Skip to main content
SpringerLink
Log in

Docking and Virtual Screening Strategies for GPCR Drug Discovery

  • Protocol
Book cover G Protein-Coupled Receptors in Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1335))

Abstract

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rosenbaum DM, Cherezov V, Hanson MA, Rasmussen SG, Thian FS, Kobilka TS, Choi HJ, Yao XJ, Weis WI, Stevens RC, Kobilka BK (2007) GPCR engineering yields high-resolution structural insights into beta2-adrenergic receptor function. Science 318(5854):1266–1273. doi:10.1126/science.1150609

    CAS  PubMed  Google Scholar 

  2. Roth CB, Hanson MA, Stevens RC (2008) Stabilization of the human beta2-adrenergic receptor TM4-TM3-TM5 helix interface by mutagenesis of Glu122(3.41), a critical residue in GPCR structure. J Mol Biol 376(5):1305–1319. doi:10.1016/j.jmb.2007.12.028

    CAS  PubMed Central  PubMed  Google Scholar 

  3. Shibata Y, White JF, Serrano-Vega MJ, Magnani F, Aloia AL, Grisshammer R, Tate CG (2009) Thermostabilization of the neurotensin receptor NTS1. J Mol Biol 390(2):262–277. doi:10.1016/j.jmb.2009.04.068

    CAS  PubMed Central  PubMed  Google Scholar 

  4. Serrano-Vega MJ, Magnani F, Shibata Y, Tate CG (2008) Conformational thermostabilization of the beta1-adrenergic receptor in a detergent-resistant form. Proc Natl Acad Sci U S A 105(3):877–882. doi:10.1073/pnas.0711253105

    CAS  PubMed Central  PubMed  Google Scholar 

  5. Magnani F, Shibata Y, Serrano-Vega MJ, Tate CG (2008) Co-evolving stability and conformational homogeneity of the human adenosine A2a receptor. Proc Natl Acad Sci U S A 105(31):10744–10749. doi:10.1073/pnas.0804396105

    CAS  PubMed Central  PubMed  Google Scholar 

  6. Yin X, Xu H, Hanson M, Liu W (2014) GPCR crystallization using lipidic cubic phase technique. Curr Pharm Biotechnol 15(10):971–979

    CAS  PubMed  Google Scholar 

  7. Liu W, Wacker D, Gati C, Han GW, James D, Wang D, Nelson G, Weierstall U, Katritch V, Barty A, Zatsepin NA, Li D, Messerschmidt M, Boutet S, Williams GJ, Koglin JE, Seibert MM, Wang C, Shah ST, Basu S, Fromme R, Kupitz C, Rendek KN, Grotjohann I, Fromme P, Kirian RA, Beyerlein KR, White TA, Chapman HN, Caffrey M, Spence JC, Stevens RC, Cherezov V (2013) Serial femtosecond crystallography of G protein-coupled receptors. Science 342(6165):1521–1524. doi:10.1126/science.1244142

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Katritch V, Cherezov V, Stevens RC (2012) Diversity and modularity of G protein-coupled receptor structures. Trends Pharmacol Sci 33(1):17–27. doi:10.1016/j.tips.2011.09.003

    CAS  PubMed Central  PubMed  Google Scholar 

  9. Guimaraes CR, Cardozo M (2008) MM-GB/SA rescoring of docking poses in structure-based lead optimization. J Chem Inf Model 48(5):958–970. doi:10.1021/ci800004w

    CAS  PubMed  Google Scholar 

  10. Durdagi S, Zhao C, Cuervo JE, Noskov SY (2011) Atomistic models for free energy evaluation of drug binding to membrane proteins. Curr Med Chem 18(17):2601–2611

    CAS  PubMed  Google Scholar 

  11. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11(5):425–445

    CAS  PubMed  Google Scholar 

  12. 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):455–461. doi:10.1002/jcc.21334

