Protein–Protein Docking in Drug Design and Discovery

  • Agnieszka A. Kaczor
  • Damian Bartuzi
  • Tomasz Maciej Stępniewski
  • Dariusz Matosiuk
  • Jana Selent
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1762)

Abstract

Protein–protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called “hot spots” in protein–protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein–protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein–protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein–protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein–protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein–protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein–protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

Key words

Drug design and discovery GPCRs Molecular modeling Protein–protein docking Transmembrane proteins Water-soluble proteins 

Notes

Acknowledgments

The chapter was developed using the equipment purchased within the project “The equipment of innovative laboratories doing research on new medicines used in the therapy of civilization and neoplastic diseases” within the Operational Program Development of Eastern Poland 2007–2013, Priority Axis I Modern Economy, operations I.3 Innovation promotion. T.S. and J.S. acknowledge support from Instituto de Salud Carlos III FEDER (CP12/03139 and PI15/00460). A.A.K., T.S. and J.S. participate in the European COST Action CM1207 (GLISTEN). T.S. acknowledges financial support from Hospital del Mar Medical Research Institute.

References

  1. 1.
    Andreani J, Guerois R (2014) Evolution of protein interactions: from interactomes to interfaces. Arch Biochem Biophys 554:65–75. https://doi.org/10.1016/j.abb.2014.05.010 CrossRefPubMedGoogle Scholar
  2. 2.
    Petta I, Lievens S, Libert C et al (2016) Modulation of protein-protein interactions for the development of novel therapeutics. Mol Ther J Am Soc Gene Ther 24:707–718. https://doi.org/10.1038/mt.2015.214 CrossRefGoogle Scholar
  3. 3.
    Gromiha MM, Yugandhar K, Jemimah S (2016) Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol 44:31–38. https://doi.org/10.1016/j.sbi.2016.10.016 CrossRefPubMedGoogle Scholar
  4. 4.
    Moal IH, Moretti R, Baker D, Fernández-Recio J (2013) Scoring functions for protein-protein interactions. Curr Opin Struct Biol 23:862–867. https://doi.org/10.1016/j.sbi.2013.06.017 CrossRefPubMedGoogle Scholar
  5. 5.
    Huang S-Y (2015) Exploring the potential of global protein-protein docking: an overview and critical assessment of current programs for automatic ab initio docking. Drug Discov Today 20:969–977. https://doi.org/10.1016/j.drudis.2015.03.007 CrossRefPubMedGoogle Scholar
  6. 6.
    Rodrigues JPGLM, Bonvin AMJJ (2014) Integrative computational modeling of protein interactions. FEBS J 281:1988–2003. https://doi.org/10.1111/febs.12771 CrossRefPubMedGoogle Scholar
  7. 7.
    Selent J, Kaczor AA (2011) Oligomerization of G protein-coupled receptors: computational methods. Curr Med Chem 18:4588–4605CrossRefPubMedGoogle Scholar
  8. 8.
    Kaczor AA, Selent J, Poso A (2013) Structure-based molecular modeling approaches to GPCR oligomerization. Methods Cell Biol 117:91–104. https://doi.org/10.1016/B978-0-12-408143-7.00005-0 CrossRefPubMedGoogle Scholar
  9. 9.
    Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288CrossRefPubMedGoogle Scholar
  10. 10.
    Wodak SJ, Janin J (1978) Computer analysis of protein-protein interaction. J Mol Biol 124:323–342CrossRefPubMedGoogle Scholar
  11. 11.
    Janin J (2010) Protein-protein docking tested in blind predictions: the CAPRI experiment. Mol Biosyst 6:2351–2362. https://doi.org/10.1039/c005060c CrossRefPubMedGoogle Scholar
