Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes

English version

Autoři: Rajat Kumar Pal ^aff001; Satishkumar Gadhiya ^aff003; Steven Ramsey ^aff002; Pierpaolo Cordone ^aff002; Lauren Wickstrom ^aff006; Wayne W. Harding ^aff002; Tom Kurtzman ^aff002; Emilio Gallicchio ^aff001
Působiště autorů: Department of Chemistry, Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY 11210, United States of America ^aff001; PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America ^aff002; PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America ^aff003; Department of Chemistry, Hunter College, 695 Park Avenue, NY 10065, United States of America ^aff004; Department of Chemistry, Lehman College, 250 Bedford Park Blvd. West, Bronx, NY 10468, United States of America ^aff005; Department of Science, Borough of Manhattan Community College, 199 Chambers Street, New York, NY 10007, United States of America ^aff006
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222902

Souhrn

Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.

Klíčová slova:

Binding analysis – Crystal structure – Dopamine – Free energy – Thermodynamics – Solvation – Receptor physiology – Receptor binding assays

Zdroje

1. de Beer S, Vermeulen N, Oostenbrink C. The Role of Water Molecules in Computational Drug Design. Curr Top Med Chem. 2010;10(1):55–66. doi: 10.2174/156802610790232288 19929830

2. Li Z, Lazaridis T. Water at biomolecular binding interfaces. Phys Chem Chem Phys. 2007;9(5):573–581. doi: 10.1039/b612449f 17242738

3. Mancera RL. Molecular modeling of hydration in drug design. Curr Opin Drug Discov Devel. 2007;10(3):275–280. 17554853

4. Ball P. Water as an Active Constituent in Cell Biology Water as an Active Constituent in Cell Biology. Chem Rev. 2008;108(1):74–108. doi: 10.1021/cr068037a

5. Ladbury JE. Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem Biol. 1996;3(12):973–980. doi: 10.1016/S1074-5521(96)90164-7 9000013

6. Nguyen CN, Cruz A, Gilson MK, Kurtzman T. Thermodynamics of water in an enzyme active site: Grid-based hydration analysis of coagulation factor xa. J Chem Theory Comput. 2014;10(7):2769–2780. doi: 10.1021/ct401110x 25018673

7. Setny P, Baron R, McCammon JA. How can hydrophobic association be enthalpy driven? J Chem Theory Comput. 2010;6(9):2866–2871. doi: 10.1021/ct1003077 20844599

8. Haider K, Wickstrom L, Ramsey S, Gilson MK, Kurtzman T. Enthalpic Breakdown of Water Structure on Protein Active-Site Surfaces. J Phys Chem B. 2016;120(34):8743–8756. doi: 10.1021/acs.jpcb.6b01094 27169482

9. Pal RK, Haider K, Kaur D, Flynn W, Xia J, Levy RM, et al. A combined treatment of hydration and dynamical effects for the modeling of host-guest binding thermodynamics: the SAMPL5 blinded challenge. J Comput Aided Mol Des. 2017;31(1):29–44. doi: 10.1007/s10822-016-9956-6 27696239

10. Beuming T, Farid R, Sherman W. High-energy water sites determine peptide binding affinity and specificity of PDZ domains. Protein Sci. 2009;18(8):1609–1619. doi: 10.1002/pro.177 19569188

11. Young T, Abel R, Kim B, Berne BJ, Friesner RA. Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding. Proc Natl Acad Sci. 2007;104(3):808–813. doi: 10.1073/pnas.0610202104 17204562

12. Huggins DJ. Application of inhomogeneous fluid solvation theory to model the distribution and thermodynamics of water molecules around biomolecules. Phys Chem Chem Phys. 2012;14(43):15106–15117. doi: 10.1039/c2cp42631e 23037989

13. Ross GA, Morris GM, Biggin PC. Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites. PLoS One. 2012;7(3):e32036. doi: 10.1371/journal.pone.0032036 22396746

14. Ross GA, Bodnarchuk MS, Essex JW. Water Sites, Networks, And Free Energies with Grand Canonical Monte Carlo. J Am Chem Soc. 2015;137(47):14930–14943. doi: 10.1021/jacs.5b07940 26509924

15. Bodnarchuk MS, Viner R, Michel J, Essex JW. Strategies to Calculate Water Binding Free Energies in Protein—Ligand Complexes. J Chem Inf Model. 2014;54(6):1623–1633. doi: 10.1021/ci400674k 24684745

