Abstract
Neurotransmitter:sodium symporters are located on presynaptic neurons and terminate neurotransmission by removing the monoamine substrates from the synaptic cleft. Until very recently, only several conformational snapshots/structures of a bacterial homolog of neurotransmitter:sodium symporters, namely, the leucine/alanine transporter LeuT from Aquifex aeolicus, were available. However, this transporter shares only 21b % overall sequence identity with its human homologs. In this chapter, we describe how a model can be developed from a template with such low identity. The effort of model building will strongly depend on the purpose. We discuss this process and focus on the important steps that allowed us to obtain a model which can be used for molecular dynamics simulations. Furthermore, we also highlight the inherent limitations of the proposed approaches. Prediction of ligand binding brings in additional complexity. Therefore, experimental scrutiny of the resulting models is a key component to successful validation. We describe two specific examples: model building of the dopamine transporter and ligand docking to the serotonin transporter. We evaluate our modeling approach by direct comparison of our models to the recently published first eukaryotic neurotransmitter:sodium symporter, the drosophila melanogaster DAT transporter.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersen J, Taboureau O, Hansen KB et al (2009) Location of the antidepressant binding site in the serotonin transporter: importance of Ser-438 in recognition of citalopram and tricyclic antidepressants. J Biol Chem 284:10276–10284. doi:10.1074/jbc.M806907200
Aurora R, Rose GD (1998) Helix capping. Protein Sci 7:21–38. doi:10.1002/pro.5560070103
Aurora R, Srinivasan R, Rose GD (1994) Rules for alpha-helix termination by glycine. Science 264:1126–1130
Barker EL, Moore KR, Rakhshan F, Blakely RD (1999) Transmembrane domain I contributes to the permeation pathway for serotonin and ions in the serotonin transporter. J Neurosci 19:4705–4717
Benkert P, Tosatto SCE, Schomburg D (2008) QMEAN: a comprehensive scoring function for model quality assessment. Proteins 71:261–277. doi:10.1002/prot.21715
Bennett-Lovsey RM, Herbert AD, Sternberg MJE, Kelley LA (2008) Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre. Proteins 70:611–625. doi:10.1002/prot.21688
Beuming T, Shi L, Javitch JA, Weinstein H (2006) A comprehensive structure-based alignment of prokaryotic and eukaryotic neurotransmitter/Na+ symporters (NSS) aids in the use of the LeuT structure to probe NSS structure and function. Mol Pharmacol 70:1630–1642. doi:10.1124/mol.106.026120
Beuming T, Kniazeff J, Bergmann ML et al (2008) The binding sites for cocaine and dopamine in the dopamine transporter overlap. Nat Neurosci 11:780–789. doi:10.1038/nn.2146
Bowman GR, Voelz VA, Pande VS (2011) Atomistic folding simulations of the five-helix bundle protein λ(6−85). J Am Chem Soc 133:664–667. doi:10.1021/ja106936n
Brenner SE, Chothia C, Hubbard TJ (1997) Population statistics of protein structures: lessons from structural classifications. Curr Opin Struct Biol 7:369–376
Bulling S, Schicker K, Zhang Y-W et al (2012) The mechanistic basis for noncompetitive ibogaine inhibition of serotonin and dopamine transporters. J Biol Chem 287:18524–18534. doi:10.1074/jbc.M112.343681
Cantor RS (1997) The lateral pressure profile in membranes: a physical mechanism of general anesthesia. Biochemistry 36:2339–2344. doi:10.1021/bi9627323
Canutescu AA, Shelenkov AA, Dunbrack RL (2003) A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci 12:2001–2014. doi:10.1110/ps.03154503
Celik L, Sinning S, Severinsen K et al (2008) Binding of serotonin to the human serotonin transporter. Molecular modeling and experimental validation. J Am Chem Soc 130:3853–3865. doi:10.1021/ja076403h
Chamberlain AK, Lee Y, Kim S, Bowie JU (2004) Snorkeling preferences foster an amino acid composition bias in transmembrane helices. J Mol Biol 339:471–479. doi:10.1016/j.jmb.2004.03.072