  12. 12.
    Lensink MF, Wodak SJ (2013) Docking, scoring, and affinity prediction in CAPRI. Proteins 81:2082–2095. https://doi.org/10.1002/prot.24428 CrossRefPubMedGoogle Scholar
  13. 13.
    Lensink MF, Velankar S, Wodak SJ (2017) Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition. Proteins 85:359–377. https://doi.org/10.1002/prot.25215 CrossRefPubMedGoogle Scholar
  14. 14.
    Bohnuud T, Luo L, Wodak SJ et al (2017) A benchmark testing ground for integrating homology modeling and protein docking. Proteins 85:10–16. https://doi.org/10.1002/prot.25063 CrossRefPubMedGoogle Scholar
  15. 15.
    Park H, Lee H, Seok C (2015) High-resolution protein-protein docking by global optimization: recent advances and future challenges. Curr Opin Struct Biol 35:24–31. https://doi.org/10.1016/j.sbi.2015.08.001 CrossRefPubMedGoogle Scholar
  16. 16.
    Kaczor AA, Selent J, Sanz F, Pastor M (2013) Modeling complexes of transmembrane proteins: systematic analysis of protein-protein docking tools. Mol Inform 32:717–733. https://doi.org/10.1002/minf.201200150 CrossRefPubMedGoogle Scholar
  17. 17.
    Zacharias M (2010) Accounting for conformational changes during protein-protein docking. Curr Opin Struct Biol 20:180–186. https://doi.org/10.1016/j.sbi.2010.02.001 CrossRefPubMedGoogle Scholar
  18. 18.
    Zacharias M (2003) Protein-protein docking with a reduced protein model accounting for side-chain flexibility. Protein Sci Publ Protein Soc 12:1271–1282. https://doi.org/10.1110/ps.0239303 CrossRefGoogle Scholar
  19. 19.
    Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:W363–W367. https://doi.org/10.1093/nar/gki481 CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    Gabb HA, Jackson RM, Sternberg MJ (1997) Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol 272:106–120. https://doi.org/10.1006/jmbi.1997.1203 CrossRefPubMedGoogle Scholar
  21. 21.
    Vakser IA (1997) Evaluation of GRAMM low-resolution docking methodology on the hemagglutinin-antibody complex. Proteins (Suppl 1):226–230Google Scholar
  22. 22.
    Tovchigrechko A, Vakser IA (2006) GRAMM-X public web server for protein-protein docking. Nucleic Acids Res 34:W310–W314. https://doi.org/10.1093/nar/gkl206 CrossRefPubMedCentralPubMedGoogle Scholar
  23. 23.
    Katchalski-Katzir E, Shariv I, Eisenstein M et al (1992) Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci U S A 89:2195–2199CrossRefPubMedCentralPubMedGoogle Scholar
  24. 24.
    Berchanski A, Shapira B, Eisenstein M (2004) Hydrophobic complementarity in protein-protein docking. Proteins 56:130–142. https://doi.org/10.1002/prot.20145 CrossRefPubMedGoogle Scholar
  25. 25.
    Heifetz A, Katchalski-Katzir E, Eisenstein M (2002) Electrostatics in protein-protein docking. Protein Sci Publ Protein Soc 11:571–587CrossRefGoogle Scholar
  26. 26.
    Mandell JG, Roberts VA, Pique ME et al (2001) Protein docking using continuum electrostatics and geometric fit. Protein Eng 14:105–113CrossRefPubMedGoogle Scholar
  27. 27.
    Roberts VA, Thompson EE, Pique ME et al (2013) DOT2: macromolecular docking with improved biophysical models. J Comput Chem 34:1743–1758. https://doi.org/10.1002/jcc.23304 CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    Wiehe K, Pierce B, Mintseris J et al (2005) ZDOCK and RDOCK performance in CAPRI rounds 3, 4, and 5. Proteins 60:207–213. https://doi.org/10.1002/prot.20559 CrossRefPubMedGoogle Scholar
  29. 29.
    Kozakov D, Brenke R, Comeau SR, Vajda S (2006) PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65:392–406. https://doi.org/10.1002/prot.21117 CrossRefPubMedGoogle Scholar
  30. 30.
