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Toward Expanded Diversity of Host–Guest Interactions via Synthesis and Characterization of Cyclodextrin Derivatives

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Abstract

Researchers developing software to predict the binding constants of small molecules for proteins have, in recent years, turned to host–guest systems as simple, computationally tractable model systems to test and improve these computational methods. However, taking full advantage of this strategy requires aqueous host–guest systems that probe a greater diversity of chemical interactions. Here, we advance the development of an experimental platform to generate such systems by building on the cyclodextrin (CD) class of hosts. The secondary face derivative mono-3-carboxypropionamido-β-cyclodextrin (CP-β-CD) was synthesized in a one-pot strategy with 87% yield, and proved to have much greater aqueous solubility than native β-CD. The complexation of anionic CP-β-CD with the cationic drug rimantadine hydrochloride was explored using one- and two-dimensional nuclear magnetic resonance; NOESY analysis showed secondary face binding of the ammonium moiety of the guest, based on cross-correlations between the amic acid functionality and the side-chain of rimantadine. Isothermal titration calorimetry was furthermore used to determine the standard Gibbs energy and enthalpy for this binding reaction, and the results were compared with those of rimantadine with native β-CD.

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References

  1. Gilson, M.K., Zhou, H.-X.: Calculation of protein–ligand binding affinities. Ann. Rev. Biophys. Biomol. Struct. 36, 21–42 (2007)

    Article  CAS  Google Scholar 

  2. Kuntz, I.D., Blaney, J.M., Oatley, S.J., Langridge, R., Ferrin, T.E.: A geometric approach to macromolecule–ligand interactions. J. Mol. Biol. 161, 269–288 (1982)

    Article  CAS  Google Scholar 

  3. Goodsell, D.S., Olson, A.J.: Automated docking of substrates to proteins by simulated annealing. Protein Struct. Funct. Gen. 8, 195–202 (1990)

    Article  CAS  Google Scholar 

  4. Abagyan, R., Totrov, M., Kuznetsov, D.: ICM—a new method for protein modeling and design. Applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488–506 (1994)

    Article  CAS  Google Scholar 

  5. Apostolakis, J., Pluckthun, A., Caflisch, A.: Docking small ligands in flexible binding sites. J. Comput. Chem. 19, 21–37 (1998)

    Article  CAS  Google Scholar 

  6. Charifson, P.S., Corkery, J.J., Murcko, M.A., Walters, W.P.: Consensus scoring: a method for obtaining improved hit rates from docking databases of three-gimensional structures into proteins. J. Med. Chem. 42, 5100–5109 (1999)

    Article  CAS  Google Scholar 

  7. Clark, K.P.: Ajay: flexible ligand docking without parameter adjustment across four ligand–receptor complexes. J. Comput. Chem. 16, 1210–1226 (1995)

    Article  CAS  Google Scholar 

  8. David, L., Luo, R., Gilson, M.K.: Ligand–receptor docking with the Mining Minima optimizer. J. Comput. Aided Mol. Des. 15, 157–171 (2001)

    Article  CAS  Google Scholar 

  9. Trott, O., Olson, A.J.: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010). https://doi.org/10.1002/jcc.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jain, A.N.: Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem. 46, 499–511 (2003). https://doi.org/10.1021/jm020406h

    Article  CAS  PubMed  Google Scholar 

  11. Halgren, T.A., Murphy, R.B., Friesner, R.A., Beard, H.S., Frye, L.L., Pollard, W.T., Banks, J.L.: Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47, 1750–1759 (2004). https://doi.org/10.1021/jm030644s

    Article  CAS  PubMed  Google Scholar 

  12. Abel, R., Wang, L., Mobley, D.L., Friesner, R.A.: A critical review of validation, blind testing, and real-world use of Alchemical protein–ligand binding free energy calculations. Curr. Top. Med. Chem. 17, 2577–2585 (2017)

    Article  CAS  Google Scholar 

  13. Gallicchio, E., Lapelosa, M., Levy, R.M.: Binding energy distribution analysis method (BEDAM) for estimation of protein–ligand binding affinities. J. Chem. Theory Comput. 6, 2961–2977 (2010). https://doi.org/10.1021/ct1002913

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gilson, M.K., Given, J.A., Bush, B.L., McCammon, J.A.: The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys. J. 72, 1047–1069 (1997)

    Article  CAS  Google Scholar 

  15. Hansen, N., van Gunsteren, W.F.: Practical aspects of free-energy calculations: a review. J. Chem. Theory Comput. 10, 2632–2647 (2014). https://doi.org/10.1021/ct500161f

