Advertisement

TRP Channels pp 187-206 | Cite as

In Silico Approaches for TRP Channel Modulation

  • Magdalena Nikolaeva Koleva
  • Gregorio Fernandez-BallesterEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1987)

Abstract

The implication of several TRP ion channels (e.g., TRPV1) in diverse physiological and pathological processes has signaled them as pivotal drug targets. Consequently, the identification of selective and potent ligands for these channels is of great interest in pharmacology and biomedicine. However, a major challenge in the design of modulators is ensuring the specificity for their intended targets. In recent years, the emergence of high-resolution structures of ion channels facilitates the computer-assisted drug design at molecular levels. Here we describe some computational methods and general protocols to discover channel modulators, including homology modelling, docking and virtual screening, and structure-based peptide design.

Key words

TRP channel modulation Pharmacology In silico drug discovery Homology models Virtual screening Computational approaches Peptide design Structure-based design 

Notes

Acknowledgments

This work was supported by the Agencia Estatal de Investigación (AEI, MINECO) (SAF2016-66275-C02-01) and Generalitat Valenciana PROMETEO/2014/011. MNK is a recipient of an Industrial Doctorate Fellowship from MINECO (DI-16-08303).

References

  1. 1.
    Vennekens R, Owsianik G, Nilius B (2008) Vanilloid transient receptor potential cation channels: an overview. Curr Pharm Des 14(1):18–31PubMedCrossRefGoogle Scholar
  2. 2.
    Cortright DN, Szallasi A (2009) TRP channels and pain. Curr Pharm Des 15(15):1736–1749PubMedCrossRefGoogle Scholar
  3. 3.
    Bacigalupo J, Delgado R, Muñoz Y et al (2015) TRP channels in visual transduction. In: Madrid R, Bacigalupo J (eds) TRP channels in sensory transduction. Springer International Publishing, ChamGoogle Scholar
  4. 4.
    Montell C, Caterina MJ (2007) Thermoregulation: channels that are cool to the core. Curr Biol 17(20):R885–R887PubMedCrossRefGoogle Scholar
  5. 5.
    Venkatachalam K, Montell C (2007) TRP channels. Annu Rev Biochem 76:387–417PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Latorre R, Brauchi S, Orta G et al (2007) ThermoTRP channels as modular proteins with allosteric gating. Cell Calcium 42(4–5):427–438PubMedCrossRefGoogle Scholar
  7. 7.
    Caterina MJ, Leffler A, Malmberg AB et al (2000) Impaired nociception and pain sensation in mice lacking the capsaicin receptor. Science 288(5464):306–313PubMedCrossRefGoogle Scholar
  8. 8.
    García-Martinez C, Humet M, Planells-Cases R et al (2002) Attenuation of thermal nociception and hyperalgesia by VR1 blockers. Proc Natl Acad Sci U S A 99(4):2374–2379PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Fernández-Ballester G, Fernández-Carvajal A, González-Ros JM et al (2011) Ionic channels as targets for drug design: a review on computational methods. Pharmaceutics 3(4):932–953PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11(13–14):580–594PubMedCrossRefGoogle Scholar
  11. 11.
    Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact 171(2):165–176PubMedCrossRefGoogle Scholar
  12. 12.
    Grisshammer R, Tate CG (1995) Overexpression of integral membrane proteins for structural studies. Q Rev Biophys 28(3):315–422PubMedCrossRefGoogle Scholar
  13. 13.
    Liao M, Cao E, Julius D et al (2013) Structure of the TRPV1 ion channel determined by electron cryo-microscopy. Nature 504(7478):107–112PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Henderson R (2013) Structural biology: ion channel seen by electron microscopy. Nature 504(7478):93–94PubMedCrossRefGoogle Scholar
  15. 15.
    Clapham DE (2015) Structural biology: pain-sensing TRPA1 channel resolved. Nature 520(7548):439–441PubMedCrossRefGoogle Scholar
  16. 16.
    Huynh KW, Cohen MR, Jiang J et al (2016) Structure of the full-length TRPV2 channel by cryo-EM. Nat Commun 7:11130PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Saotome K, Singh AK, Yelshanskaya MV et al (2016) Crystal structure of the epithelial calcium channel TRPV6. Nature 534(7608):506–511PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Gabanyi MJ, Berman HM (2012) Structural databases of biological macromolecules. Wiley, ChichesterCrossRefGoogle Scholar
  19. 19.
    Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Laskowski RA, Hutchinson EG, Michie AD et al (1997) PDBsum: a web-based database of summaries and analyses of all PDB structures. Trends Biochem Sci 22(12):488–490PubMedCrossRefGoogle Scholar
  21. 21.
    de Beer TA, Berka K, Thornton JM et al (2014) PDBsum additions. Nucleic Acids Res 42:D292–D296PubMedCrossRefGoogle Scholar
  22. 22.