    Zhang C, Lai L (2011) SDOCK: a global protein-protein docking program using stepwise force-field potentials. J Comput Chem 32:2598–2612. https://doi.org/10.1002/jcc.21839 CrossRefPubMedGoogle Scholar
  31. 31.
    Comeau SR, Gatchell DW, Vajda S, Camacho CJ (2004) ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res 32:W96–W99. https://doi.org/10.1093/nar/gkh354 CrossRefPubMedCentralPubMedGoogle Scholar
  32. 32.
    Comeau SR, Kozakov D, Brenke R et al (2007) ClusPro: performance in CAPRI rounds 6-11 and the new server. Proteins 69:781–785. https://doi.org/10.1002/prot.21795 CrossRefPubMedGoogle Scholar
  33. 33.
    Ritchie DW (2003) Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2. Proteins 52:98–106. https://doi.org/10.1002/prot.10379 CrossRefPubMedGoogle Scholar
  34. 34.
    Garzon JI, Lopéz-Blanco JR, Pons C et al (2009) FRODOCK: a new approach for fast rotational protein-protein docking. Bioinformatics 25:2544–2551. https://doi.org/10.1093/bioinformatics/btp447 CrossRefPubMedCentralPubMedGoogle Scholar
  35. 35.
    Dominguez C, Boelens R, Bonvin AMJJ (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737. https://doi.org/10.1021/ja026939x CrossRefPubMedGoogle Scholar
  36. 36.
    de Vries SJ, van Dijk ADJ, Krzeminski M et al (2007) HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins 69:726–733. https://doi.org/10.1002/prot.21723 CrossRefPubMedGoogle Scholar
  37. 37.
    Hwang H, Vreven T, Janin J, Weng Z (2010) Protein-protein docking benchmark version 4.0. Proteins 78:3111–3114. https://doi.org/10.1002/prot.22830 CrossRefPubMedCentralPubMedGoogle Scholar
  38. 38.
    Vajda S (2005) Classification of protein complexes based on docking difficulty. Proteins 60:176–180. https://doi.org/10.1002/prot.20554 CrossRefPubMedGoogle Scholar
  39. 39.
    Selent J, Kaczor AA, Guixà-González R et al (2013) Rational design of the survivin/CDK4 complex by combining protein-protein docking and molecular dynamics simulations. J Mol Model 19:1507–1514. https://doi.org/10.1007/s00894-012-1705-8 CrossRefPubMedGoogle Scholar
  40. 40.
    Renthal R (1999) Transmembrane and water-soluble helix bundles display reverse patterns of surface roughness. Biochem Biophys Res Commun 263:714–717. https://doi.org/10.1006/bbrc.1999.1439 CrossRefPubMedGoogle Scholar
  41. 41.
    Kaczor AA, Guixà-González R, Carrió P et al (2012) Fractal dimension as a measure of surface roughness of G protein-coupled receptors: implications for structure and function. J Mol Model 18:4465–4475. https://doi.org/10.1007/s00894-012-1431-2 CrossRefPubMedCentralPubMedGoogle Scholar
  42. 42.
    Suzuki Y (2017) Predicting receptor functionality of signaling lymphocyte activation molecule for measles virus hemagglutinin from docking simulation. Microbiol Immunol. https://doi.org/10.1111/1348-0421.12484
  43. 43.
    Dar HA, Zaheer T, Paracha RZ, Ali A (2017) Structural analysis and insight into Zika virus NS5 mediated interferon inhibition. Infect Genet Evol 51:143–152. https://doi.org/10.1016/j.meegid.2017.03.027 CrossRefPubMedGoogle Scholar
  44. 44.
    Antal Z, Szoverfi J, Fejer SN (2017) Predicting the initial steps of salt-stable cowpea chlorotic mottle virus capsid assembly with atomistic force fields. J Chem Inf Model 57:910–917. https://doi.org/10.1021/acs.jcim.7b00078 CrossRefPubMedGoogle Scholar
  45. 45.
    Hossain MS, Azad AK, Chowdhury PA, Wakayama M (2017) Computational identification and characterization of a promiscuous T-cell epitope on the extracellular protein 85B of mycobacterium spp. for peptide-based subunit vaccine design. Biomed Res Int 2017:4826030. https://doi.org/10.1155/2017/4826030 PubMedCentralPubMedGoogle Scholar
