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Dynamics and structural determinants of ligand recognition of the 5-HT6 receptor

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Abstract

In order to identify molecular models of the human 5-HT6 receptor suitable for virtual screening, homology modeling and membrane-embedded molecular dynamics simulations were performed. Structural requirements for robust enrichment were assessed by an unbiased chemometric analysis of enrichments from retrospective virtual screening studies. The two main structural features affecting enrichment are the outward movement of the second extracellular loop and the formation of a hydrophobic cavity deep in the binding site. These features appear transiently in the trajectories and furthermore the stretches of uniformly high enrichment may only last 4–10 ps. The formation of the inner hydrophobic cavity was also linked to the active-like to inactive-like transition of the receptor, especially the so-called connector region. The best structural models provided significant and robust enrichment over three independent ligand sets.

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Abbreviations

5-HT:

5-Hydroxytriptamine

AD:

Alzheimer’s disease

BEDROC:

Boltzmann-Enhanced Discrimination Receiver Operator Characteristic area under the curve

ECL:

Extracellular loop

GDD:

GPCR Decoy Database

GPCR:

G protein-coupled receptor

ICL:

Intracellular loop

IFD:

Induced fit docking

POPC:

1-Palmitoyl-2-oleoylphosphatidylcholine

RMSD:

Root mean square deviation (of atomic positions)

References

  1. Mortier J, Rakers C, Bermudez M, Murgueitio MS, Riniker S, Wolber G (2015) The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug Discov Today 20:686–702

    Article  CAS  Google Scholar 

  2. Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) How fast-folding proteins fold. Science 334:517–520

    Article  CAS  Google Scholar 

  3. Dror RO, Pan AC, Arlow DH, Borhani DW, Maragakis P, Shan Y, Xu H, Shaw DE (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci USA 108:13118–13123

    Article  CAS  Google Scholar 

  4. Rodríguez D, Brea J, Loza MI, Carlsson J (2014) Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands. Structure 22:1140–1151

    Article  Google Scholar 

  5. Dror RO, Arlow DH, Maragakis P, Mildorf TJ, Pan AC, Xu H, Borhani DW, Shaw DE (2011) Activation mechanism of the β2-adrenergic receptor. Proc Natl Acad Sci USA 108:18684–18689

    Article  CAS  Google Scholar 

  6. Kohlhoff KJ, Shukla D, Lawrenz M, Bowman GR, Konerding DE, Belov D, Altman RB, Pande VS (2014) Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 6:15–21

    Article  CAS  Google Scholar 

  7. Martí-Solano M, Sanz F, Pastor M, Selent J (2014) A dynamic view of molecular switch behavior at serotonin receptors: implications for functional selectivity. PLoS One 9:e109312

    Article  Google Scholar 

  8. Jensen MØ, Jogini V, Borhani DW, Leffler AE, Dror RO, Shaw DE (2012) Mechanism of voltage gating in potassium channels. Science 336:229–233

    Article  CAS  Google Scholar 

  9. Tarcsay A, Paragi G, Vass M, Jójárt B, Bogár F, Keserű GM (2013) The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J Chem Inf Model 53:2990–2999

    Article  CAS  Google Scholar 

  10. Boukharta L, Gutiérrez-de-Terán H, Aqvist J (2014) Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. PLoS Comput Biol 10:e1003585

    Article  Google Scholar 

  11. Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137:2695–2703

    Article  CAS  Google Scholar 

  12. Katritch V, Rueda M, Abagyan R (2012) Ligand-guided receptor optimization. Methods Mol Biol 857:189–205

    Article  CAS  Google Scholar 

  13. Kohen R, Metcalf MA, Khan N, Druck T, Huebner K, Lachowicz JE, Meltzer HY, Sibley DR, Roth BL, Hamblin MW (1996) Cloning, characterization, and chromosomal localization of a human 5-HT6 serotonin receptor. J Neurochem 66:47–56

    Article  CAS  Google Scholar 

  14. Quiedeville A, Boulouard M, Da Silva Costa-Aze V, Dauphin F, Bouet V, Freret T (2014) 5-HT6 receptor antagonists as treatment for age-related cognitive decline. Rev Neurosci 25:417–427

    CAS  Google Scholar 

  15. Marazziti D, Baroni S, Borsini F, Picchetti M, Vatteroni E, Falaschi V, Catena-Dell’Osso M (2013) Serotonin receptors of type 6 (5-HT6): from neuroscience to clinical pharmacology. Curr Med Chem 20:371–377

    CAS  Google Scholar 

  16. Benhamú B, Martín-Fontecha M, Vázquez-Villa H, Pardo L, López-Rodríguez ML (2014) Serotonin 5-HT6 receptor antagonists for the treatment of cognitive deficiency in Alzheimer’s disease. J Med Chem 57:7160–7181