    De Las Rivas J, Fontanillo C (2010) Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6(6):e1000807PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Orchard S, Ammari M, Aranda B et al (2014) The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358–D363PubMedCrossRefGoogle Scholar
  24. 24.
    Mosca R, Céol A, Stein A et al (2014) 3DID: a catalog of domain-based interactions of known three-dimensional structure. Nucleic Acids Res 42:D374–D379PubMedCrossRefGoogle Scholar
  25. 25.
    Chatr-Aryamontri A, Oughtred R, Boucher L et al (2017) The BioGRID interaction database: 2017 update. Nucleic Acids Res 45(D1):D369–D379PubMedCrossRefGoogle Scholar
  26. 26.
    Li W, Cowley A, Uludag M et al (2015) The EMBL-EBI bioinformatics web and programmatic tools framework. Nucleic Acids Res 43(W1):W580–W584PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    McWilliam H, Li W, Uludag M et al (2013) Analysis Tool Web Services from the EMBL-EBI. Nucleic Acids Res 41(Web Server issue):W597–W600PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Altschul SF, Madden TL, Schäffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Polyanovsky VO, Roytberg MA, Tumanyan VG (2011) Comparative analysis of the quality of a global algorithm and a local algorithm for alignment of two sequences. Algorithms Mol Biol 6(1):25PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Brudno M, Malde S, Poliakov A et al (2003) Global alignment: finding rearrangements during alignment. Bioinformatics 19:54–62CrossRefGoogle Scholar
  31. 31.
    Schrödinger L (2015) The PyMOL Molecular Graphics System, Version 1.8 Schrödinger, LLCGoogle Scholar
  32. 32.
    Krieger E, Vriend G (2014) YASARA View - molecular graphics for all devices - from smartphones to workstations. Bioinformatics 30(20):2981–2982PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Krieger E, Vriend G (2015) New ways to boost molecular dynamics simulations. J Comput Chem 36(13):996–1007PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Biasini M, Bienert S, Waterhouse A et al (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42(Web Server issue):W252–W258PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Guex N, Peitsch MC, Schwede T (2009) Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis 30(Suppl 1):S162–S173PubMedCrossRefGoogle Scholar
  36. 36.
    Webb B, Sali A (2014) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 47:1–32CrossRefGoogle Scholar
  37. 37.
    Martí-Renom MA, Stuart AC, Fiser A et al (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325PubMedCrossRefGoogle Scholar
  38. 38.
    Skolnick J, Brylinski M (2009) FINDSITE: a combined evolution/structure-based approach to protein function prediction. Brief Bioinform 10(4):378–391PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Mukherjee S, Zhang Y (2011) Protein-protein complex structure predictions by multimeric threading and template recombination. Structure 19(7):955–966PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Schmidtke P, Le Guilloux V, Maupetit J et al (2010) FPocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res 38:W582–W589PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Weisel M, Proschak E, Schneider G (2007) PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem Cent J 1(7):1–17Google Scholar
  42. 42.
    Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748PubMedCrossRefGoogle Scholar
  44. 44.
    London N, Raveh B, Cohen E et al (2011) Rosetta FlexPepDock web server—high resolution modeling of peptide-protein interactions. Nucleic Acids Res 39(Web Server issue):W249–W253PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Guerois R, Nielsen JE, Serrano L (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320(2):369–387PubMedCrossRefGoogle Scholar
  46. 46.
    Schymkowitz J, Borg J, Stricher F et al (2005) The FoldX web server: an online force field. Nucleic Acids Res 33:W382–W388PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16(1):11–26PubMedCrossRefGoogle Scholar
  48. 48.
    Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295(2):337–356PubMedCrossRefGoogle Scholar
  49. 49.
    Lamichhane M, Gezelter JD, Newman KE (2014) Real space electrostatics for multipoles. I. Development of methods. J Chem Phys 141(13):134109PubMedCrossRefGoogle Scholar
  50. 50.
    Lamichhane M, Newman KE, Gezelter JD (2014) Real space electrostatics for multipoles. II. Comparisons with the Ewald sum. J Chem Phys 141(13):134110PubMedCrossRefGoogle Scholar
  51. 51.
    Lindahl E, Hess B, Dvd S (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. Mol Model Ann 7(8):306–317CrossRefGoogle Scholar
  52. 52.