  46. 46.
    He Y, Xiang Z, Mobley HLT (2010) Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J Biomed Biotechnol 2010:297505. https://doi.org/10.1155/2010/297505 PubMedCentralPubMedGoogle Scholar
  47. 47.
    Totrov M, Abagyan R (1997) Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins (Suppl 1):215–220Google Scholar
  48. 48.
    Rawal L, Panwar D, Ali S (2017) Intermolecular interactions between DMα and DMβ proteins in BuLA-DM complex of water buffalo Bubalus bubalis. J Cell Biochem. https://doi.org/10.1002/jcb.26075
  49. 49.
    Dundas J, Ouyang Z, Tseng J et al (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34:W116–W118. https://doi.org/10.1093/nar/gkl282 CrossRefPubMedCentralPubMedGoogle Scholar
  50. 50.
    Krissinel E, Henrick K (2007) Inference of macromolecular assemblies from crystalline state. J Mol Biol 372:774–797. https://doi.org/10.1016/j.jmb.2007.05.022 CrossRefPubMedGoogle Scholar
  51. 51.
    Sinha VK, Sharma OP, Kumar MS (2017) Insight into the intermolecular recognition mechanism involved in complement component 4 activation through serine protease-trypsin. J Biomol Struct Dyn:1–15. https://doi.org/10.1080/07391102.2017.1288658
  52. 52.
    Prakash P, Sayyed-Ahmad A, Cho KJ et al (2017) Computational and biochemical characterization of two partially overlapping interfaces and multiple weak-affinity K-Ras dimers. Sci Rep 7:40109. https://doi.org/10.1038/srep40109 CrossRefPubMedCentralPubMedGoogle Scholar
  53. 53.
    Congreve M, Langmead CJ, Mason JS, Marshall FH (2011) Progress in structure based drug design for G protein-coupled receptors. J Med Chem 54:4283–4311. https://doi.org/10.1021/jm200371q CrossRefPubMedCentralPubMedGoogle Scholar
  54. 54.
    Pierce KL, Premont RT, Lefkowitz RJ (2002) Seven-transmembrane receptors. Nat Rev Mol Cell Biol 3:639–650. https://doi.org/10.1038/nrm908 CrossRefPubMedGoogle Scholar
  55. 55.
    Gilman AG (1987) G proteins: transducers of receptor-generated signals. Annu Rev Biochem 56:615–649. https://doi.org/10.1146/annurev.biochem.56.1.615 CrossRefPubMedGoogle Scholar
  56. 56.
    Bouvier M (2001) Oligomerization of G-protein-coupled transmitter receptors. Nat Rev Neurosci 2:274–286. https://doi.org/10.1038/35067575 CrossRefPubMedGoogle Scholar
  57. 57.
    Ferre S, Casado V, Devi LA et al (2014) G protein-coupled receptor oligomerization revisited: functional and pharmacological perspectives. Pharmacol Rev 66:413–434. https://doi.org/10.1124/pr.113.008052 CrossRefPubMedCentralPubMedGoogle Scholar
  58. 58.
    González-Maeso J (2011) GPCR oligomers in pharmacology and signaling. Mol Brain 4:20. https://doi.org/10.1186/1756-6606-4-20 CrossRefPubMedCentralPubMedGoogle Scholar
  59. 59.
    Kniazeff J, Prézeau L, Rondard P et al (2011) Dimers and beyond: the functional puzzles of class C GPCRs. Pharmacol Ther 130:9–25. https://doi.org/10.1016/j.pharmthera.2011.01.006 CrossRefPubMedGoogle Scholar
  60. 60.
    Bellot M, Galandrin S, Boularan C et al (2015) Dual agonist occupancy of AT1-R-α2C-AR heterodimers results in atypical Gs-PKA signaling. Nat Chem Biol 11:271–279. https://doi.org/10.1038/nchembio.1766 CrossRefPubMedCentralPubMedGoogle Scholar
  61. 61.
    Rashid AJ, So CH, Kong MMC et al (2007) D1–D2 dopamine receptor heterooligomers with unique pharmacology are coupled to rapid activation of Gq/11 in the striatum. Proc Natl Acad Sci U S A 104:654–659. https://doi.org/10.1073/pnas.0604049104 CrossRefPubMedGoogle Scholar
  62. 62.
    Han Y, Moreira IS, Urizar E et al (2009) Allosteric communication between protomers of dopamine class A GPCR dimers modulates activation. Nat Chem Biol 5:688–695. https://doi.org/10.1038/nchembio.199 CrossRefPubMedCentralPubMedGoogle Scholar