    CAS  PubMed Central  PubMed  Google Scholar 

  13. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295(2):337–356. doi:10.1006/jmbi.1999.3371

    CAS  PubMed  Google Scholar 

  14. Muegge I, Martin YC (1999) A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 42(5):791–804. doi:10.1021/jm980536j

    CAS  PubMed  Google Scholar 

  15. Neudert G, Klebe G (2011) DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes. J Chem Inf Model 51(10):2731–2745. doi:10.1021/ci200274q

    CAS  PubMed  Google Scholar 

  16. Gschwend DA, Good AC, Kuntz ID (1996) Molecular docking towards drug discovery. J Mol Recognit 9(2):175–186. doi:10.1002/(SICI)1099-1352(199603)9:2<175::AID-JMR260>3.0.CO;2-D

    CAS  PubMed  Google Scholar 

  17. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, 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(7):1739–1749

    CAS  PubMed  Google Scholar 

  18. 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(7):1750–1759

    CAS  PubMed  Google Scholar 

  19. 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

    CAS  PubMed  Google Scholar 

  20. Perola E, Walters WP, Charifson PS (2004) A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins 56(2):235–249. doi:10.1002/prot.20088

    CAS  PubMed  Google Scholar 

  21. Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner RA (2012) Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. J Comput Aided Mol Des 26(6):787–799. doi:10.1007/s10822-012-9575-9

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  23. Prime 3.7 (2014) Schrödinger, Inc., Portland, OR

    Google Scholar 

  24. Farid R, Day T, Friesner RA, Pearlstein RA (2006) New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg Med Chem 14(9):3160–3173. doi:10.1016/j.bmc.2005.12.032

    CAS  PubMed  Google Scholar 

  25. Sherman W, Beard HS, Farid R (2006) Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Des 67(1):83–84. doi:10.1111/j.1747-0285.2005.00327.x

    CAS  PubMed  Google Scholar 

  26. LigPrep v3.1 (2014) Schrödinger, Inc., Portland, OR

    Google Scholar 

  27. 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(6–7):591–604. doi:10.1007/s10822-010-9349-1

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  29. 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 Aided Mol Des 27(3):221–234. doi:10.1007/s10822-013-9644-8

    PubMed  Google Scholar 

  30. Maestro 9.9 (2014) Schrödinger, Inc., Portland, OR

    Google Scholar 

  31. Bas DC, Rogers DM, Jensen JH (2008) Very fast prediction and rationalization of pKa values for protein-ligand complexes. Proteins 73(3):765–783. doi:10.1002/prot.22102

    CAS  PubMed  Google Scholar 

  32. Yusuf D, Davis AM, Kleywegt GJ, Schmitt S (2008) An alternative method for the evaluation of docking performance: RSR vs RMSD. J Chem Inf Model 48(7):1411–1422. doi:10.1021/ci800084x

    CAS  PubMed  Google Scholar 

  33. Baber JC, Thompson DC, Cross JB, Humblet C (2009) GARD: a generally applicable replacement for RMSD. J Chem Inf Model 49(8):1889–1900. doi:10.1021/ci9001074

    CAS  PubMed  Google Scholar 

  34. Schulz-Gasch T, Scharfer C, Guba W, Rarey M (2012) TFD: torsion fingerprints as a new measure to compare small molecule conformations. J Chem Inf Model 52(6):1499–1512. doi:10.1021/ci2002318

    CAS  PubMed  Google Scholar 

  35. Michino M, Abola E, GPCR Dock 2008 participants, Brooks CL 3rd, Dixon JS, Moult J, Stevens RC (2009) Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat Rev Drug Discov 8(6):455–463. doi:10.1038/nrd2877

    CAS  PubMed Central  PubMed  Google Scholar 

  36. Kufareva I, Katritch V, Participants of GPCR Dock 2013, Stevens RC, Abagyan R (2014) Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure 22(8):1120–1139. doi:10.1016/j.str.2014.06.012

