
  57. Osko, J. D. & Christianson, D. W. Methods for the expression, purification, and crystallization of histone deacetylase 6–inhibitor complexes. Methods Enzymol. 626, 447–474 (2019). https://doi.org/10.1016/bs.mie.2019.06.028 · DOI
  58. Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997). · DOI · Paper page
  59. Battye, T. G. G., Kontogiannis, L., Johnson, O., Powell, H. R. & Leslie, A. G. W. iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLM. Acta Crystallogr. D. Biol. Crystallogr. 67, 271–281 (2011). · DOI · Paper page
  60. Evans, P. R. & Murshudov, G. N. How good are my data and what is the resolution?. Acta Crystallogr. D. Biol. Crystallogr. 69, 1204–1214 (2013). · DOI · Paper page
  61. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D. Biol. Crystallogr. 66, 213–221 (2010). · DOI · Paper page
  62. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D. Biol. Crystallogr 66, 486–501 (2010). · DOI · Paper page
  63. Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018). · DOI · Paper page
  64. McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007). · DOI · Paper page
  65. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D. Biol. Crystallogr. 66, 12–21 (2010). · DOI · Paper page
  66. Lindorff-Larsen, K. et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field: improved protein side-chain potentials. Proteins Struct. Funct. Bioinforma. 78, 1950–1958 (2010). · DOI · Paper page
  67. Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015). · DOI · Paper page
  68. Leszczynski, J. & K. Shukla, M. K. Practical Aspects of Computational Chemistry. (Springer, 2009). · DOI
  69. Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984). · DOI · Paper page
  70. Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007). · DOI · Paper page
  71. Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995). · DOI · Paper page
  72. Páll, S. & Hess, B. A flexible algorithm for calculating pair interactions on SIMD architectures. Comput. Phys. Commun. 184, 2641–2650 (2013). · DOI · Paper page
  73. Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaijie, J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997). · DOI · Paper page

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Metadata

Authors
Hosseinzadeh, P., Watson, P. R., Craven, T. W., Li, X., Rettie, S., Pardo-Avila, F., Bera, A. K., Mulligan, V. K., Lu, P., Ford, A. S., Weitzner, B. D., Stewart, L. J., Moyer, A. P., Di Piazza, M., Whalen, J. G., Greisen, P., Christianson, D. W., & Baker, D.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-06-07
Volume
12
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-021-23609-8
Cited by
106
References
7
Language
en
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Hosseinzadeh, P., Watson, P. R., Craven, T. W., Li, X., Rettie, S., Pardo-Avila, F., Bera, A. K., Mulligan, V. K., Lu, P., Ford, A. S., Weitzner, B. D., Stewart, L. J., Moyer, A. P., Di Piazza, M., Whalen, J. G., Greisen, P., Christianson, D. W., & Baker, D. (2021). Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites. Nature Communications. https://doi.org/10.1038/s41467-021-23609-8

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