    Article  Google Scholar 

  17. Wesołowska A (2010) Potential role of the 5-HT6 receptor in depression and anxiety: an overview of preclinical data. Pharmacol Rep 62:564–577

    Article  Google Scholar 

  18. Heal DJ, Smith SL, Fisas A, Codony X, Buschmann H (2008) Selective 5-HT6 receptor ligands: progress in the development of a novel pharmacological approach to the treatment of obesity and related metabolic disorders. Pharmacol Ther 117:207–231

    Article  CAS  Google Scholar 

  19. Roth BL, Craigo SC, Choudhary MS, Uluer A, Monsma FJ Jr, Shen Y, Meltzer HY, Sibley DR (1994) Binding of typical and atypical antipsychotic agents to 5-hydroxytryptamine-6 and 5-hydroxytryptamine-7 receptors. J Pharmacol Exp Ther 268:1403–1410

    CAS  Google Scholar 

  20. Sleight AJ, Boess FG, Bos M, Levet-Trafit B, Riemer C, Bourson A (1998) Characterization of Ro 04-6790 and Ro 63-0563: potent and selective antagonists at human and rat 5-HT6 receptors. Br J Pharmacol 124:556–562

    Article  CAS  Google Scholar 

  21. Bromidge SM, Brown AM, Clarke SE, Dodgson K, Gager T, Grassam HL, Jeffrey PM, Joiner GF, King FD, Middlemiss DN, Moss SF, Newman H, Riley G, Routledge C, Wyman P (1999) 5-Chloro-N-(4-methoxy-3-piperazin-1-yl-phenyl)-3-methyl-2-benzothiophenesulfonamide (SB-271046): a potent, selective, and orally bioavailable 5-HT6 receptor antagonist. J Med Chem 42:202–205

    Article  CAS  Google Scholar 

  22. Ivanenkov YA, Majouga AG, Veselov MS, Chufarova NV, Baranovsky SS, Filkov GI (2014) Computational approaches to the design of novel 5-HT6R ligands. Rev Neurosci 25:451–467

    CAS  Google Scholar 

  23. Wilkinson D, Windfeld K, Colding-Jørgensen E (2014) Safety and efficacy of idalopirdine, a 5-HT6 receptor antagonist, in patients with moderate Alzheimer’s disease (LADDER): a randomised, double-blind, placebo-controlled phase 2 trial. Lancet Neurol 13:1092–1099

    Article  CAS  Google Scholar 

  24. de la Fuente T, Martín-Fontecha M, Sallander J, Benhamú B, Campillo M, Medina RA, Pellissier LP, Claeysen S, Dumuis A, Pardo L, López-Rodríguez ML (2010) Benzimidazole derivatives as new serotonin 5-HT6 receptor antagonists. Molecular mechanisms of receptor inactivation. J Med Chem 53:1357–1369

    Article  Google Scholar 

  25. Kołaczkowski M, Marcinkowska M, Bucki A, Śniecikowska J, Pawłowski M et al (2015) Novel 5-HT6 receptor antagonists/D2 receptor partial agonists targeting behavioral and psychological symptoms of dementia. Eur J Med Chem 92:221–235

    Article  Google Scholar 

  26. Wang C, Jiang Y, Ma J, Wu H, Wacker D (2013) Structural basis for molecular recognition at serotonin receptors. Science 340:610–614

    Article  CAS  Google Scholar 

  27. Wacker D, Wang C, Katritch V, Han GW, Huang XP, Vardy E, McCorvy JD, Jiang Y, Chu M, Siu FY, Liu W, Xu HE, Cherezov V, Roth BL, Stevens RC (2013) Structural features for functional selectivity at serotonin receptors. Science 340:615–619

    Article  CAS  Google Scholar 

  28. Smusz S, Mordalski S, Witek J, Rataj K, Kafel R, Bojarski AJ (2015) Multi-step protocol for automatic evaluation of docking results based on machine learning methods—a case study of serotonin receptors 5-HT6 and 5-HT7. J Chem Inf Model 55:823–832

    Article  CAS  Google Scholar 

  29. Prime, version 3.0, Schrödinger, LLC: New York (2011)

  30. Schrödinger Suite 2011 Induced Fit Docking protocol, Glide version 5.7, Schrödinger, LLC: New York, 2011, Prime version 3.0, Schrödinger, LLC: New York (2011)

  31. Wang J, Wolf RM, Caldwell JW, Kollamn PA, Case DA (2004) Development and testing of a general Amber force field. J Comput Chem 25:1157–1174

    Article  CAS  Google Scholar 

  32. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935

    Article  CAS  Google Scholar 

  33. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA et al (2009) Gaussian 09, revision A1. Gaussian Inc., Wallingford