16. Sindhikara DJ, Hirata F. Analysis of biomolecular solvation sites by 3D-RISM theory. J Phys Chem B. 2013;117(22):6718–6723. doi: 10.1021/jp4046116 23675899

17. Graham SE, Smith RD, Carlson HA. Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics. J Chem Inf Model. 2018;58(2):305–314. doi: 10.1021/acs.jcim.7b00268 29286658

18. Michel J, Tirado-Rives J, Jorgensen WL. Prediction of the water content in protein binding sites. J Phys Chem B. 2009;113(40):13337–13346. doi: 10.1021/jp9047456 19754086

19. Bruce Macdonald HE, Cave-Ayland C, Ross GA, Essex JW. Ligand Binding Free Energies with Adaptive Water Networks: Two-Dimensional Grand Canonical Alchemical Perturbations. J Chem Theory Comput. 2018;14(12):6586–6597. doi: 10.1021/acs.jctc.8b00614 30451501

20. Gallicchio E, Paris K, Levy RM. The AGBNP2 implicit solvation model. J Chem Theory Comput. 2009;5(9):2544–2564. doi: 10.1021/ct900234u 20419084

21. Heidbreder CA, Newman AH. Current perspectives on selective dopamine D3 receptor antagonists as pharmacotherapeutics for addictions and related disorders. Ann N Y Acad Sci. 2010;1187(1):4–34. doi: 10.1111/j.1749-6632.2009.05149.x 20201845

22. Gadhiya S, Cordone P, Pal RK, Gallicchio E, Wickstrom L, Kurtzman T, et al. New Dopamine D3-Selective Receptor Ligands Containing a 6-Methoxy-1,2,3,4-tetrahydroisoquinolin-7-ol Motif. ACS Med Chem Lett. 2018;9(10):990–995. doi: 10.1021/acsmedchemlett.8b00229 30344905

23. Madapa S, Gadhiya S, Kurtzman T, Alberts IL, Ramsey S, Reith M, et al. Synthesis and evaluation of C9 alkoxy analogues of (-)-stepholidine as dopamine receptor ligands. Eur J Med Chem. 2017;125:255–268. doi: 10.1016/j.ejmech.2016.09.036 27688181

24. Yuan S, Filipek S, Palczewski K, Vogel H. Activation of G-protein-coupled receptors correlates with the formation of a continuous internal water pathway. Nat Commun. 2014;5(1):4733. doi: 10.1038/ncomms5733 25203160

25. Maramai S, Gemma S, Brogi S, Campiani G, Butini S, Stark H, et al. Dopamine D3 Receptor Antagonists as Potential Therapeutics for the Treatment of Neurological Diseases. Front Neurosci. 2016;10(OCT):451. doi: 10.3389/fnins.2016.00451 27761108

26. Volkow ND, Fowler JS, Wang GJ, Swanson JM, Telang F. Dopamine in drug abuse and addiction: Results of imaging studies and treatment implications. Arch Neurol. 2007;64(11):1575–1579. doi: 10.1001/archneur.64.11.1575 17998440

27. Brooks DJ. Dopamine agonists: Their role in the treatment of Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2000;68(6):685–689. doi: 10.1136/jnnp.68.6.685 10811688

28. Chien EYT, Liu W, Zhao Q, Katritch V, Won Han G, Hanson MA, et al. Structure of the Human Dopamine D3 Receptor in Complex with a D2/D3 Selective Antagonist. Science. 2010;330(6007):1091–1095. doi: 10.1126/science.1197410 21097933

29. Cho DI, Zheng M, Kim KM. Current perspectives on the selective regulation of dopamine D2 and D3 receptors. Arch Pharm Res. 2010;33(10):1521–1538. doi: 10.1007/s12272-010-1005-8 21052931

30. Li B, Li W, Du P, Yu KQ, Fu W. Molecular insights into the D1R agonist and D2R/D3R antagonist effects of the natural product (-)-stepholidine: Molecular modeling and dynamics Simulations. J Phys Chem B. 2012;116(28):8121–8130. doi: 10.1021/jp3049235 22702398

31. Mo J, Guo Y, Yang YS, Shen JS, Jin GZ, Zhen X. Recent Developments in Studies of l-Stepholidine and its Analogs: Chemistry, Pharmacology and Clinical Implications. Curr Med Chem. 2007;14(28):2996–3002. doi: 10.2174/092986707782794050 18220736