    CAS  PubMed Central  PubMed  Google Scholar 

  37. Kufareva I, Rueda M, Katritch V, Stevens RC, Abagyan R, Participants of GPCR Dock 2013 (2011) Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure 19(8):1108–1126. doi:10.1016/j.str.2011.05.012

    CAS  PubMed Central  PubMed  Google Scholar 

  38. Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508. doi:10.1021/ci600426e

    CAS  PubMed  Google Scholar 

  39. Urban JD, Clarke WP, von Zastrow M, Nichols DE, Kobilka B, Weinstein H, Javitch JA, Roth BL, Christopoulos A, Sexton PM, Miller KJ, Spedding M, Mailman RB (2007) Functional selectivity and classical concepts of quantitative pharmacology. J Pharmacol Exp Ther 320(1):1–13. doi:10.1124/jpet.106.104463

    CAS  PubMed  Google Scholar 

  40. Beuming T, Sherman W (2012) Current assessment of docking into GPCR crystal structures and homology models: successes, challenges, and guidelines. J Chem Inf Model 52(12):3263–3277. doi:10.1021/ci300411b

    CAS  PubMed  Google Scholar 

  41. Bottegoni G, Kufareva I, Totrov M, Abagyan R (2009) Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 52(2):397–406. doi:10.1021/jm8009958

    CAS  PubMed Central  PubMed  Google Scholar 

  42. Greenidge PA, Kramer C, Mozziconacci JC, Sherman W (2014) Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA. J Chem Inf Model 54(10):2697–2717. doi:10.1021/ci5003735

    CAS  PubMed  Google Scholar 

  43. Huang SY, Zou X (2007) Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins 66(2):399–421. doi:10.1002/prot.21214

    CAS  PubMed  Google Scholar 

  44. Simms J, Hall NE, Lam PH, Miller LJ, Christopoulos A, Abagyan R, Sexton PM (2009) Homology modeling of GPCRs. Methods Mol Biol 552:97–113. doi:10.1007/978-1-60327-317-6_7

    CAS  PubMed  Google Scholar 

  45. Nguyen ED, Norn C, Frimurer TM, Meiler J (2013) Assessment and challenges of ligand docking into comparative models of G-protein coupled receptors. PLoS One 8(7), e67302. doi:10.1371/journal.pone.0067302

    CAS  PubMed Central  PubMed  Google Scholar 

  46. Costanzi S (2013) Modeling G protein-coupled receptors and their interactions with ligands. Curr Opin Struct Biol 23(2):185–190. doi:10.1016/j.sbi.2013.01.008

    CAS  PubMed  Google Scholar 

  47. Pala D, Beuming T, Sherman W, Lodola A, Rivara S, Mor M (2013) Structure-based virtual screening of MT2 melatonin receptor: influence of template choice and structural refinement. J Chem Inf Model 53(4):821–835. doi:10.1021/ci4000147

    CAS  PubMed  Google Scholar 

  48. Bender A, Glen RC (2005) A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication. J Chem Inf Model 45(5):1369–1375. doi:10.1021/ci0500177

    CAS  PubMed  Google Scholar 

  49. Good AC, Oprea TI (2008) Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J Comput Aided Mol Des 22(3-4):169–178. doi:10.1007/s10822-007-9167-2

    CAS  PubMed  Google Scholar 

  50. Kirchmair J, Markt P, Distinto S, Wolber G, Langer T (2008) Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection: what can we learn from earlier mistakes? J Comput Aided Mol Des 22(3-4):213–228. doi:10.1007/s10822-007-9163-6

    CAS  PubMed  Google Scholar 

  51. Katritch V, Kufareva I, Abagyan R (2011) Structure based prediction of subtype-selectivity for adenosine receptor antagonists. Neuropharmacology 60(1):108–115. doi:10.1016/j.neuropharm.2010.07.009