    Article  CAS  PubMed  Google Scholar 

  16. Jorgensen, W.L., Buckner, J.K., Boudon, S., Tirado-Rives, J.: Efficient computation of absolute free energies of binding by computer simulations. Application to the methane dimer in water. J. Chem. Phys. 89, 3742–3746 (1988). https://doi.org/10.1063/1.454895

    Article  CAS  Google Scholar 

  17. Lau, F.T.K., Karplus, M.: Molecular recognition in proteins: simulation analysis of substrate binding by a tyrosyl-tRNA synthetase mutant. J. Mol. Biol. 236, 1049–1066 (1994). https://doi.org/10.1016/0022-2836(94)90011-6

    Article  CAS  PubMed  Google Scholar 

  18. Simonson, T., Archontis, G., Karplus, M.: Free energy simulations come of age: protein–ligand recognition. Acc. Chem. Res. 35, 430–437 (2002). https://doi.org/10.1021/ar010030m

    Article  CAS  PubMed  Google Scholar 

  19. Tembe, B.L., McCammon, J.A.: Ligand–receptor interactions. Comput. Chem. 8, 281–283 (1984)

    Article  CAS  Google Scholar 

  20. Woo, H.-J., Roux, B.: Calculation of absolute protein–ligand binding free energy from computer simulations. Proc. Natl. Acad. Sci. USA 102, 6825–6830 (2005). https://doi.org/10.1073/pnas.0409005102

    Article  CAS  PubMed  Google Scholar 

  21. Homeyer, N., Stoll, F., Hillisch, A., Gohlke, H.: Binding free energy calculations for lead optimization: assessment of their accuracy in an industrial drug design context. J. Chem. Theory Comput. 10, 3331–3344 (2014). https://doi.org/10.1021/ct5000296

    Article  CAS  PubMed  Google Scholar 

  22. Kuhn, B., Tichý, M., Wang, L., Robinson, S., Martin, R.E., Kuglstatter, A., Benz, J., Giroud, M., Schirmeister, T., Abel, R., Diederich, F., Hert, J.: Prospective evaluation of free energy calculations for the prioritization of cathepsin L inhibitors. J. Med. Chem. 60, 2485–2497 (2017). https://doi.org/10.1021/acs.jmedchem.6b01881

    Article  CAS  PubMed  Google Scholar 

  23. Williams-Noonan, B.J., Yuriev, E., Chalmers, D.K.: Free energy methods in drug design: prospects of “Alchemical Perturbation” in medicinal chemistry. J. Med. Chem. (2017). https://doi.org/10.1021/acs.jmedchem.7b00681

    Article  PubMed  Google Scholar 

  24. Cournia, Z., Allen, B., Sherman, W.: Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J. Chem. Inf. Model. 57, 2911–2937 (2017). https://doi.org/10.1021/acs.jcim.7b00564

    Article  CAS  PubMed  Google Scholar 

  25. Sherborne, B., Shanmugasundaram, V., Cheng, A.C., Christ, C.D., DesJarlais, R.L., Duca, J.S., Lewis, R.A., Loughney, D.A., Manas, E.S., McGaughey, G.B., Peishoff, C.E., van Vlijmen, H.: Collaborating to improve the use of free-energy and other quantitative methods in drug discovery. J. Comput. Aided Mol. Des. 30, 1139–1141 (2016). https://doi.org/10.1007/s10822-016-9996-y

    Article  CAS  PubMed  Google Scholar 

  26. Lybrand, T.P., McCammon, J.A., Wipff, G.: Theoretical calculation of relative binding affinity in host–guest systems. Proc. Natl. Acad. Sci. USA 83, 833–835 (1986)

    Article  CAS  Google Scholar 

  27. Pitera, J., Kollman, P.: Designing an optimum guest for a host using multimolecule free energy calculations: predicting the best ligand for Rebek’s “tennis ball”. J. Am. Chem. Soc. 120, 7557–7567 (1998). https://doi.org/10.1021/ja973028s

    Article  CAS  Google Scholar 

  28. Kaminski, G.A., Jorgensen, W.L.: Host–guest chemistry of rotaxanes and catenanes: application of a polarizable all-atom force field to cyclobis(paraquat-p-phenylene) complexes with disubstituted benzenes and biphenyls. J. Chem. Soc. Perkin Trans. 2, 2365–2375 (1999). https://doi.org/10.1039/A905160K