  63. 63.
    Smith NJ, Milligan G (2010) Allostery at G protein-coupled receptor homo- and heteromers: uncharted pharmacological landscapes. Pharmacol Rev 62:701–725. https://doi.org/10.1124/pr.110.002667 CrossRefPubMedCentralPubMedGoogle Scholar
  64. 64.
    Bouvier M, Hébert TE (2014) CrossTalk proposal: weighing the evidence for class A GPCR dimers, the evidence favours dimers. J Physiol 592:2439–2441. https://doi.org/10.1113/jphysiol.2014.272252 CrossRefPubMedCentralPubMedGoogle Scholar
  65. 65.
    Lambert NA, Javitch JA (2014) CrossTalk opposing view: weighing the evidence for class A GPCR dimers, the jury is still out. J Physiol 592:2443–2445. https://doi.org/10.1113/jphysiol.2014.272997 CrossRefPubMedCentralPubMedGoogle Scholar
  66. 66.
    James JR, Oliveira MI, Carmo AM et al (2006) A rigorous experimental framework for detecting protein oligomerization using bioluminescence resonance energy transfer. Nat Methods 3:1001–1006. https://doi.org/10.1038/nmeth978 CrossRefPubMedGoogle Scholar
  67. 67.
    Meyer BH, Segura J-M, Martinez KL et al (2006) FRET imaging reveals that functional neurokinin-1 receptors are monomeric and reside in membrane microdomains of live cells. Proc Natl Acad Sci U S A 103:2138–2143. https://doi.org/10.1073/pnas.0507686103 CrossRefPubMedCentralPubMedGoogle Scholar
  68. 68.
    Gaitonde SA, Gonzá Lez-Maeso J (2017) Contribution of heteromerization to G protein-coupled receptor function. Curr Opin Pharmacol 32:23–31. https://doi.org/10.1016/j.coph.2016.10.006 CrossRefPubMedGoogle Scholar
  69. 69.
    Guidolin D, Agnati LF, Marcoli M et al (2014) G-protein-coupled receptor type A heteromers as an emerging therapeutic target. Expert Opin Ther Targets 8222:1–19. https://doi.org/10.1517/14728222.2014.981155 Google Scholar
  70. 70.
    Shonberg J, Scammells PJ, Capuano B (2011) Design strategies for bivalent ligands targeting GPCRs. ChemMedChem 6:963–974. https://doi.org/10.1002/cmdc.201100101 CrossRefPubMedGoogle Scholar
  71. 71.
    Viñals X, Moreno E, Lanfumey L et al (2015) Cognitive impairment induced by delta9-tetrahydrocannabinol occurs through heteromers between cannabinoid CB1 and serotonin 5-HT2A receptors. PLoS Biol. https://doi.org/10.1371/journal.pbio.1002194
  72. 72.
    Jastrzebska B, Chen Y, Orban T et al (2015) Disruption of rhodopsin dimerization with synthetic peptides targeting an interaction interface. J Biol Chem 290:25728–25744. https://doi.org/10.1074/jbc.M115.662684 CrossRefPubMedCentralPubMedGoogle Scholar
  73. 73.
    Wang J, He L, Combs C et al (2006) Dimerization of CXCR4 in living malignant cells: control of cell migration by a synthetic peptide that reduces homologous CXCR4 interactions. Mol Cancer Ther 5:2474–2483. https://doi.org/10.1158/1535-7163.MCT-05-0261 CrossRefPubMedGoogle Scholar
  74. 74.
    Hebert TE, Moffett S, Morello JP et al (1996) A peptide derived from a beta2-adrenergic receptor transmembrane domain inhibits both receptor dimerization and activation. J Biol Chem 271:16384–16392. https://doi.org/10.1074/jbc.271.27.16384 CrossRefPubMedGoogle Scholar
  75. 75.
    Khelashvili G, Dorff K, Shan J et al (2010) GPCR-OKB: the G protein coupled receptor oligomer knowledge base. Bioinformatics 26:1804–1805. https://doi.org/10.1093/bioinformatics/btq264 CrossRefPubMedCentralPubMedGoogle Scholar
  76. 76.
    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 22:1120–1139. https://doi.org/10.1016/j.str.2014.06.012 CrossRefPubMedCentralPubMedGoogle Scholar
  77. 77.