    CAS  PubMed Central  PubMed  Google Scholar 

  52. Katritch V, Jaakola VP, Lane JR, Lin J, Ijzerman AP, Yeager M, Kufareva I, Stevens RC, Abagyan R (2010) Structure-based discovery of novel chemotypes for adenosine A(2A) receptor antagonists. J Med Chem 53(4):1799–1809. doi:10.1021/jm901647p

    CAS  PubMed Central  PubMed  Google Scholar 

  53. Carlsson J, Yoo L, Gao ZG, Irwin JJ, Shoichet BK, Jacobson KA (2010) Structure-based discovery of A2A adenosine receptor ligands. J Med Chem 53(9):3748–3755. doi:10.1021/jm100240h

    CAS  PubMed Central  PubMed  Google Scholar 

  54. Mysinger MM, Weiss DR, Ziarek JJ, Gravel S, Doak AK, Karpiak J, Heveker N, Shoichet BK, Volkman BF (2012) Structure-based ligand discovery for the protein-protein interface of chemokine receptor CXCR4. Proc Natl Acad Sci U S A 109(14):5517–5522. doi:10.1073/pnas.1120431109

    CAS  PubMed Central  PubMed  Google Scholar 

  55. Kruse AC, Weiss DR, Rossi M, Hu J, Hu K, Eitel K, Gmeiner P, Wess J, Kobilka BK, Shoichet BK (2013) Muscarinic receptors as model targets and antitargets for structure-based ligand discovery. Mol Pharmacol 84(4):528–540. doi:10.1124/mol.113.087551

    CAS  PubMed Central  PubMed  Google Scholar 

  56. de Graaf C, Kooistra AJ, Vischer HF, Katritch V, Kuijer M, Shiroishi M, Iwata S, Shimamura T, Stevens RC, de Esch IJ, Leurs R (2011) Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor. J Med Chem 54(23):8195–8206. doi:10.1021/jm2011589

    PubMed Central  PubMed  Google Scholar 

  57. Negri A, Rives ML, Caspers MJ, Prisinzano TE, Javitch JA, Filizola M (2013) Discovery of a novel selective kappa-opioid receptor agonist using crystal structure-based virtual screening. J Chem Inf Model 53(3):521–526. doi:10.1021/ci400019t

    CAS  PubMed Central  PubMed  Google Scholar 

  58. Carlsson J, Coleman RG, Setola V, Irwin JJ, Fan H, Schlessinger A, Sali A, Roth BL, Shoichet BK (2011) Ligand discovery from a dopamine D3 receptor homology model and crystal structure. Nat Chem Biol 7(11):769–778. doi:10.1038/nchembio.662

    CAS  PubMed Central  PubMed  Google Scholar 

  59. Vass M, Schmidt E, Horti F, Keseru GM (2014) Virtual fragment screening on GPCRs: a case study on dopamine D3 and histamine H4 receptors. Eur J Med Chem 77:38–46. doi:10.1016/j.ejmech.2014.02.034

    CAS  PubMed  Google Scholar 

  60. Lane JR, Chubukov P, Liu W, Canals M, Cherezov V, Abagyan R, Stevens RC, Katritch V (2013) Structure-based ligand discovery targeting orthosteric and allosteric pockets of dopamine receptors. Mol Pharmacol 84(6):794–807. doi:10.1124/mol.113.088054

    CAS  PubMed Central  PubMed  Google Scholar 

  61. Kolb P, Rosenbaum DM, Irwin JJ, Fung JJ, Kobilka BK, Shoichet BK (2009) Structure-based discovery of beta2-adrenergic receptor ligands. Proc Natl Acad Sci U S A 106(16):6843–6848. doi:10.1073/pnas.0812657106

    CAS  PubMed Central  PubMed  Google Scholar 

  62. Vilar S, Ferino G, Phatak SS, Berk B, Cavasotto CN, Costanzi S (2011) Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models. J Mol Graph Model 29(5):614–623. doi:10.1016/j.jmgm.2010.11.005