    Google Scholar 

  34. Case DA, Cheatham TE, Darden T III, GohlkeH LuoR, Merz KM, Onufriev A Jr, Simmerling C, Wang B, Woods R (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    Article  CAS  Google Scholar 

  35. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802

    Article  CAS  Google Scholar 

  36. McGibbon RT, Beauchamp KA, Schwantes CR, Wang LP, Hernández CX, Harrigan MP, Lane TJ, Swails JM, Pande VS (2014) MDTraj: a modern, open library for the analysis of molecular dynamics trajectories. bioRxiv. doi:10.1101/008896

    Google Scholar 

  37. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38

    Article  CAS  Google Scholar 

  38. Schrödinger Suite 2014-1 Protein Preparation Wizard, Epik version 2.7, Schrödinger, LLC, New York, NY, 2013, Impact version 6.2, Schrödinger, LLC, New York, NY, 2014, Prime version 3.5, Schrödinger, LLC, New York, NY (2014)

  39. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aid Mol Des 27:221–234

    Article  Google Scholar 

  40. Gatica EA, Cavasotto CN (2012) Ligand and decoy sets for docking to G protein-coupled receptors. J Chem Inf Model 52:1–6

    Article  CAS  Google Scholar 

  41. https://www.ebi.ac.uk/chembl/assay/inspect/CHEMBL1909108

  42. Calculator, version 5.10.2, 2012, ChemAxon (http://www.chemaxon.com)

  43. LigPrep, version 2.9, Schrödinger, LLC, New York, NY (2014)

  44. Epik, version 2.7, Schrödinger, LLC, New York, NY (2014)

  45. Greenwood JR, Calkins D, Sullivan AP, Shelley JC (2010) Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J Comput Aided Mol Des 24:591–604

    Article  CAS  Google Scholar 

  46. Shelley JC, Cholleti A, Frye L, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21:681–691

  47. Glide, version 6.2, Schrödinger, LLC, New York, NY (2014)

  48. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shaw DE, Shelley M, Perry JK, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  Google Scholar 

  49. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759

    Article  CAS  Google Scholar 

  50. Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47:488–508

    Article  CAS  Google Scholar 

  51. Zhao W, Hevener KE, White SW, Lee RE, Boyett JM (2009) A statistical framework to evaluate virtual screening. BMC Bioinform 10:225

    Article  Google Scholar 

  52. Tehan BG, Bortolato A, Blaney FE, Weir MP, Mason JS (2014) Unifying family A GPCR theories of activation. Pharmacol Ther 143:51–60 (and references therein)

    Article  CAS  Google Scholar 

  53. Feher M, Williams CI (2012) Numerical errors and chaotic behavior in docking simulations. J Chem Inf Model 52:724–738

    Article  CAS  Google Scholar 

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Acknowledgments

This work was supported by the National Brain Research Program KTIA-NAP-13-1-2013-0001. V.M., Gy.M.K. and Á.T. participate in the European Cooperation in Science and Technology (COST) Action CM1207: GPCR-Ligand Interactions, Structures, and Transmembrane Signalling: a European Research Network (GLISTEN). G.P. would like to thank for the financial support of the Marie Curie Intra European Fellowship within the 7th European Community Framework Programme.

Author information

Author notes

  1. Márton Vass

    Present address: Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV, Amsterdam, The Netherlands

  2. Ákos Tarcsay

    Present address: ChemAxon Ltd., Záhony Str. 7, Budapest, 1031, Hungary

Authors and Affiliations

  1. Discovery Chemistry, Gedeon Richter Plc., Gyömrői út 19-21, Budapest, 1103, Hungary

    Márton Vass & Ákos Tarcsay

  2. Department of Chemical Informatics, Faculty of Education, University of Szeged, Boldogasszony sgt. 6, Szeged, 6725, Hungary

    Balázs Jójárt

  3. MTA SZTE Supramolecular and Nanostructured Materials Research Group, Hungarian Academy of Sciences, University of Szeged, Dóm tér 8, Szeged, 6720, Hungary

    Ferenc Bogár & Gábor Paragi

  4. Department of Theoretical Chemistry, VU University Amsterdam, De Boelelaan 1083, 1081 HV, Amsterdam, The Netherlands

    Gábor Paragi

  5. Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, Budapest, 1117, Hungary

    György M. Keserű

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  1. Márton Vass
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Correspondence to Ákos Tarcsay.

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Vass, M., Jójárt, B., Bogár, F. et al. Dynamics and structural determinants of ligand recognition of the 5-HT6 receptor. J Comput Aided Mol Des 29, 1137–1149 (2015). https://doi.org/10.1007/s10822-015-9883-y

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