Cherezov V, Rosenbaum DM, Hanson MA et al (2007) High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor. Science 318:1258–1265. doi:10.1126/science.1150577
Chien EYT, Liu W, Zhao Q et al (2010) Structure of the human dopamine D3 receptor in complex with a D2/D3 selective antagonist. Science 330:1091–1095. doi:10.1126/science.1197410
Choi Y, Deane CM (2010) FREAD revisited: accurate loop structure prediction using a database search algorithm. Proteins 78:1431–1440. doi:10.1002/prot.22658
Dalton JAR, Jackson RM (2010) Homology-modelling protein–ligand interactions: allowing for ligand-induced conformational change. J Mol Biol 399:645–661. doi:10.1016/j.jmb.2010.04.047
Davis IW, Leaver-Fay A, Chen VB et al (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35:W375–W383. doi:10.1093/nar/gkm216
Deane CM, Blundell TL (2001) CODA: a combined algorithm for predicting the structurally variable regions of protein models. Protein Sci 10:599–612. doi:10.1110/ps.37601
Duan Y, Kollman PA (1998) Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 282:740–744
Eisenberg D, Lüthy R, Bowie JU (1997) VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 277:396–404
Evers A, Gohlke H, Klebe G (2003) Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials. J Mol Biol 334:327–345. doi:10.1016/j.jmb.2003.09.032
Faure G, Bornot A, de Brevern AG (2008) Protein contacts, inter-residue interactions and side-chain modelling. Biochimie 90:626–639. doi:10.1016/j.biochi.2007.11.007
Fernandez-Fuentes N, Madrid-Aliste CJ, Rai BK et al (2007) M4T: a comparative protein structure modeling server. Nucleic Acids Res 35:W363–W368. doi:10.1093/nar/gkm341
Forrest LR, Tang CL, Honig B (2006) On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins. Biophys J 91:508–517. doi:10.1529/biophysj.106.082313
Freddolino PL, Schulten K (2009) Common structural transitions in explicit-solvent simulations of villin headpiece folding. Biophys J 97:2338–2347. doi:10.1016/j.bpj.2009.08.012
Friesner RA, Abel R, Goldfeld DA et al (2013) Computational methods for high resolution prediction and refinement of protein structures. Curr Opin Struct Biol 23:177–184. doi:10.1016/j.sbi.2013.01.010
Gedeon PC, Indarte M, Surratt CK, Madura JD (2010) Molecular dynamics of leucine and dopamine transporter proteins in a model cell membrane lipid bilayer. Proteins 78:797–811. doi:10.1002/prot.22601
Haga K, Kruse AC, Asada H et al (2012) Structure of the human M2 muscarinic acetylcholine receptor bound to an antagonist. Nature 482:547–551. doi:10.1038/nature10753
Huang X, Zhan C-G (2007) How dopamine transporter interacts with dopamine: insights from molecular modeling and simulation. Biophys J 93:3627–3639. doi:10.1529/biophysj.107.110924
Huang ES, Koehl P, Levitt M et al (1998) Accuracy of side-chain prediction upon near-native protein backbones generated by Ab initio folding methods. Proteins 33:204–217
Indarte M, Madura JD, Surratt CK (2008) Dopamine transporter comparative molecular modeling and binding site prediction using the LeuT(Aa) leucine transporter as a template. Proteins 70:1033–1046. doi:10.1002/prot.21598
Jaakola V-P, Griffith MT, Hanson MA et al (2008) The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322:1211–1217. doi:10.1126/science.1164772
Jacobson MP, Pincus DL, Rapp CS et al (2004) A hierarchical approach to all-atom protein loop prediction. Proteins 55:351–367. doi:10.1002/prot.10613
Jaroszewski L (2009) Protein structure prediction based on sequence similarity. Methods Mol Biol 569:129–156. doi:10.1007/978-1-59745-524-4_7
Jerabek H, Pabst G, Rappolt M, Stockner T (2010) Membrane-mediated effect on ion channels induced by the anesthetic drug ketamine. J Am Chem Soc 132:7990–7997. doi:10.1021/ja910843d
Kardos J, Palló A, Bencsura A, Simon A (2010) Assessing structure, function and druggability of major inhibitory neurotransmitter gamma-aminobutyrate symporter subtypes. Curr Med Chem 17:2203–2213