    CAS  PubMed Central  PubMed  Google Scholar 

  63. Engel S, Skoumbourdis AP, Childress J, Neumann S, Deschamps JR, Thomas CJ, Colson AO, Costanzi S, Gershengorn MC (2008) A virtual screen for diverse ligands: discovery of selective G protein-coupled receptor antagonists. J Am Chem Soc 130(15):5115–5123. doi:10.1021/ja077620l

    CAS  PubMed  Google Scholar 

  64. Costanzi S, Santhosh Kumar T, Balasubramanian R, Kendall Harden T, Jacobson KA (2012) Virtual screening leads to the discovery of novel non-nucleotide P2Y(1) receptor antagonists. Bioorg Med Chem 20(17):5254–5261. doi:10.1016/j.bmc.2012.06.044

    CAS  PubMed Central  PubMed  Google Scholar 

  65. Kooistra AJ, Roumen L, Leurs R, de Esch IJ, de Graaf C (2013) From heptahelical bundle to hits from the Haystack: structure-based virtual screening for GPCR ligands. Methods Enzymol 522:279–336. doi:10.1016/B978-0-12-407865-9.00015-7

    CAS  PubMed  Google Scholar 

  66. Andrews SP, Brown GA, Christopher JA (2014) Structure-based and fragment-based GPCR drug discovery. ChemMedChem 9(2):256–275. doi:10.1002/cmdc.201300382

    CAS  PubMed  Google Scholar 

  67. Chin SP, Buckle MJ, Chalmers DK, Yuriev E, Doughty SW (2014) Toward activated homology models of the human M1 muscarinic acetylcholine receptor. J Mol Graph Model 49:91–98. doi:10.1016/j.jmgm.2014.02.002

    CAS  PubMed  Google Scholar 

  68. Kolaczkowski M, Bucki A, Feder M, Pawlowski M (2013) Ligand-optimized homology models of D(1) and D(2) dopamine receptors: application for virtual screening. J Chem Inf Model 53(3):638–648. doi:10.1021/ci300413h

    CAS  PubMed  Google Scholar 

  69. McRobb FM, Capuano B, Crosby IT, Chalmers DK, Yuriev E (2010) Homology modeling and docking evaluation of aminergic G protein-coupled receptors. J Chem Inf Model 50(4):626–637. doi:10.1021/ci900444q

    CAS  PubMed  Google Scholar 

  70. Thomas T, McLean KC, McRobb FM, Manallack DT, Chalmers DK, Yuriev E (2014) Homology modeling of human muscarinic acetylcholine receptors. J Chem Inf Model 54(1):243–253. doi:10.1021/ci400502u

    CAS  PubMed  Google Scholar 

  71. Gatica EA, Cavasotto CN (2012) Ligand and decoy sets for docking to G protein-coupled receptors. J Chem Inf Model 52(1):1–6. doi:10.1021/ci200412p

    CAS  PubMed  Google Scholar 

  72. Weiss DR, Ahn S, Sassano MF, Kleist A, Zhu X, Strachan R, Roth BL, Lefkowitz RJ, Shoichet BK (2013) Conformation guides molecular efficacy in docking screens of activated beta-2 adrenergic G protein coupled receptor. ACS Chem Biol 8(5):1018–1026. doi:10.1021/cb400103f

    CAS  PubMed Central  PubMed  Google Scholar 

  73. Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44(1):235–249

    CAS  PubMed  Google Scholar 

  74. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46(1-3):3–26

    CAS  PubMed  Google Scholar 

  75. Teague SJ, Davis AM, Leeson PD, Oprea T (1999) The design of leadlike combinatorial libraries. Angew Chem Int Ed Engl 38(24):3743–3748

    CAS  PubMed  Google Scholar 

  76. Langmead CJ, Andrews SP, Congreve M, Errey JC, Hurrell E, Marshall FH, Mason JS, Richardson CM, Robertson N, Zhukov A, Weir M (2012) Identification of novel adenosine A(2A) receptor antagonists by virtual screening. J Med Chem 55(5):1904–1909. doi:10.1021/jm201455y