    Article  Google Scholar 

  29. Chen, W., Chang, C.E., Gilson, M.K.: Calculation of cyclodextrin binding affinities: energy, entropy, and implications for drug design. Biophys. J. 87, 3035–3049 (2004). https://doi.org/10.1529/biophysj.104.049494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chang, C.-E., Gilson, M.K.: Free energy, entropy, and induced fit in host-guest recognition: calculations with the second-generation mining minima algorithm. J. Am. Chem. Soc. 126, 13156–13164 (2004)

    Article  CAS  Google Scholar 

  31. Rogers, K.E., Ortiz-Sánchez, J.M., Baron, R., Fajer, M., de Oliveira, C.A.F., McCammon, J.A.: On the role of dewetting transitions in host–guest binding free energy calculations. J. Chem. Theory Comput. 9, 46–53 (2013). https://doi.org/10.1021/ct300515n

    Article  CAS  PubMed  Google Scholar 

  32. Wickstrom, L., He, P., Gallicchio, E., Levy, R.M.: Large scale affinity calculations of cyclodextrin host–guest complexes: understanding the role of reorganization in the molecular recognition process. J. Chem. Theory Comput. 9, 3136–3150 (2013). https://doi.org/10.1021/ct400003r

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bell, D.R., Qi, R., Jing, Z., Xiang, J.Y., Mejias, C., Schnieders, M.J., Ponder, J.W., Ren, P.: Calculating binding free energies of host–guest systems using AMOEBA polarizable force field. Phys. Chem. Chem. Phys. 18, 30261–30269 (2016). https://doi.org/10.1039/c6cp02509a

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Henriksen, N.M., Gilson, M.K.: Evaluating force field performance in thermodynamic calculations of cyclodextrin host-guest binding: water models, partial charges, and host force field parameters. J. Chem. Theory Comput. 13, 4253–4269 (2017). https://doi.org/10.1021/acs.jctc.7b00359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mobley, D.L., Gilson, M.K.: Predicting binding free energies: frontiers and benchmarks. Ann. Rev. Biophys. 46, 531–558 (2017)

    Article  CAS  Google Scholar 

  36. Gao, K., Yin, J., Henriksen, N.M., Fenley, A.T., Gilson, M.K.: Binding enthalpy calculations for a neutral host–guest pair yield widely divergent salt effects across water models. J. Chem. Theory Comput. 11, 4555–4564 (2015)

    Article  CAS  Google Scholar 

  37. Yin, J., Fenley, A.T., Henriksen, N.M., Gilson, M.K.: Toward improved force-field accuracy through sensitivity analysis of host–guest binding thermodynamics. J. Phys. Chem. B 119, 10145–10155 (2015). https://doi.org/10.1021/acs.jpcb.5b04262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bradshaw, R.T., Essex, J.W.: Evaluating parametrization protocols for hydration free energy calculations with the AMOEBA polarizable force field. J. Chem. Theory Comput. 12, 3871–3883 (2016). https://doi.org/10.1021/acs.jctc.6b00276

    Article  CAS  PubMed  Google Scholar 

  39. Buck, M., Bouguet-Bonnet, S., Pastor, R.W., MacKerell, A.D.: Importance of the CMAP correction to the CHARMM22 protein force field: dynamics of hen lysozyme. Biophys. J. 90, L36–L38 (2006). https://doi.org/10.1529/biophysj.105.078154

    Article  CAS  PubMed  Google Scholar 

  40. Jorgensen, W.L., Tirado-Rives, J.: The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc. 110, 1657–1666 (1988). https://doi.org/10.1021/ja00214a001

    Article  CAS  PubMed  Google Scholar 

  41. Kang, M., Smith, P.E.: A Kirkwood-Buff derived force field for amides. J. Comput. Chem. 27, 1477–1485 (2006). https://doi.org/10.1002/jcc.20441

    Article  CAS  PubMed  Google Scholar 

  42. Oostenbrink, C., Villa, A., Mark, A.E., van Gunsteren, W.F.: A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J. Comput. Chem. 25, 1656–1676 (2004). https://doi.org/10.1002/jcc.20090

    Article  CAS  PubMed  Google Scholar 

  43. Piana, S., Donchev, A.G., Robustelli, P., Shaw, D.E.: Water dispersion interactions strongly influence simulated structural properties of disordered protein States. J. Phys. Chem. B 119, 5113–5123 (2015). https://doi.org/10.1021/jp508971m

    Article  CAS  PubMed  Google Scholar 

  44. Ploetz, E.A., Smith, P.E.: A Kirkwood-Buff force field for the aromatic amino acids. Phys. Chem. Chem. Phys. PCCP 13, 18154–18167 (2011). https://doi.org/10.1039/c1cp21883b

