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Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations

  • Kristen A. Marino
  • Marta Filizola
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1705)

Abstract

An increasing number of G protein-coupled receptor (GPCR) crystal structures provide important—albeit static—pictures of how small molecules or peptides interact with their receptors. These high-resolution structures represent a tremendous opportunity to apply molecular dynamics (MD) simulations to capture atomic-level dynamical information that is not easy to obtain experimentally. Understanding ligand binding and unbinding processes, as well as the related responses of the receptor, is crucial to the design of better drugs targeting GPCRs. Here, we discuss possible ways to study the dynamics involved in the binding of small molecules to GPCRs, using long timescale MD simulations or metadynamics-based approaches.

Key words

Molecular dynamics Ligand binding Small-molecule drugs GPCRs Enhanced-sampling methods Interaction fingerprints Allosteric communication 

Notes

Acknowledgments

This work was supported by National Institutes of Health grants MH107053, DA026434, and DA034049. Computations discussed here were run on resources available through (a) the Scientific Computing Facility at the Icahn School of Medicine at Mount Sinai, (b) the Extreme Science and Engineering Discovery Environment (XSEDE) under MCB080077, which is supported by National Science Foundation grant number ACI-1053575, and (c) the Pittsburgh Supercomputing Center which provided Anton computer time (under PSCA14006) through grant R01GM116961 from the National Institutes of Health. The Anton machine at PSC was generously made available by D.E. Shaw Research

References

  1. 1.
    Kruse AC, Ring AM, Manglik A, Hu J, Hu K, Eitel K, Hubner H, Pardon E, Valant C, Sexton PM, Christopoulos A, Felder CC, Gmeiner P, Steyaert J, Weis WI, Garcia KC, Wess J, Kobilka BK (2013) Activation and allosteric modulation of a muscarinic acetylcholine receptor. Nature 504(7478):101–106. https://doi.org/10.1038/nature12735 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Oswald C, Rappas M, Kean J, Doré AS, Errey JC, Bennett K, Deflorian F, Christopher JA, Jazayeri A, Mason JS, Congreve M, Cooke RM, Marshall FH (2016) Intracellular allosteric antagonism of the CCR9 receptor. Nature 540(7633):462–465. https://doi.org/10.1038/nature20606 CrossRefPubMedGoogle Scholar
  3. 3.
    Zheng Y, Qin L, Zacarías NVO, de Vries H, Han GW, Gustavsson M, Dabros M, Zhao C, Cherney RJ, Carter P, Stamos D, Abagyan R, Cherezov V, Stevens RC, Ijzerman AP, Heitman LH, Tebben A, Kufareva I, Handel TM (2016) Structure of CC chemokine receptor 2 with orthosteric and allosteric antagonists. Nature 540(7633):458–461. https://doi.org/10.1038/nature20605 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Wootten D, Christopoulos A, Sexton PM (2013) Emerging paradigms in GPCR allostery: implications for drug discovery. Nat Rev Drug Discov 12(8):630–644. https://doi.org/10.1038/nrd4052 CrossRefPubMedGoogle Scholar
  5. 5.
    DeWire SM, Yamashita DS, Rominger DH, Liu G, Cowan CL, Graczyk TM, Chen X-T, Pitis PM, Gotchev D, Yuan C, Koblish M, Lark MW, Violin JD (2013) A G protein-biased ligand at the μ-opioid receptor is potently analgesic with reduced gastrointestinal and respiratory dysfuncation compared with morphine. J Pharmacol Exp Ther 344:708–717. https://doi.org/10.1124/jpet.112.201616 CrossRefPubMedGoogle Scholar
  6. 6.
    Crowley RS, Riley AP, Sherwood AM, Groer CE, Shivaperumal N, Biscaia M, Paton K, Schneider S, Provasi D, Kivell BM, Filizola M, Prisinzano TE (2016) Synthetic studies of neoclerodane diterpenes from salvia divinorum: identification of a potent and centrally acting μ opioid analgesic with reduced abuse liability. J Med Chem 59(24):11027–11038. https://doi.org/10.1021/acs.jmedchem.6b01235 CrossRefPubMedGoogle Scholar
  7. 7.
    Schneider S, Provasi D, Filizola M (2016) How oliceridine (TRV-130) binds and stabilizes a mu-opioid receptor conformational state that selectively triggers G protein signaling pathways. Biochemistry 55(46):6456–6466. https://doi.org/10.1021/acs.biochem.6b00948 CrossRefPubMedGoogle Scholar
  8. 8.
    Shang Y, Yeatman HR, Provasi D, Alt A, Christopoulos A, Canals M, Filizola M (2016) Proposed mode of binding and action of positive allosteric modulators at opioid receptors. ACS Chem Biol 11(5):1220–1229. https://doi.org/10.1021/acschembio.5b00712 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Dror RO, Green HF, Valant C, Borhani DW, Valcourt JR, Pan AC, Arlow DH, Canals M, Lane JR, Rahmani R, Baell JB, Sexton PM, Christopoulos A, Shaw DE (2013) Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs. Nature 503(7475):295–299. https://doi.org/10.1038/nature12595 PubMedGoogle Scholar
  10. 10.
    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 U S A 108(32):13118–13123CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Kruse AC, Hu J, Pan AC, Arlow DH, Rosenbaum DM, Rosemond E, Green HF, Liu T, Chae PS, Dror RO, Shaw DE, Weis WI, Wess J, Kobilka BK (2012) Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature 482:552–556. https://doi.org/10.1038/nature10867 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Stanley N, Pardo L, Fabritiis GD (2016) The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor. Sci Rep 6:22639. https://doi.org/10.1038/srep22639 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci U S A 99(20):12562–12566. https://doi.org/10.1073/pnas.202427399 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Provasi D, Bortolato A, Filizola M (2009) Exploring molecular mechanisms of ligand recognition by opioid receptors with metadynamics. Biochemistry 48(42):10020–10029. https://doi.org/10.1021/bi901494n CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J Med Chem 49(21):6177–6196. https://doi.org/10.1021/jm051256o CrossRefPubMedGoogle Scholar
  16. 16.
    Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120(24):11919–11929CrossRefPubMedGoogle Scholar
  17. 17.
    Kappel K, Miao Y, McCammon JA (2015) Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor. Q Rev Biophys 48(4):479–487. https://doi.org/10.1017/S0033583515000153 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Bhattacharya S, Vaidehi N (2014) Differences in allosteric communication pipelines in the inactive and active states of a GPCR. Biophys J 107(2):422–434. https://doi.org/10.1016/j.bpj.2014.06.015 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Miao Y, Nichols SE, Gasper PM, Metzger VT, McCammon JA (2013) Activation and dynamic network of the M2 muscarinic receptor. Proc Natl Acad Sci U S A 110(27):10982–10987CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Fiser A, Do RKG, Sali A (2000) Modeling of loops in protein structures. Protein Sci 9:1753–1773CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Rohl CA, Strauss CEM, Chivian D, Baker D (2004) Modeling structurally variable regions in homologous proteins with rosetta. Proteins 55(3):656–677. https://doi.org/10.1002/prot.10629 CrossRefPubMedGoogle Scholar
  22. 22.
    Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29(11):1859–1865. https://doi.org/10.1002/jcc.20945 CrossRefPubMedGoogle Scholar
  23. 23.
    Schmidt TH, Kandt C (2012) LAMBADA and InflateGRO2: efficient membrane alignment and insertion of membrane proteins for molecular dynamics simulations. J Chem Inf Model 52(10):2657–2669. https://doi.org/10.1021/ci3000453 CrossRefPubMedGoogle Scholar
  24. 24.
    Vanommeslaeghe K, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52(12):3144–3154. https://doi.org/10.1021/ci300363c CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52(12):3155–3168. https://doi.org/10.1021/ci3003649 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31(4):671–690. https://doi.org/10.1002/jcc.21367 PubMedPubMedCentralGoogle Scholar
  27. 27.
    Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001 CrossRefGoogle Scholar
  28. 28.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kalé L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802. https://doi.org/10.1002/jcc.20289 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Tribello GA, Bonomi M, Branduardi D, Camilloni C, Bussi G (2014) PLUMED 2: new feathers for an old bird. Comput Phys Commun 185(2):604–613. https://doi.org/10.1016/j.cpc.2013.09.018 CrossRefGoogle Scholar
  30. 30.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38. https://doi.org/10.1016/0263-7855(96)00018-5 CrossRefPubMedGoogle Scholar
  31. 31.
    Delano WL (2002) The PyMOL molecular graphics system. doi:citeulike-article-id:2816763Google Scholar
  32. 32.
    Scherer MK, Trendelkamp-Schroer B, Paul F, Pérez-Hernández G, Hoffmann M, Plattner N, Wehmeyer C, Prinz J-H, Noé F (2015) PyEMMA 2: a software package for estimation, validation, and analysis of Markov models. J Chem Theory Comput 11(11):5525–5542. https://doi.org/10.1021/acs.jctc.5b00743 CrossRefPubMedGoogle Scholar
  33. 33.
    Fenalti G, Giguere PM, Katritch V, Huang XP, Thompson AA, Cherezov V, Roth BL, Stevens RC (2014) Molecular control of delta-opioid receptor signalling. Nature 506(7487):191–196. https://doi.org/10.1038/nature12944 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Manglik A, Kruse AC, Kobilka TS, Thian FS, Mathiesen JM, Sunahara RK, Pardo L, Weis WI, Kobilka BK, Granier S (2012) Crystal structure of the micro-opioid receptor bound to a morphinan antagonist. Nature 485(7398):321–326. https://doi.org/10.1038/nature10954 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Huang W, Manglik A, Venkatakrishnan AJ, Laeremans T, Feinberg EN, Sanborn AL, Kato HE, Livingston KE, Thorsen TS, Kling RC, Granier S, Gmeiner P, Husbands SM, Traynor JR, Weis WI, Steyaert J, Dror RO, Kobilka BK (2015) Structural insights into micro-opioid receptor activation. Nature 524(7565):315–321. https://doi.org/10.1038/nature14886 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Wu EL, Cheng X, Jo S, Rui H, Song KC, Davila-Contreras EM, Qi Y, Lee J, Monje-Galvan V, Venable RM, Klauda JB, Im W (2014) CHARMM-GUI membrane builder toward realistic biological membrane simulations. J Comput Chem 35(27):1997–2004. https://doi.org/10.1002/jcc.23702 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, Wei S, Buckner J, Jeong JC, Qi Y, Jo S, Pande VS, Case DA, Brooks CL 3rd, AD MK Jr, Klauda JB, Im W (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 12(1):405–413. https://doi.org/10.1021/acs.jctc.5b00935 CrossRefPubMedGoogle Scholar
  38. 38.
    Shaw DE, Deneroff MM, Dror RO, Kuskin JS, Larson RH, Salmon JK, Young C, Batson B, Bowers KJ, Chao JC, Eastwood MP, Gagliardo J, Grossman JP, Ho CR, Ierardi DJ, In K, Jl K, Layman T, Mcleavey C, Moraes MA, Mueller R, Priest EC, Shan Y, Spengler J, Theobald M, Towles B, Wang SC (2008) Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM 51(7):91–97. https://doi.org/10.1145/1364782.1364802 CrossRefGoogle Scholar
  39. 39.
    Raiteri P, Laio A, Gervasio FL, Micheletti C, Parrinello M (2006) Efficient reconstruction of complex free energy landscapes by multiple walkers metadynamics. J Phys Chem B 110(8):3533–3539. https://doi.org/10.1021/jp054359r CrossRefPubMedGoogle Scholar
  40. 40.
    Tiwary P, Parrinello M (2015) A time-independent free energy estimator for metadynamics. J Phys Chem B 119(3):736–742. https://doi.org/10.1021/jp504920s CrossRefPubMedGoogle Scholar
  41. 41.
    Sander J, Ester M, Kriegel H-P, Xu X (1998) Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Disc 2(2):169–194. https://doi.org/10.1023/A:1009745219419 CrossRefGoogle Scholar
  42. 42.
    Beauchamp KA, Bowman GR, Lane TJ, Maibaum L, Haque IS, Pande VS (2011) MSMBuilder2: modeling conformational dynamics on the picosecond to millisecond scale. J Chem Theory Comput 7(10):3412–3419. https://doi.org/10.1021/ct200463m CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Collier G, Ortiz V (2013) Emerging computational approaches for the study of protein allostery. Arch Biochem Biophys 538(1):6–15. https://doi.org/10.1016/j.abb.2013.07.025 CrossRefPubMedGoogle Scholar
  44. 44.
    Feher VA, Durrant JD, Van Wart AT, Amaro RE (2014) Computational approaches to mapping allosteric pathways. Curr Opin Struct Biol 25:98–103. https://doi.org/10.1016/j.sbi.2014.02.004 CrossRefPubMedGoogle Scholar
  45. 45.
    Stolzenberg S, Michino M, LeVine MV, Weinstein H, Shi L (2016) Computational approaches to detect allosteric pathways in transmembrane molecular machines. Biochim Biophys Acta 1858(7, Part B):1652–1662. https://doi.org/10.1016/j.bbamem.2016.01.010 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Fanelli F, Felline A (2011) Dimerization and ligand binding affect the structure network of A2A adenosine receptor. Biochim Biophys Acta Biomembr 1808(5):1256–1266. https://doi.org/10.1016/j.bbamem.2010.08.006 CrossRefGoogle Scholar
  47. 47.
    Michino M, Free RB, Doyle TB, Sibley DR, Shi L (2015) Structural basis for Na+−sensitivity in dopamine D2 and D3 receptors. Chem Commun 51(41):8618–8621. https://doi.org/10.1039/C5CC02204E CrossRefGoogle Scholar
  48. 48.
    Angelova K, Felline A, Lee M, Patel M, Puett D, Fanelli F (2011) Conserved amino acids participate in the structure networks deputed to intramolecular communication in the lutropin receptor. Cell Mol Life Sci 68(7):1227–1239. https://doi.org/10.1007/s00018-010-0519-z CrossRefPubMedGoogle Scholar
  49. 49.
    Kong Y, Karplus M (2007) The signaling pathway of rhodopsin. Structure 15(5):611–623. https://doi.org/10.1016/j.str.2007.04.002 CrossRefPubMedGoogle Scholar
  50. 50.
    Isin B, Schulten K, Tajkhorshid E, Bahar I (2008) Mechanism of signal propagation upon retinal isomerization: insights from molecular dynamics simulations of rhodopsin restrained by normal modes. Biophys J 95(2):789–803. https://doi.org/10.1529/biophysj.107.120691 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    LeVine MV, Perez-Aguilar JM 2014, Weinstein H N-body information theory (NbIT) analysis of rigid-body dynamics in intracellular loop 2 of the 5-HT2A receptor. In: Ortuño F, Rojas I (eds) International Work-Conference on Bioinformatics and Biomedical Engineering, GranadaGoogle Scholar
  52. 52.
    Perez-Aguilar JM, Shan J, LeVine MV, Khelashvili G, Weinstein H (2014) A functional selectivity mechanism at the serotonin-2A GPCR involves ligand-dependent conformations of intracellular loop 2. J Am Chem Soc 136(45):16044–16054. https://doi.org/10.1021/ja508394x CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    LeVine MV, Weinstein H (2014) NbIT - a new information theory-based analysis of allosteric mechanisms reveals residues that underlie function in the leucine transporter LeuT. PLoS Comput Biol 10(5):e1003603. https://doi.org/10.1371/journal.pcbi.1003603 CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer Science+Business Media LLC 2018

Authors and Affiliations

  1. 1.Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkUSA

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