    CAS  PubMed Central  PubMed  Google Scholar 

  77. Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem 47(2):337–344. doi:10.1021/jm030331x

    CAS  PubMed  Google Scholar 

  78. de Graaf C, Rognan D (2008) Selective structure-based virtual screening for full and partial agonists of the beta2 adrenergic receptor. J Med Chem 51(16):4978–4985. doi:10.1021/jm800710x

    PubMed  Google Scholar 

  79. Rose PW, Prlic A, Bi C, Bluhm WF, Christie CH, Dutta S, Green RK, Goodsell DS, Westbrook JD, Woo J, Young J, Zardecki C, Berman HM, Bourne PE, Burley SK (2014) The RCSB protein data bank: views of structural biology for basic and applied research and education. Nucleic Acids Res 43:D345–D356. doi:10.1093/nar/gku1214

    PubMed Central  PubMed  Google Scholar 

  80. Allen FH (2002) The cambridge structural database: a quarter of a million crystal structures and rising. Acta Crystallogr B 58(Pt 3 Pt 1):380–388

    PubMed  Google Scholar 

  81. Ballesteros JA, Weinstein H (1995) [19] Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Methods Neurosci 25:366–428

    CAS  Google Scholar 

  82. Shi L, Javitch JA (2004) The second extracellular loop of the dopamine D2 receptor lines the binding-site crevice. Proc Natl Acad Sci U S A 101(2):440–445. doi:10.1073/pnas.2237265100

    CAS  PubMed Central  PubMed  Google Scholar 

  83. Abel R, Salam NK, Shelley J, Farid R, Friesner RA, Sherman W (2011) Contribution of explicit solvent effects to the binding affinity of small-molecule inhibitors in blood coagulation factor serine proteases. ChemMedChem 6(6):1049–1066. doi:10.1002/cmdc.201000533

    CAS  PubMed  Google Scholar 

  84. Rapp C, Kalyanaraman C, Schiffmiller A, Schoenbrun EL, Jacobson MP (2011) A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. J Chem Inf Model 51(9):2082–2089. doi:10.1021/ci200033n

    CAS  PubMed Central  PubMed  Google Scholar 

  85. Sastry GM, Inakollu VS, Sherman W (2013) Boosting virtual screening enrichments with data fusion: coalescing hits from two-dimensional fingerprints, shape, and docking. J Chem Inf Model 53(7):1531–1542. doi:10.1021/ci300463g

    CAS  PubMed  Google Scholar 

  86. MacroModel v10.5 (2014) Schrödinger, Inc., Portland, OR

    Google Scholar 

  87. Jaguar v8.5 (2014) Schrödinger, Inc., Portland, OR

    Google Scholar 

  88. Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130(9):2817–2831. doi:10.1021/ja0771033

    CAS  PubMed Central  PubMed  Google Scholar 

  89. Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein ligand binding. Proc Natl Acad Sci U S A 104:808–813

    CAS  PubMed Central  PubMed  Google Scholar 

  90. Beuming T, Che Y, Abel R, Kim B, Shanmugasundaram V, Sherman W (2012) Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization. Proteins 80(3):871–883. doi:10.1002/prot.23244

    CAS  PubMed  Google Scholar 

  91. Mason JS, Bortolato A, Congreve M, Marshall FH (2012) New insights from structural biology into the druggability of G protein-coupled receptors. Trends Pharmacol Sci 33(5):249–260. doi:10.1016/j.tips.2012.02.005

    CAS  PubMed  Google Scholar 

  92. Higgs C, Beuming T, Sherman W (2010) Hydration site thermodynamics explain SARs for Triazolylpurines analogues binding to the A2A receptor. ACS Med Chem Lett 1(4):160–164. doi:10.1021/ml100008s