32. Knable MB, Heinz A, Raedler T, Weinberger DR. Extrapyramidal side effects with risperidone and haloperidol at comparable D2 receptor occupancy levels. Psychiatry Res—Neuroimaging. 1997;75(2):91–101. doi: 10.1016/S0925-4927(97)00023-1

33. Sykes DA, Moore H, Stott L, Holliday N, Javitch JA, Lane JR, et al. Extrapyramidal side effects of antipsychotics are linked to their association kinetics at dopamine D2 receptors. Nat Commun. 2017;8(1):763. doi: 10.1038/s41467-017-00716-z 28970469

34. Song R, Bi GH, Zhang HY, Yang RF, Gardner EL, Li J, et al. Blockade of D3 receptors by YQA14 inhibits cocaine’s rewarding effects and relapse to drug-seeking behavior in rats. Neuropharmacology. 2014;77:398–405. doi: 10.1016/j.neuropharm.2013.10.010 24176392

35. Keck TM, John WS, Czoty PW, Nader MA, Newman AH. Identifying Medication Targets for Psychostimulant Addiction: Unraveling the Dopamine D3 Receptor Hypothesis. J Med Chem. 2015;58(14):5361–5380. doi: 10.1021/jm501512b 25826710

36. Ferruz N, Doerr S, Vanase-Frawley MA, Zou Y, Chen X, Marr ES, et al. Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs. Sci Rep. 2018;8(1):1–10. doi: 10.1038/s41598-018-19345-7

37. Newman AH, Beuming T, Banala AK, Donthamsetti P, Pongetti K, Labounty A, et al. Molecular determinants of selectivity and efficacy at the dopamine D3 receptor. J Med Chem. 2012;55(15):6689–6699. doi: 10.1021/jm300482h 22632094

38. Meade JA, Free RB, Miller NR, Chun LS, Doyle TB, Moritz AE, et al. (-)-Stepholidine is a potent pan-dopamine receptor antagonist of both G protein- and β-arrestin-mediated signaling. Psychopharmacology (Berl). 2015;232(5):917–930. doi: 10.1007/s00213-014-3726-8

39. Zhang B, Guo F, Ma Y, Song Y, Lin R, Shen FY, et al. Activation of D1R/PKA/mTOR signaling cascade in medial prefrontal cortex underlying the antidepressant effects of l-SPD. Sci Rep. 2017;7(1):3809. doi: 10.1038/s41598-017-03680-2 28630404

40. Manuszak M, Harding W, Gadhiya S, Ranaldi R. (-)-Stepholidine reduces cue-induced reinstatement of cocaine seeking and cocaine self-administration in rats. Drug Alcohol Depend. 2018;189:49–54. doi: 10.1016/j.drugalcdep.2018.04.030 29879681

41. Fu W, Shen J, Luo X, Zhu W, Cheng J, Yu K, et al. Dopamine D1 receptor agonist and D2 receptor antagonist effects of the natural product (2)-stepholidine: Molecular modeling and dynamics simulations. Biophys J. 2007;93(5):1431–1441. doi: 10.1529/biophysj.106.088500 17468175

42. Lazaridis T. Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory. J Phys Chem B. 1998;102(18):3531–3541. doi: 10.1021/jp9723574

43. Izadi S, Anandakrishnan R, Onufriev AV. Building water models: A different approach. J Phys Chem Lett. 2014;5(21):3863–3871. doi: 10.1021/jz501780a 25400877

44. Nguyen CN, Kurtzman Young T, Gilson MK. Grid inhomogeneous solvation theory: Hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. J Chem Phys. 2012;137(4):973–980. doi: 10.1063/1.4733951

45. Oroguchi T, Nakasako M. Changes in hydration structure are necessary for collective motions of a multi-domain protein. Sci Rep. 2016;6(1):26302. doi: 10.1038/srep26302 27193111

46. Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL. Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B. 2001;105(28):6474–6487. doi: 10.1021/jp003919d

47. Jacobson MP, Kaminski GA, Friesner RA, Rapp CS. Force field validation using protein side chain prediction. J Phys Chem B. 2002;106(44):11673–11680. doi: 10.1021/jp021564n

48. Qiu D, Shenkin PS, Hollinger FP, Still WC. The GB/SA continuum model for solvation. A fast analytical method for the calculation of approximate Born radii. J Phys Chem A. 1997;101(16):3005–3014. doi: 10.1021/jp961992r

49. Hawkins GD, Cramer CJ, Truhlar DG. Parametrized models of aqueous free energies of solvation based on pairwise descreening of solute atomic charges from a dielectric medium. J Phys Chem. 1996;100(51):19824–19839. doi: 10.1021/jp961710n

50. Gallicchio E, Lapelosa M, Levy RM. Binding Energy Distribution Analysis Method (BEDAM) for Estimation of Protein-Ligand Binding Affinities. J Chem Theory Comput. 2010;6:2961–2977.