    Jiang W, Phillips JC, Huang L et al (2014) generalized scalable multiple copy algorithms for molecular dynamics simulations in NAMD. Comput Phys Commun 185(3):908–916PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Canutescu AA, Shelenkov AA, Dunbrack RL (2003) A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci 12(9):2001–2014PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Morris GM, Goodsell DS, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662CrossRefGoogle Scholar
  55. 55.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461PubMedPubMedCentralGoogle Scholar
  56. 56.
    Rubinstein M, Niv MY (2009) Peptidic modulators of protein-protein interactions: progress and challenges in computational design. Biopolymers 91(7):505–513PubMedCrossRefGoogle Scholar
  57. 57.
    Fernandez-Ballester G, Serrano L (2006) Prediction of protein–protein interaction based on structure. In: Guerois R, de la Paz ML (eds) Protein design: methods and applications. Humana Press, Totowa, NJGoogle Scholar
  58. 58.
    Fernandez-Ballester G, Beltrao P, Gonzalez JM et al (2009) Structure-based prediction of the Saccharomyces cerevisiae SH3-ligand interactions. J Mol Biol 388(4):902–916PubMedCrossRefGoogle Scholar
  59. 59.
    Encinar JA, Fernandez-Ballester G, Sánchez IE et al (2009) ADAN: a database for prediction of protein-protein interaction of modular domains mediated by linear motifs. Bioinformatics 25(18):2418–2424PubMedCrossRefGoogle Scholar
  60. 60.
    García-Sanz N, Fernández-Carvajal A, Morenilla-Palao C et al (2004) Identification of a tetramerization domain in the C terminus of the vanilloid receptor. J Neurosci 24(23):5307–5314PubMedCrossRefPubMedCentralGoogle Scholar
  61. 61.
    García-Sanz N, Valente P, Gomis A et al (2007) A role of the transient receptor potential domain of vanilloid receptor I in channel gating. J Neurosci 27(43):11641–11650PubMedCrossRefPubMedCentralGoogle Scholar
  62. 62.
    Valente P, García-Sanz N, Gomis A et al (2008) Identification of molecular determinants of channel gating in the transient receptor potential box of vanilloid receptor I. FASEB J 22(9):3298–3309PubMedCrossRefGoogle Scholar
  63. 63.
    Peczuh MW, Hamilton AD (2000) Peptide and protein recognition by designed molecules. Chem Rev 100(7):2479–2494PubMedCrossRefGoogle Scholar
  64. 64.
    London N, Movshovitz-Attias D, Schueler-Furman O (2010) The structural basis of peptide-protein binding strategies. Structure 18(2):188–199PubMedCrossRefGoogle Scholar
  65. 65.
    Ye G, Tiwari R, Parang K (2008) Development of Src tyrosine kinase substrate binding site inhibitors. Curr Opin Investig Drugs 9(6):605–613PubMedGoogle Scholar
  66. 66.
    Valente P, Fernández-Carvajal A, Camprubí-Robles M et al (2011) Membrane-tethered peptides patterned after the TRP domain (TRPducins) selectively inhibit TRPV1 channel activity. FASEB J 25(5):1628–1640PubMedCrossRefGoogle Scholar
  67. 67.
    Watt PM (2006) Screening for peptide drugs from the natural repertoire of biodiverse protein folds. Nat Biotechnol 24(2):177–183PubMedCrossRefGoogle Scholar
  68. 68.
    Vanhee P, van der Sloot AM, Verschueren E et al (2011) Computational design of peptide ligands. Trends Biotechnol 29(5):231–239PubMedCrossRefGoogle Scholar
  69. 69.
    Fernández-Ballester G, Fernández-Carvajal A, Devesa I et al (2011) In silico-based direct evolution of peptides and peptidomimetics in drug discovery. Curr Top Pharmacol 15:35–55Google Scholar
  70. 70.
    Reina J, Lacroix E, Hobson SD et al (2002) Computer-aided design of a PDZ domain to recognize new target sequences. Nat Struct Biol 9(8):621–627PubMedGoogle Scholar
  71. 71.
    Benyamini H, Friedler A (2010) Using peptides to study protein-protein interactions. Future Med Chem 2(6):989–1003PubMedCrossRefGoogle Scholar
  72. 72.
    Torbeev VY, Kent SB (2007) Convergent chemical synthesis and crystal structure of a 203 amino acid “covalent dimer” HIV-1 protease enzyme molecule. Angew Chem Int Ed Engl 46(10):1667–1670PubMedCrossRefGoogle Scholar
  73. 73.
    Vlieghe P, Lisowski V, Martinez J et al (2010) Synthetic therapeutic peptides: science and market. Drug Discov Today 15(1–2):40–56PubMedCrossRefGoogle Scholar
  74. 74.
    Grauer A, Konig B (2009) Peptidomimetics—A versatile route to biologically active compounds. J Org Chem 2009(30):5099–5113Google Scholar
  75. 75.
    Vagner J, Qu H, Hruby VJ (2008) Peptidomimetics, a synthetic tool of drug discovery. Curr Opin Chem Biol 12(3):292–296PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Magdalena Nikolaeva Koleva
    • 1
    • 2
  • Gregorio Fernandez-Ballester
    • 1
    Email author
  1. 1.Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de ElcheUniversitas Miguel HernándezElcheSpain
  2. 2.AntalGenics SL. Ed. Quorum III, University Scientific ParkUniversitas Miguel HernándezElcheSpain

Personalised recommendations