    Casciari D, Seeber M, Fanelli F (2006) Quaternary structure predictions of transmembrane proteins starting from the monomer: a docking-based approach. BMC Bioinformatics 7:340. https://doi.org/10.1186/1471-2105-7-340 CrossRefPubMedCentralPubMedGoogle Scholar
  78. 78.
    Dell’Orco D, Casciari D, Fanelli F (2008) Quaternary structure predictions and estimation of mutational effects on the free energy of dimerization of the OMPLA protein. J Struct Biol 163:155–162. https://doi.org/10.1016/j.jsb.2008.05.006 CrossRefPubMedGoogle Scholar
  79. 79.
    Kaczor AA, Guixà-González R, Carriõ P et al (2015) Multi-component protein – protein docking based protocol with external scoring for modeling dimers of g protein-coupled receptors. Mol Inform 34:246–255. https://doi.org/10.1002/minf.201400088 CrossRefPubMedGoogle Scholar
  80. 80.
    Chaudhury S, Berrondo M, Weitzner BD et al (2011) Benchmarking and analysis of protein docking performance in Rosetta v3.2. PLoS One 6:e22477. https://doi.org/10.1371/journal.pone.0022477 CrossRefPubMedCentralPubMedGoogle Scholar
  81. 81.
    Jörg M, Kaczor AA, Mak FS et al (2014) Investigation of novel ropinirole analogues: synthesis, pharmacological evaluation and computational analysis of dopamine D2 receptor functionalized congeners and homobivalent ligands. MedChemComm 5:891–898. https://doi.org/10.1039/C4MD00066H CrossRefGoogle Scholar
  82. 82.
    Kaczor AA, Jörg M, Capuano B (2016) The dopamine D2 receptor dimer and its interaction with homobivalent antagonists: homology modeling, docking and molecular dynamics. J Mol Model 22:203. https://doi.org/10.1007/s00894-016-3065-2 CrossRefPubMedCentralPubMedGoogle Scholar
  83. 83.
    Viswanath S, Dominguez L, Foster LS et al (2015) Extension of a protein docking algorithm to membranes and applications to amyloid precursor protein dimerization. Proteins 83:2170–2185. https://doi.org/10.1002/prot.24934 CrossRefPubMedCentralPubMedGoogle Scholar
  84. 84.
    MacCallum JL, Bennett WFD, Tieleman DP (2007) Partitioning of amino acid side chains into lipid bilayers: results from computer simulations and comparison to experiment. J Gen Physiol 129:371–377. https://doi.org/10.1085/jgp.200709745 CrossRefPubMedCentralPubMedGoogle Scholar
  85. 85.
    Alford RF, Koehler Leman J, Weitzner BD et al (2015) An integrated framework advancing membrane protein modeling and design. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1004398
  86. 86.
    Hurwitz N, Schneidman-Duhovny D, Wolfson HJ (2016) Memdock: an α-helical membrane protein docking algorithm. Bioinformatics 32:2444–2450. https://doi.org/10.1093/bioinformatics/btw184 CrossRefPubMedGoogle Scholar
  87. 87.
    Guixà-González R, Javanainen M, Gómez-Soler M et al (2016) Membrane omega-3 fatty acids modulate the oligomerisation kinetics of adenosine A2A and dopamine D2 receptors. Sci Rep 6:19839. https://doi.org/10.1038/srep19839 CrossRefPubMedCentralPubMedGoogle Scholar
  88. 88.
    Tusnády GE, Dosztányi Z, Simon I (2005) TMDET: web server for detecting transmembrane regions of proteins by using their 3D coordinates. Bioinformatics 21:1276–1277. https://doi.org/10.1093/bioinformatics/bti121 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Agnieszka A. Kaczor
    • 1
    • 2
  • Damian Bartuzi
    • 1
  • Tomasz Maciej Stępniewski
    • 3
  • Dariusz Matosiuk
    • 1
  • Jana Selent
    • 3
  1. 1.Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling LabMedical University of LublinLublinPoland
  2. 2.School of PharmacyUniversity of Eastern FinlandKuopioFinland
  3. 3.GPCR Drug Discovery GroupResearch Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Parc de Recerca Biomèdica de Barcelona (PRBB)BarcelonaSpain

Personalised recommendations