51. Gallicchio E, Levy RM. Recent theoretical and computational advances for modeling protein-ligand binding affinities. Adv Protein Chem Struct Biol. 2011;85(1):27–80. doi: 10.1016/B978-0-12-386485-7.00002-8 21920321

52. Tan Z, Gallicchio E, Lapelosa M, Levy RM. Theory of binless multi-state free energy estimation with applications to protein-ligand binding. J Chem Phys. 2012;136(14):144102. doi: 10.1063/1.3701175 22502496

53. Gallicchio E, Levy RM, Parashar M. Asynchronous replica exchange for molecular simulations. J Comput Chem. 2008;29(5):788–794. doi: 10.1002/jcc.20839 17876761

54. Gallicchio E, Xia J, Flynn WF, Zhang B, Samlalsingh S, Mentes A, et al. Asynchronous replica exchange software for grid and heterogeneous computing. Comput Phys Commun. 2015;196:236–246. doi: 10.1016/j.cpc.2015.06.010 27103749

55. Sherman W, Day T, Jacobson MP, Friesner RA, Farid R. Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem. 2006;49(2):534–553. doi: 10.1021/jm050540c 16420040

56. Jacobson MP, Pincus DL, Rapp CS, Day TJF, Honig B, Shaw DE, et al. A hierarchical approach to all-atom loop prediction. Proteins. 2004;55:351–367. doi: 10.1002/prot.10613 15048827

57. Jacobson MP, Friesner RA, Xiang Z, Honig B. On the role of the crystal environment in determining protein side-chain conformations. J Mol Biol. 2002;320(3):597–608. doi: 10.1016/s0022-2836(02)00470-9 12096912

58. Salomon-Ferrer R, Case DA, Walker RC. An overview of the Amber biomolecular simulation package. Wiley Interdiscip Rev Comput Mol Sci. 2013;3(2):198–210. doi: 10.1002/wcms.1121

59. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J Chem Theory Comput. 2015;11(8):3696–3713. doi: 10.1021/acs.jctc.5b00255 26574453

60. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and assessmnet of Docking Accuracy. J Med Chem. 2004;47(7):1739–1749. doi: 10.1021/jm0306430 15027865

61. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M. Epik: A software program for pKa prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des. 2007;21(12):681–691. doi: 10.1007/s10822-007-9133-z 17899391

62. Gallicchio E, Levy RM. Prediction of SAMPL3 host-guest affinities with the binding energy distribution analysis method (BEDAM). J Comput Aided Mol Des. 2012;26(5):505–516. doi: 10.1007/s10822-012-9552-3 22354755

63. Gallicchio E, Deng N, He P, Wickstrom L, Perryman AL, Santiago DN, et al. Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge. J Comput Aided Mol Des. 2014;28:475–490. doi: 10.1007/s10822-014-9711-9 24504704

64. Provasi D, Artacho MC, Negri A, Mobarec JC, Filizola M. Ligand-Induced modulation of the Free-Energy landscape of G protein-coupled receptors explored by adaptive biasing techniques. PLoS Comput Biol. 2011;7(10):e1002193. doi: 10.1371/journal.pcbi.1002193 22022248

65. Michino M, Boateng CA, Donthamsetti P, Yano H, Bakare OM, Bonifazi A, et al. Toward understanding the structural basis of partial agonism at the dopamine D3 receptor. J Med Chem. 2017;60(2):580–593. doi: 10.1021/acs.jmedchem.6b01148 27983845

66. Deng Y, Roux B. Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem B. 2009;113(8):2234–2246. doi: 10.1021/jp807701h 19146384

67. Bruce Macdonald HE, Cave-Ayland C, Ross GA, Essex JW. Ligand Binding Free Energies with Adaptive Water Networks: Two-Dimensional Grand Canonical Alchemical Perturbations. J Chem Theory Comput. 2018;14(12):6586–6597. doi: 10.1021/acs.jctc.8b00614 30451501

68. Clark M, Meshkat S, Wiseman JS. Grand Canonical Free-Energy Calculations of Protein-Ligand Binding. J Chem Inf Model. 2009;49(4):934–943. doi: 10.1021/ci8004397 19309088

Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes

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