Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Transport domain unlocking sets the uptake rate of an aspartate transporter

Abstract

Glutamate transporters terminate neurotransmission by clearing synaptically released glutamate from the extracellular space, allowing repeated rounds of signalling and preventing glutamate-mediated excitotoxicity. Crystallographic studies of a glutamate transporter homologue from the archaeon Pyrococcus horikoshii, GltPh, showed that distinct transport domains translocate substrates into the cytoplasm by moving across the membrane within a central trimerization scaffold. Here we report direct observations of these ‘elevator-like’ transport domain motions in the context of reconstituted proteoliposomes and physiological ion gradients using single-molecule fluorescence resonance energy transfer (smFRET) imaging. We show that GltPh bearing two mutations introduced to impart characteristics of the human transporter exhibits markedly increased transport domain dynamics, which parallels an increased rate of substrate transport, thereby establishing a direct temporal relationship between transport domain motion and substrate uptake. Crystallographic and computational investigations corroborated these findings by revealing that the ‘humanizing’ mutations favour structurally ‘unlocked’ intermediate states in the transport cycle exhibiting increased solvent occupancy at the interface between the transport domain and the trimeric scaffold.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Transport rates and ‘elevator-like’ domain dynamics are correlated.
Figure 2: Ligand-dependent state distributions in detergent.
Figure 3: Coupled Na+ and aspartate binding to H276,395-GltPh.
Figure 4: Unimodal dynamic behaviour of H276,395-GltPh.
Figure 5: Crystal structure of the H276,395-GltPh.
Figure 6: Kinetic model of transport.

Similar content being viewed by others

Accession codes

Primary accessions

Protein Data Bank

Data deposits

The crystallographic model has been deposited in the Protein Data Bank under accession number 4X2S.

References

  1. Zerangue, N. & Kavanaugh, M. P. Flux coupling in a neuronal glutamate transporter. Nature 383, 634–637 (1996)

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Yernool, D., Boudker, O., Jin, Y. & Gouaux, E. Structure of a glutamate transporter homologue from Pyrococcus horikoshii. Nature 431, 811–818 (2004)

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Boudker, O., Ryan, R. M., Yernool, D., Shimamoto, K. & Gouaux, E. Coupling substrate and ion binding to extracellular gate of a sodium-dependent aspartate transporter. Nature 445, 387–393 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Reyes, N., Ginter, C. & Boudker, O. Transport mechanism of a bacterial homologue of glutamate transporters. Nature 462, 880–885 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Verdon, G. & Boudker, O. Crystal structure of an asymmetric trimer of a bacterial glutamate transporter homolog. Nature Struct. Mol. Biol. 19, 355–357 (2012)

    Article  CAS  Google Scholar 

  6. Verdon, G., Oh, S., Serio, R. N. & Boudker, O. Coupled ion binding and structural transitions along the transport cycle of glutamate transporters. ELife 3, e02283 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ryan, R. M., Compton, E. L. & Mindell, J. A. Functional characterization of a Na+-dependent aspartate transporter from Pyrococcus horikoshii. J. Biol. Chem. 284, 17540–17548 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Groeneveld, M. & Slotboom, D. J. Na+:aspartate coupling stoichiometry in the glutamate transporter homologue GltPh . Biochemistry 49, 3511–3513 (2010)

    Article  CAS  PubMed  Google Scholar 

  9. Akyuz, N., Altman, R., Blanchard, S. C. & Boudker, O. Transport dynamics of a glutamate transporter homologue. Nature 502, 114–118 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Erkens, G. B., Hanelt, I., Goudsmits, J. M., Slotboom, D. J. & van Oijen, A. M. Unsynchronised subunit motion in single trimeric sodium-coupled aspartate transporters. Nature 502, 119–123 (2013)

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Georgieva, E. R., Borbat, P. P., Ginter, C., Freed, J. H. & Boudker, O. Conformational ensemble of the sodium-coupled aspartate transporter. Nature Struct. Mol. Biol. 20, 215–221 (2013)

    Article  CAS  Google Scholar 

  12. Hänelt, I., Wunnicke, D., Bordignon, E., Steinhoff, H. J. & Slotboom, D. J. Conformational heterogeneity of the aspartate transporter GltPh . Nature Struct. Mol. Biol. 20, 210–214 (2013)