    CAS  PubMed Central  PubMed  Google Scholar 

  93. Ivanov AA, Barak D, Jacobson KA (2009) Evaluation of homology modeling of G-protein-coupled receptors in light of the A(2A) adenosine receptor crystallographic structure. J Med Chem 52(10):3284–3292. doi:10.1021/jm801533x

    CAS  PubMed Central  PubMed  Google Scholar 

  94. Liu W, Chun E, Thompson AA, Chubukov P, Xu F, Katritch V, Han GW, Roth CB, Heitman LH, IJzerman AP, Cherezov V, Stevens RC (2012) Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337(6091):232–236. doi:10.1126/science.1219218

    CAS  PubMed Central  PubMed  Google Scholar 

  95. 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. doi:10.1038/nature12944

    CAS  PubMed Central  PubMed  Google Scholar 

  96. Lenselink EB, Beuming T, Sherman W, van Vlijmen HW, IJzerman AP (2014) Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor. J Chem Inf Model 54(6):1737–1746. doi:10.1021/ci5000455

    CAS  PubMed  Google Scholar 

  97. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20(10-11):647–671. doi:10.1007/s10822-006-9087-6

    CAS  PubMed  Google Scholar 

  98. Salam NK, Nuti R, Sherman W (2009) Novel method for generating structure-based pharmacophores using energetic analysis. J Chem Inf Model 49(10):2356–2368. doi:10.1021/ci900212v

    CAS  PubMed  Google Scholar 

  99. Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169. doi:10.1021/ci049885e

    CAS  PubMed  Google Scholar 

  100. Kirchmair J, Distinto S, Markt P, Schuster D, Spitzer GM, Liedl KR, Wolber G (2009) How to optimize shape-based virtual screening: choosing the right query and including chemical information. J Chem Inf Model 49(3):678–692. doi:10.1021/ci8004226

    CAS  PubMed  Google Scholar 

  101. Sastry GM, Dixon SL, Sherman W (2011) Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. J Chem Inf Model 51(10):2455–2466. doi:10.1021/ci2002704

    CAS  PubMed  Google Scholar 

  102. Eckert H, Bajorath J (2007) Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today 12(5-6):225–233. doi:10.1016/j.drudis.2007.01.011

    CAS  PubMed  Google Scholar 

  103. Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11(23-24):1046–1053. doi:10.1016/j.drudis.2006.10.005

    CAS  PubMed  Google Scholar 

  104. Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2006) New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model 46(2):462–470. doi:10.1021/ci050348j

    CAS  PubMed  Google Scholar 

  105. Tan L, Geppert H, Sisay MT, Gutschow M, Bajorath J (2008) Integrating structure- and ligand-based virtual screening: comparison of individual, parallel, and fused molecular docking and similarity search calculations on multiple targets. ChemMedChem 3(10):1566–1571. doi:10.1002/cmdc.200800129

    CAS  PubMed  Google Scholar 

  106. Zhang Q, Muegge I (2006) Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J Med Chem 49(5):1536–1548. doi:10.1021/jm050468i

    CAS  PubMed  Google Scholar 

  107. Rasmussen SG, DeVree BT, Zou Y, Kruse AC, Chung KY, Kobilka TS, Thian FS, Chae PS, Pardon E, Calinski D, Mathiesen JM, Shah ST, Lyons JA, Caffrey M, Gellman SH, Steyaert J, Skiniotis G, Weis WI, Sunahara RK, Kobilka BK (2011) Crystal structure of the beta2 adrenergic receptor-Gs protein complex. Nature 477(7366):549–555. doi:10.1038/nature10361

    CAS  PubMed Central  PubMed  Google Scholar 

  108. Warne T, Moukhametzianov R, Baker JG, Nehme R, Edwards PC, Leslie AG, Schertler GF, Tate CG (2011) The structural basis for agonist and partial agonist action on a beta(1)-adrenergic receptor. Nature 469(7329):241–244. doi:10.1038/nature09746