    Article  CAS  PubMed  Google Scholar 

  45. Song, D., Wang, W., Ye, W., Ji, D., Luo, R., Chen, H.-F.: ff14IDPs force field improving the conformation sampling of intrinsically disordered proteins. Chem. Biol. Drug Des. 89, 5–15 (2017). https://doi.org/10.1111/cbdd.12832

    Article  CAS  PubMed  Google Scholar 

  46. Weerasinghe, S., Gee, M., Kang, M., Bentenitis, N., Smith, P.: Developing Force Fields from the Microscopic Structure of Solutions: The Kirkwood-Buff Approach. Modelling Solvent Environment, pp. 55–76. Wiley-VCH, Weinheim (2010). https://doi.org/10.1002/9783527629251.ch3

    Book  Google Scholar 

  47. Davis, M.E., Brewster, M.E.: Cyclodextrin-based pharmaceutics: past, present and future. Nat. Rev. Drug Discov. 3, 1023–1035 (2004). https://doi.org/10.1038/nrd1576

    Article  CAS  PubMed  Google Scholar 

  48. Brewster, M.E., Loftsson, T.: Cyclodextrins as pharmaceutical solubilizers. Adv. Drug Deliv. Rev. 59, 645–666 (2007). https://doi.org/10.1016/j.addr.2007.05.012

    Article  CAS  PubMed  Google Scholar 

  49. Loftsson, T., Brewster, M.E.: Pharmaceutical applications of cyclodextrins: basic science and product development. J. Pharm. Pharmacol. 62, 1607–1621 (2010). https://doi.org/10.1111/j.2042-7158.2010.01030.x

    Article  CAS  PubMed  Google Scholar 

  50. Yanli, C., Caixia, W., Jianwei, M., Yongping, Y.: A facile and practical approach to randomly methylated beta-cyclodextrin. J. Chem. Technol. Biotechnol. 85, 248–251 (2010). https://doi.org/10.1002/jctb.2295

    Article  CAS  Google Scholar 

  51. Khan, A.R., Forgo, P., Stine, K.J., D’Souza, V.T.: Methods for selective modifications of cyclodextrins. Chem. Rev. 98, 1977–1996 (1998). https://doi.org/10.1021/cr970012b

    Article  CAS  PubMed  Google Scholar 

  52. Ashton, P.R., Königer, R., Stoddart, J.F., Alker, D., Harding, V.D.: Amino acid derivatives of β-cyclodextrin. J. Org. Chem. 61, 903–908 (1996). https://doi.org/10.1021/jo951396d

    Article  CAS  Google Scholar 

  53. Carrazana, J., Jover, A., Meijide, F., Soto, V.H., Tato, J.V.: Complexation of adamantyl compounds by β-cyclodextrin and monoaminoderivatives. J. Phys. Chem. B 109, 9719–9726 (2005). https://doi.org/10.1021/jp0505781

    Article  CAS  PubMed  Google Scholar 

  54. Kantonen, S.A., Henriksen, N.M., Gilson, M.K.: Accounting for apparent deviations between calorimetric and van’t Hoff enthalpies. Biochim. Biophys. Acta (2017). https://doi.org/10.1016/j.bbagen.2017.11.020

    Article  Google Scholar 

  55. Dodzuik, E.: Cyclodextrins and Their Complexes. Wiley-VCH, Weinheim (2006)

    Book  Google Scholar 

  56. Hwang, T.L., Shaka, A.J.: Water suppression that works. Excitation sculpting using arbitrary wave-forms and pulsed-field gradients. J. Magn. Reson. A 112, 275–279 (1995). https://doi.org/10.1006/jmra.1995.1047

    Article  CAS  Google Scholar 

  57. Wiseman, T., Williston, S., Brandts, J.F., Lin, L.N.: Rapid measurement of binding constants and heats of binding using a new titration calorimeter. Anal. Biochem. 179, 131–137 (1989). https://doi.org/10.1016/0003-2697(89)90213-3

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

M.K.G. has an equity interest in, and is a cofounder and scientific advisor of, VeraChem LLC. NMR spectra were collected at the UCSD Skaggs School of Pharmacy and Pharmaceutical Sciences NMR Facility.

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  1. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0736, USA

    K. Kellett, S. A. Kantonen, B. M. Duggan & M. K. Gilson

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Kellett, K., Kantonen, S.A., Duggan, B.M. et al. Toward Expanded Diversity of Host–Guest Interactions via Synthesis and Characterization of Cyclodextrin Derivatives. J Solution Chem 47, 1597–1608 (2018). https://doi.org/10.1007/s10953-018-0769-1

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