    Article  CAS  Google Scholar 

  13. Zhao, Y. et al. Single-molecule dynamics of gating in a neurotransmitter transporter homologue. Nature 465, 188–193 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Stolzenberg, S., Khelashvili, G. & Weinstein, H. Structural intermediates in a model of the substrate translocation path of the bacterial glutamate transporter homologue GltPh. J. Phys. Chem. B 116, 5372–5383 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ryan, R. M., Kortt, N. C., Sirivanta, T. & Vandenberg, R. J. The position of an arginine residue influences substrate affinity and K+ coupling in the human glutamate transporter, EAAT1. J. Neurochem. 114, 565–575 (2010)

    Article  CAS  PubMed  Google Scholar 

  16. Borre, L. & Kanner, B. I. Coupled, but not uncoupled, fluxes in a neuronal glutamate transporter can be activated by lithium ions. J. Biol. Chem. 276, 40396–40401 (2001)

    Article  CAS  PubMed  Google Scholar 

  17. Altman, R. B. et al. Enhanced photostability of cyanine fluorophores across the visible spectrum. Nature Methods 9, 428–429 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zheng, Q. et al. On the mechanisms of cyanine fluorophore photostabilization. J. Phys. Chem. Lett. 3, 2200–2203 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Alejo, J. L., Blanchard, S. C. & Andersen, O. S. Small-molecule photostabilizing agents are modifiers of lipid bilayer properties. Biophys. J. 104, 2410–2418 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Geggier, P. et al. Conformational sampling of aminoacyl-tRNA during selection on the bacterial ribosome. J. Mol. Biol. 399, 576–595 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Grewer, C. et al. Individual subunits of the glutamate transporter EAAC1 homotrimer function independently of each other. Biochemistry 44, 11913–11923 (2005)

    Article  CAS  PubMed  Google Scholar 

  22. Groeneveld, M. & Slotboom, D. J. Rigidity of the subunit interfaces of the trimeric glutamate transporter GltT during translocation. J. Mol. Biol. 372, 565–570 (2007)

    Article  CAS  PubMed  Google Scholar 

  23. Reyes, N., Oh, S. & Boudker, O. Binding thermodynamics of a glutamate transporter homolog. Nature Struct. Mol. Biol. 20, 634–640 (2013)

    Article  CAS  Google Scholar 

  24. Shimamoto, K. et al. DL-threo-β-benzyloxyaspartate, a potent blocker of excitatory amino acid transporters. Mol. Pharmacol. 53, 195–201 (1998)

    Article  CAS  PubMed  Google Scholar 

  25. Mondal, S., Khelashvili, G., Shi, L. & Weinstein, H. The cost of living in the membrane: a case study of hydrophobic mismatch for the multi-segment protein LeuT. Chem. Phys. Lipids 169, 27–38 (2013)

    Article  CAS  PubMed  Google Scholar 

  26. Mondal, S., Khelashvili, G. & Weinstein, H. Not just an oil slick: how the energetics of protein–membrane interactions impacts the function and organization of transmembrane proteins. Biophys. J. 106, 2305–2316 (2014)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. Jardetzky, O. Simple allosteric model for membrane pumps. Nature 211, 969–970 (1966)

    Article  ADS  CAS  PubMed  Google Scholar 

  28. Mitchell, P. Translocations through natural membranes. Adv. Enzymol. 29, 33–87 (1967)

    CAS  PubMed  Google Scholar 

  29. Lee, C. et al. A two-domain elevator mechanism for sodium/proton antiport. Nature 501, 573–577 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Quistgaard, E. M., Low, C., Moberg, P., Tresaugues, L. & Nordlund, P. Structural basis for substrate transport in the GLUT-homology family of monosaccharide transporters. Nature Struct. Mol. Biol. 20, 766–768 (2013)

    Article  CAS  Google Scholar 

  31. Zhou, X. et al. Structural basis of the alternating-access mechanism in a bile acid transporter. Nature 505, 569–573 (2014)