Kelm S, Shi J, Deane CM (2010) MEDELLER: homology-based coordinate generation for membrane proteins. Bioinformatics 26:2833–2840. doi:10.1093/bioinformatics/btq554
Kinch L, Yong Shi S, Cong Q et al (2011) CASP9 assessment of free modeling target predictions. Proteins 79(Suppl 1):59–73. doi:10.1002/prot.23181
Klepsch F, Chiba P, Ecker GF (2011) Exhaustive sampling of docking poses reveals binding hypotheses for propafenone type inhibitors of P-glycoprotein. PLoS Comput Biol 7(5):e1002036. doi:10.1371/journal.pcbi.1002036
Koldsø H, Christiansen AB, Sinning S, Schiøtt B (2013) Comparative modeling of the human monoamine transporters: similarities in substrate binding. ACS Chem Neurosci 4:295–309. doi:10.1021/cn300148r
Kristensen AS, Andersen J, Jørgensen TN et al (2011) SLC6 neurotransmitter transporters: structure, function, and regulation. Pharmacol Rev 63:585–640. doi:10.1124/pr.108.000869
Krivov GG, Shapovalov MV, Dunbrack RL (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77:778–795. doi:10.1002/prot.22488
Kryshtafovych A, Fidelis K (2009) Protein structure prediction and model quality assessment. Drug Discov Today 14:386–393. doi:10.1016/j.drudis.2008.11.010
Kryshtafovych A, Fidelis K, Moult J (2011a) CASP9 results compared to those of previous CASP experiments. Proteins 79(Suppl 1):196–207. doi:10.1002/prot.23182
Kryshtafovych A, Fidelis K, Tramontano A (2011b) Evaluation of model quality predictions in CASP9. Proteins 79(Suppl 1):91–106. doi:10.1002/prot.23180
Lane TJ, Shukla D, Beauchamp KA, Pande VS (2013) To milliseconds and beyond: challenges in the simulation of protein folding. Curr Opin Struct Biol 23:58–65. doi:10.1016/j.sbi.2012.11.002
Law RJ, Capener C, Baaden M et al (2005) Membrane protein structure quality in molecular dynamics simulation. J Mol Graph Model 24:157–165. doi:10.1016/j.jmgm.2005.05.006
Levitt M (1992) Accurate modeling of protein conformation by automatic segment matching. J Mol Biol 226:507–533
Liang S, Grishin NV (2002) Side-chain modeling with an optimized scoring function. Protein Sci 11:322–331. doi:10.1110/ps.24902
Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) How fast-folding proteins fold. Science 334:517–520. doi:10.1126/science.1208351
Liu X, Fan K, Wang W (2004) The number of protein folds and their distribution over families in nature. Proteins 54:491–499. doi:10.1002/prot.10514
Liu T, Geng X, Zheng X et al (2012) Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles. Amino Acids 42:2243–2249. doi:10.1007/s00726-011-0964-5
Loland CJ, Norregaard L, Gether U (1999) Defining proximity relationships in the tertiary structure of the dopamine transporter. J Biol Chem 274:36928–36934
Lundström J, Rychlewski L, Bujnicki J, Elofsson A (2001) Pcons: a neural-network-based consensus predictor that improves fold recognition. Protein Sci 10:2354–2362
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. doi:10.1085/jgp.200709745
MacCallum JL, Pérez A, Schnieders MJ et al (2011) Assessment of protein structure refinement in CASP9. Proteins 79(Suppl 1):74–90. doi:10.1002/prot.23131
Makino CL, Riley CK, Looney J et al (2010) Binding of more than one retinoid to visual opsins. Biophys J 99:2366–2373. doi:10.1016/j.bpj.2010.08.003
Mariani V, Kiefer F, Schmidt T et al (2011) Assessment of template based protein structure predictions in CASP9. Proteins 79(Suppl 1):37–58. doi:10.1002/prot.23177
Martí-Renom MA, Stuart AC, Fiser A et al (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325. doi:10.1146/annurev.biophys.29.1.291
McGuffin LJ (2007) Benchmarking consensus model quality assessment for protein fold recognition. BMC Bioinforma 8:345. doi:10.1186/1471-2105-8-345
Moretti S, Armougom F, Wallace IM et al (2007) The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods. Nucleic Acids Res 35:W645–W648. doi:10.1093/nar/gkm333
Moult J (2005) A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr Opin Struct Biol 15:285–289. doi:10.1016/j.sbi.2005.05.011
Noé F, Schütte C, Vanden-Eijnden E et al (2009) Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations. Proc Natl Acad Sci U S A 106:19011–19016. doi:10.1073/pnas.0905466106
Norregaard L, Frederiksen D, Nielsen EO, Gether U (1998) Delineation of an endogenous zinc-binding site in the human dopamine transporter. EMBO J 17:4266–4273. doi:10.1093/emboj/17.15.4266
Nugent T, Jones DT (2012) Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis. Proc Natl Acad Sci U S A 109:E1540–E1547. doi:10.1073/pnas.1120036109
Paczkowski FA, Sharpe IA, Dutertre S, Lewis RJ (2007) χ-Conotoxin and tricyclic antidepressant interactions at the norepinephrine transporter define a new transporter model. J Biol Chem 282:17837–17844. doi:10.1074/jbc.M610813200
Palló A, Bencsura A, Héja L et al (2007) Major human gamma-aminobutyrate transporter: in silico prediction of substrate efficacy. Biochem Biophys Res Commun 364:952–958. doi:10.1016/j.bbrc.2007.10.108
Payandeh J, Scheuer T, Zheng N, Catterall WA (2011) The crystal structure of a voltage-gated sodium channel. Nature 475:353–358. doi:10.1038/nature10238
Peng H-P, Yang A-S (2007) Modeling protein loops with knowledge-based prediction of sequence-structure alignment. Bioinformatics 23:2836–2842. doi:10.1093/bioinformatics/btm456
Penmatsa A, Wang KH, Gouaux E (2013) X-ray structure of dopamine transporter elucidates antidepressant mechanism. Nature 503(7474):85–90. doi:10.1038/nature12533
Perozo E, Kloda A, Cortes DM, Martinac B (2002) Physical principles underlying the transduction of bilayer deformation forces during mechanosensitive channel gating. Nat Struct Biol 9:696–703. doi:10.1038/nsb827
Punta M, Forrest LR, Bigelow H et al (2007) Membrane protein prediction methods. Methods 41:460–474. doi:10.1016/j.ymeth.2006.07.026
Raval A, Piana S, Eastwood MP et al (2012) Refinement of protein structure homology models via long, all-atom molecular dynamics simulations. Proteins 80:2071–2079. doi:10.1002/prot.24098
Rohl CA, Strauss CE, Misura KM, Baker D (2004) Protein structure prediction using Rosetta. Methods Enzymol 383:66–93
Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815. doi:10.1006/jmbi.1993.1626
Samuli Ollila OH, Louhivuori M, Marrink SJ, Vattulainen I (2011) Protein shape change has a major effect on the gating energy of a mechanosensitive channel. Biophys J 100:1651–1659. doi:10.1016/j.bpj.2011.02.027
Sarker S, Weissensteiner R, Steiner I et al (2010) The high-affinity binding site for tricyclic antidepressants resides in the outer vestibule of the serotonin transporter. Mol Pharmacol 78:1026–1035. doi:10.1124/mol.110.067538
Schellman C (1980) The alpha-L conformation at the ends of helices. In: Jaenicke R (ed) Protein fold. Elsevier, New York, NY, pp 53–56
Schlessinger A, Geier E, Fan H et al (2011) Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET. Proc Natl Acad Sci U S A 108:15810–15815. doi:10.1073/pnas.1106030108
Schlessinger A, Wittwer MB, Dahlin A et al (2012) High selectivity of the γ-aminobutyric acid transporter 2 (GAT-2, SLC6A13) revealed by structure-based approach. J Biol Chem 287:37745–37756. doi:10.1074/jbc.M112.388157
Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31:3381–3385
Shafrir Y, Durell SR, Guy HR (2008) Models of the structure and gating mechanisms of the pore domain of the NaChBac ion channel. Biophys J 95:3650–3662. doi:10.1529/biophysj.108.135327
Shen M-Y, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524. doi:10.1110/ps.062416606
Shirts M, Pande VS (2000) COMPUTING: screen savers of the world unite! Science 290:1903–1904. doi:10.1126/science.290.5498.1903
Singh SK, Piscitelli CL, Yamashita A, Gouaux E (2008) A competitive inhibitor traps LeuT in an open-to-out conformation. Science 322:1655–1661. doi:10.1126/science.1166777
Sippl MJ (1995) Knowledge-based potentials for proteins. Curr Opin Struct Biol 5:229–235