    CAS  PubMed Central  PubMed  Google Scholar 

  109. Lebon G, Warne T, Edwards PC, Bennett K, Langmead CJ, Leslie AG, Tate CG (2011) Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation. Nature 474(7352):521–525. doi:10.1038/nature10136

    CAS  PubMed Central  PubMed  Google Scholar 

  110. Zhang J, Zhang K, Gao ZG, Paoletta S, Zhang D, Han GW, Li T, Ma L, Zhang W, Muller CE, Yang H, Jiang H, Cherezov V, Katritch V, Jacobson KA, Stevens RC, Wu B, Zhao Q (2014) Agonist-bound structure of the human P2Y12 receptor. Nature 509(7498):119–122. doi:10.1038/nature13288

    CAS  PubMed Central  PubMed  Google Scholar 

  111. Egloff P, Hillenbrand M, Klenk C, Batyuk A, Heine P, Balada S, Schlinkmann KM, Scott DJ, Schutz M, Pluckthun A (2014) Structure of signaling-competent neurotensin receptor 1 obtained by directed evolution in Escherichia coli. Proc Natl Acad Sci U S A 111(6):E655–E662. doi:10.1073/pnas.1317903111

    CAS  PubMed Central  PubMed  Google Scholar 

  112. 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. doi:10.1038/nature12735

    CAS  PubMed Central  PubMed  Google Scholar 

  113. Costanzi S, Vilar S (2012) In silico screening for agonists and blockers of the beta(2) adrenergic receptor: implications of inactive and activated state structures. J Comput Chem 33(5):561–572. doi:10.1002/jcc.22893

    CAS  PubMed Central  PubMed  Google Scholar 

  114. Free RB, Chun LS, Moritz AE, Miller BN, Doyle TB, Conroy JL, Padron A, Meade JA, Xiao J, Hu X, Dulcey AE, Han Y, Duan L, Titus S, Bryant-Genevier M, Barnaeva E, Ferrer M, Javitch JA, Beuming T, Shi L, Southall NT, Marugan JJ, Sibley DR (2014) Discovery and characterization of a G protein-biased agonist that inhibits beta-arrestin recruitment to the D2 dopamine receptor. Mol Pharmacol 86(1):96–105. doi:10.1124/mol.113.090563

    PubMed Central  PubMed  Google Scholar 

  115. Vilar S, Karpiak J, Berk B, Costanzi S (2011) In silico analysis of the binding of agonists and blockers to the beta2-adrenergic receptor. J Mol Graph Model 29(6):809–817. doi:10.1016/j.jmgm.2011.01.005

    CAS  PubMed Central  PubMed  Google Scholar 

  116. Goldfeld DA, Murphy R, Kim B, Wang L, Beuming T, Abel R, Friesner RA (2014) Docking and free energy perturbation studies of ligand binding in the kappa opioid receptor. J Phys Chem B 119:824–835. doi:10.1021/jp5053612

    PubMed  Google Scholar 

  117. 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):13118–13123. doi:10.1073/pnas.1104614108

    CAS  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Schrödinger, Inc., 120 West 45th Street, New York, NY, 10036, USA

    Thijs Beuming, Fiona McRobb, Matt Repasky & Woody Sherman

  2. Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands

    Bart Lenselink

  3. Dipartimento di Farmacia, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124, Parma, Italy

    Daniele Pala

Authors
  1. Thijs Beuming
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniele Pala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fiona McRobb
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matt Repasky
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Woody Sherman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thijs Beuming .

Editor information

Editors and Affiliations

  1. Department of Structural and Chemical Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Marta Filizola

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Beuming, T., Lenselink, B., Pala, D., McRobb, F., Repasky, M., Sherman, W. (2015). Docking and Virtual Screening Strategies for GPCR Drug Discovery. In: Filizola, M. (eds) G Protein-Coupled Receptors in Drug Discovery. Methods in Molecular Biology, vol 1335. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2914-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2914-6_17

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2913-9

  • Online ISBN: 978-1-4939-2914-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation