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Simulation of Ligand Binding to Membrane Proteins

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Membrane Protein Structure and Function Characterization

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1635))

Abstract

Membrane proteins are involved in a large variety of functions. Most of these protein functions are regulated by ligand binding with diverse modes of action: agonists, partial agonists, antagonists, and allosteric modulators, potentiators and inhibitors. From the pharmacological point of view, membrane proteins are one if not the major target for drug development. However, experimental structure determination of membrane proteins in complex or in free form still represents a great challenge. Molecular dynamics (MD) simulations commonly reach the microsecond scale on membrane systems. This numerical tool is mature enough to predict and add molecular details on the different ligand-binding modes. In the present chapter, I will present the different steps to design, simulate, and analyze a MD simulation system containing a protein embedded in a membrane and surrounded by water and ligand. As an illustration, the simulation of the ligand-gated ion channel γ-aminobutyric acid type A receptor (GABAAR) surrounded by one of its allosteric potentiators, bromoform, will be presented and discussed.

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References

  1. Yıldırım MA, Goh K-I, Cusick ME et al (2007) Drug—target network. Nat Biotechnol 25:1119–1126. doi:10.1038/nbt1338

    Article  PubMed  Google Scholar 

  2. McCammon JA, Gelin BR, Karplus M (1977) Dynamics of folded proteins. Nature 267:585–590

    Article  CAS  PubMed  Google Scholar 

  3. Shaw DE, Dror RO, Salmon JK et al (2009) Millisecond-scale molecular dynamics simulations on Anton. In: Proceedings international conference high performance computing, network storage and analysis. ACM, New York, NY, pp 39:1–39:11

    Google Scholar 

  4. Stansfeld PJ, Sansom MSP (2011) Molecular simulation approaches to membrane proteins. Structure 19:1562–1572. doi:10.1016/j.str.2011.10.002

    Article  CAS  PubMed  Google Scholar 

  5. Bennett WFD, Tieleman DP (2013) Computer simulations of lipid membrane domains. Biochim Biophys Acta 1828:1765–1776. doi:10.1016/j.bbamem.2013.03.004

    Article  CAS  PubMed  Google Scholar 

  6. Bill RM, Henderson PJF, Iwata S et al (2011) Overcoming barriers to membrane protein structure determination. Nat Biotechnol 29:335–340. doi:10.1038/nbt.1833

    Article  CAS  PubMed  Google Scholar 

  7. Vinothkumar KR (2015) Membrane protein structures without crystals, by single particle electron cryomicroscopy. Curr Opin Struct Biol 33:103–114. doi:10.1016/j.sbi.2015.07.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Montaville P, Jamin N (2010) Determination of membrane protein structures using solution and solid-state NMR. Methods Mol Biol (Clifton, NJ) 654:261–282. doi:10.1007/978-1-60761-762-4_14

    Article  CAS  Google Scholar 

  9. Dror RO, Dirks RM, Grossman JP et al (2012) Biomolecular simulation: a computational microscope for molecular biology. Annu Rev Biophys 41:429–452. doi:10.1146/annurev-biophys-042910-155245

    Article  CAS  PubMed  Google Scholar 

  10. Dror RO, Pan AC, Arlow DH et al (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci 108:13118–13123. doi:10.1073/pnas.1104614108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Miller PS, Aricescu AR (2014) Crystal structure of a human GABAA receptor. Nature 512:270–275. doi:10.1038/nature13293

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Murail S, Howard RJ, Broemstrup T et al (2012) Molecular mechanism for the dual alcohol modulation of cys-loop receptors. PLoS Comput Biol 8:e1002710. doi:10.1371/journal.pcbi.1002710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Murail S, Wallner B, Trudell JR et al (2011) Microsecond simulations indicate that ethanol binds between subunits and could stabilize an open-state model of a glycine receptor. Biophys J 100:1642–1650. doi:10.1016/j.bpj.2011.02.032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Brannigan G, LeBard DN, Henin J et al (2010) Multiple binding sites for the general anesthetic isoflurane identified in the nicotinic acetylcholine receptor transmembrane domain. Proc Natl Acad Sci U S A 107:14122–14127. doi:10.1073/pnas.1008534107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lebard DN, Henin J, Eckenhoff RG et al (2012) General anesthetics predicted to block the GLIC pore with micromolar affinity. PLoS Comput Biol 8:e1002532. doi:10.1371/journal.pcbi.1002532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Laurent B, Murail S, Shahsavar A et al (2016) Sites of anesthetic inhibitory action on a cationic ligand-gated ion channel. Structure 24:595–605. doi:10.1016/j.str.2016.02.014

    Article  CAS  PubMed  Google Scholar 

  17. Sauguet L, Howard RJ, Malherbe L et al (2013) Structural basis for potentiation by alcohols and anaesthetics in a ligand-gated ion channel. Nat Commun 4:1697. doi:10.1038/ncomms2682

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nury H, Van Renterghem C, Weng Y et al (2011) X-ray structures of general anaesthetics bound to a pentameric ligand-gated ion channel. Nature 469:428–431. doi:10.1038/nature09647

    Article  CAS  PubMed  Google Scholar 

  19. Hibbs RE, Gouaux E (2011) Principles of activation and permeation in an anion-selective Cys-loop receptor. Nature 474:54–60. doi:10.1038/nature10139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sauguet L, Fourati Z, Prangé T et al (2016) Structural basis for xenon inhibition in a cationic pentameric ligand-gated ion channel. PLoS One 11:e0149795. doi:10.1371/journal.pone.0149795

    Article  PubMed  PubMed Central  Google Scholar 

  21. Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. doi:10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  22. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(33–8):27–28

    Google Scholar 

  23. Lindorff-Larsen K, Maragakis P, Piana S et al (2012) Systematic validation of protein force fields against experimental data. PLoS One 7:e32131. doi:10.1371/journal.pone.0032131

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Guvench O, MacKerell AD (2008) Comparison of protein force fields for molecular dynamics simulations. Methods Mol Biol (Clifton, NJ) 443:63–88. doi:10.1007/978-1-59745-177-2_4

    Article  CAS  Google Scholar 

  25. Beauchamp KA, Lin Y-S, Das R, Pande VS (2012) Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements. J Chem Theory Comput 8:1409–1414. doi:10.1021/ct2007814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ollila OHS, Pabst G (2016) Atomistic resolution structure and dynamics of lipid bilayers in simulations and experiments. Biochim Biophys Acta 1858:2512–2528. doi:10.1016/jbbamem2016

    Article  CAS  PubMed  Google Scholar 

  27. Best RB, Zhu X, Shim J et al (2012) Optimization of the additive charmm all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. J Chem Theory Comput 8:3257–3273. doi:10.1021/ct300400x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Klauda JB, Venable RM, Freites JA et al (2010) Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J Phys Chem B 114:7830–7843. doi:10.1021/jp101759q

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Domański J, Stansfeld PJ, Sansom MSP, Beckstein O (2010) Lipidbook: a public repository for force-field parameters used in membrane simulations. J Membr Biol 236:255–258. doi:10.1007/s00232-010-9296-8

    Article  PubMed  Google Scholar 

  30. Lomize MA, Lomize AL, Pogozheva ID, Mosberg HI (2006) OPM: orientations of proteins in membranes database. Bioinformatics 22:623–625. doi:10.1093/bioinformatics/btk023

    Article  CAS  PubMed  Google Scholar 

  31. Laurent B, Murail S, Da Silva F et al (2012) Modeling complex biological systems: from solution chemistry to membranes and channels. Pure Appl Chem 85:1–13. doi:10.1351/PAC-CON-12-04-10

    Article  Google Scholar 

  32. Olsson MHM, Søndergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7:525–537. doi:10.1021/ct100578z

    Article  CAS  PubMed  Google Scholar 

  33. Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29:1859–1865. doi:10.1002/jcc.20945

    Article  CAS  PubMed  Google Scholar 

  34. Hénin J, Salari R, Murlidaran S, Brannigan G (2014) A predicted binding site for cholesterol on the GABAA receptor. Biophys J 106:1938–1949. doi:10.1016/j.bpj.2014.03.024

    Article  PubMed  PubMed Central  Google Scholar 

  35. North P, Fleischer S (1983) Alteration of synaptic membrane cholesterol/phospholipid ratio using a lipid transfer protein. Effect on gamma-aminobutyric acid uptake. J Biol Chem 258:1242–1253

    CAS  PubMed  Google Scholar 

  36. Lee J, Cheng X, Swails JM et al (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 12:405–413. doi:10.1021/acs.jctc.5b00935

    Article  CAS  PubMed  Google Scholar 

  37. Domanski J, Beckstein O, Iorga BI (2017) Ligandbook - an online repository for small and drug-like molecule force field parameters. Bioinformatics btx037. doi: 10.1093/bioinformatics/btx037

    Google Scholar 

  38. Zoete V, Cuendet MA, Grosdidier A, Michielin O (2011) SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 32:2359–2368. doi:10.1002/jcc.21816

    Article  CAS  PubMed  Google Scholar 

  39. Vanommeslaeghe K, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52:3144–3154. doi:10.1021/ci300363c

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Laurent B (2014.) Étude de l’anesthésie générale à l’échelle atomique par modélisation d’un homologue bactérien du récepteur nicotinique humain. Phdthesis, Université Paris-Diderot, Paris VII

    Google Scholar 

  41. Laurent B, Chavent M, Cragnolini T et al (2015) Epock: rapid analysis of protein pocket dynamics. Bioinformatics 31:1478–1480. doi:10.1093/bioinformatics/btu822

    Article  CAS  PubMed  Google Scholar 

  42. Mihic SJ, Ye Q, Wick MJ et al (1997) Sites of alcohol and volatile anaesthetic action on GABA(A) and glycine receptors. Nature 389:385–389. doi:10.1038/38738

    Article  CAS  PubMed  Google Scholar 

  43. Huang D, Caflisch A (2011) Small molecule binding to proteins: affinity and binding/unbinding dynamics from atomistic simulations. ChemMedChem 6:1578–1580. doi:10.1002/cmdc.201100237

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The author would like to thank Jerome Hénin and Marc Baaden for their help in the critical reading of this chapter. This work was supported by CNRS (Centre National de la Recherche Scientifique) and Air Liquide.

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Authors and Affiliations

  1. Laboratoire de Biochimie Théorique, CNRS, UPR9080, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie, F-75005, Paris, France

    Samuel Murail

  2. Air Liquide, Centre de Recherches Paris-Saclay, Boite Postale 126, Les Loges-en-Josas, Jouy-en-Josas, 78354, France

    Samuel Murail

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  1. Samuel Murail
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Correspondence to Samuel Murail .

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Editors and Affiliations

  1. Département de Chimie, CNRS UMR 7203 LBM, Sorbonne Universités—UPMC Univ Paris 06, École Normale Supérieure—PSL Research University, Paris, France

    Jean-Jacques Lacapere

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Murail, S. (2017). Simulation of Ligand Binding to Membrane Proteins. In: Lacapere, JJ. (eds) Membrane Protein Structure and Function Characterization. Methods in Molecular Biology, vol 1635. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7151-0_20

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  • DOI: https://doi.org/10.1007/978-1-4939-7151-0_20

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7149-7

  • Online ISBN: 978-1-4939-7151-0

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