Skovstrup S, Taboureau O, Bräuner-Osborne H, Jørgensen FS (2010) Homology modelling of the GABA transporter and analysis of tiagabine binding. ChemMedChem 5:986–1000. doi:10.1002/cmdc.201000100
Söding J (2005) Protein homology detection by HMM–HMM comparison. Bioinformatics 21:951–960. doi:10.1093/bioinformatics/bti125
Stockner T, Montgomery TR, Kudlacek O et al (2013) Mutational analysis of the high-affinity zinc binding site validates a refined human dopamine transporter homology model. PLoS Comput Biol 9:e1002909. doi:10.1371/journal.pcbi.1002909
Tieleman DP, Marrink SJ, Berendsen HJ (1997) A computer perspective of membranes: molecular dynamics studies of lipid bilayer systems. Biochim Biophys Acta 1331:235–270
Tosatto SCE, Bindewald E, Hesser J, Männer R (2002) A divide and conquer approach to fast loop modeling. Protein Eng 15:279–286
Uhl G (2003) The top 20 dopamine transporter mutants: structure–function relationships and cocaine actions. Eur J Pharmacol 479:71–82. doi:10.1016/j.ejphar.2003.08.058
Voelz VA, Bowman GR, Beauchamp K, Pande VS (2010) Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39). J Am Chem Soc 132:1526–1528. doi:10.1021/ja9090353
Wallner B, Elofsson A (2003) Can correct protein models be identified? Protein Sci 12:1073–1086. doi:10.1110/ps.0236803.a
Wallner B, Elofsson A (2005) All are not equal: a benchmark of different homology modeling programs. Protein Sci 14:1315–1327. doi:10.1110/ps.041253405
Warne T, Moukhametzianov R, Baker JG et al (2011) The structural basis for agonist and partial agonist action on a β(1)-adrenergic receptor. Nature 469:241–244. doi:10.1038/nature09746
Warren GL, Andrews CW, Capelli A-M et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931. doi:10.1021/jm050362n
Wein T, Wanner KT (2010) Generation of a 3D model for human GABA transporter hGAT-1 using molecular modeling and investigation of the binding of GABA. J Mol Model 16:155–161. doi:10.1007/s00894-009-0520-3
Wu B, Chien EYT, Mol CD et al (2010) Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists. Science 330:1066–1071. doi:10.1126/science.1194396
Xhaard H, Backström V, Denessiouk K, Johnson MS (2008) Coordination of Na+ by monoamine ligands in dopamine, norepinephrine, and serotonin transporters. J Chem Inf Model 48:1423–1437. doi:10.1021/ci700255d
Xiang Z (2006) Advances in homology protein structure modeling. Curr Protein Pept Sci 7:217–227
Xiang Z, Steinbach PJ, Jacobson MP et al (2007) Prediction of side-chain conformations on protein surfaces. Proteins 66:814–823. doi:10.1002/prot.21099
Yamashita A, Singh SK, Kawate T et al (2005) Crystal structure of a bacterial homologue of Na+/Cl–-dependent neurotransmitter transporters. Nature 437:215–223. doi:10.1038/nature03978
Yarov-Yarovoy V, Schonbrun J, Baker D (2006) Multipass membrane protein structure prediction using Rosetta. Proteins 62:1010–1025. doi:10.1002/prot.20817
Zhang Y, Arakaki AK, Skolnick J (2005) TASSER: an automated method for the prediction of protein tertiary structures in CASP6. Proteins 61(Suppl 7):91–98. doi:10.1002/prot.20724
Acknowledgments
The financial support from the Austrian Science Fund (FWF) project “Transmembrane Transporters in Health and Disease” (SFB F35) and the DK + project “Ion Channels and Transporters as Molecular Drug Targets” is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Stockner, T., Jurik, A., Weissensteiner, R., Freissmuth, M., Ecker, G.F., Sitte, H.H. (2014). Development of Refined Homology Models: Adding the Missing Information to the Medically Relevant Neurotransmitter Transporters. In: Krämer, R., Ziegler, C. (eds) Membrane Transport Mechanism. Springer Series in Biophysics, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53839-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-53839-1_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53838-4
Online ISBN: 978-3-642-53839-1
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)