    Article  ADS  CAS  PubMed  Google Scholar 

  32. Zheng, Q. et al. Ultra-stable organic fluorophores for single-molecule research. Chem. Soc. Rev. 43, 1044–1056 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Munro, J. B., Altman, R. B., O’Connor, N. & Blanchard, S. C. Identification of two distinct hybrid state intermediates on the ribosome. Mol. Cell 25, 505–517 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Dave, R., Terry, D. S., Munro, J. B. & Blanchard, S. C. Mitigating unwanted photophysical processes for improved single-molecule fluorescence imaging. Biophys. J. 96, 2371–2381 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Qin, F. Restoration of single-channel currents using the segmental k-means method based on hidden Markov modeling. Biophys. J. 86, 1488–1501 (2004)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997)

    Article  CAS  PubMed  Google Scholar 

  37. Collaborative Computational Project, Number 4. The CCP4 suite: programs for protein crystallography. Acta Crystallogr. D 50, 760–763 (1994)

  38. McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004)

    Article  CAS  PubMed  Google Scholar 

  40. Murshudov, G. N. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr. D 67, 355–367 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. The PyMOL Molecular Graphics System v. 1.5.0.4 (Schrödinger LLC).

  42. Borbat, P. P., Crepeau, R. H. & Freed, J. H. Multifrequency two-dimensional Fourier transform ESR: an X/Ku-band spectrometer. J. Magn. Reson. 127, 155–167 (1997)

    Article  ADS  CAS  PubMed  Google Scholar 

  43. Chiang, Y. W., Borbat, P. P. & Freed, J. H. The determination of pair distance distributions by pulsed ESR using Tikhonov regularization. J. Magn. Reson. 172, 279–295 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  44. Chiang, Y. W., Borbat, P. P. & Freed, J. H. Maximum entropy: a complement to Tikhonov regularization for determination of pair distance distributions by pulsed ESR. J. Magn. Reson. 177, 184–196 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  45. MacKerell, A. D., Jr, Feig, M. & Brooks, C. L., III Improved treatment of the protein backbone in empirical force fields. J. Am. Chem. Soc. 126, 698–699 (2004)

    Article  CAS  PubMed  Google Scholar 

  46. Klauda, J. B. et al. Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114, 7830–7843 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Schmid, N. et al. Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur. Biophys. J. 40, 843–856 (2011)

    Article  CAS  PubMed  Google Scholar 

  49. Schmidt, T. H. & Kandt, C. LAMBADA and InflateGRO2: Efficient membrane alignment and insertion of membrane proteins for molecular dynamics simulations. J. Chem. Inf. Model. 52, 2657–2669 (2012)

    Article  CAS  PubMed  Google Scholar 

  50. Pronk, S. et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845–854 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008)

    Article  CAS  PubMed  Google Scholar 

  52. Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Pettersen, E. F. et al. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004)

    Article  CAS  PubMed  Google Scholar 

  54. Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014)

    Article  CAS  PubMed  Google Scholar 

  55. Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank P. Borbat for help with DEER data collection; H. Zhao for his help with fluorophore synthesis; G. Verdon, G. G. Gregario and S. Oh for discussions. The authors acknowledge the staff of X29 beamline at National Synchrotron Light Source and the computational resources at the Texas Advanced Computing Center at the University of Texas at Austin and the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231. The work was supported in part by the National Institute of Health grants 5U54GM087519 (to O.B. and H.W.); P01DA012408 (to H.W.); R01GM098859 and R21MH099491 (to S.C.B.); P41GM103521 and R010EB003150 (to J.H.F.).

Author information

Authors and Affiliations

Authors

Contributions

N.A., O.B. and S.C.B. designed the study. N.A. designed and performed the majority of the experiments and simulated smFRET data. N.A. analysed the smFRET data, with support from D.S.T. and S.C.B. N.A. and O.B. analysed crystallographic data. E.R.G. performed and analysed DEER experiments and E.R.G. and J.H.F. interpreted the data. Z.Z. synthesized the 4S(COT)-maleimide cyanine dyes. D.S.T. made improvements to the smFRET instrumentation and analysis software. R.B.A. prepared reagents for smFRET experiments. H.W. and M.A.C. designed, and S.S., G.K., and M.A.C. carried out the molecular dynamics simulations. N.A., O.B. and S.C.B., H.W. and M.A.C. interpreted results and wrote the manuscript.

Corresponding authors

Correspondence to Olga Boudker or Scott C. Blanchard.

Ethics declarations

Competing interests

The authors S.C.B. and R.B.A. have equity interest in Lumidyne Technologies.

Extended data figures and tables

Extended Data Figure 1 Elevator model of transport and spatial conservation of a positively charged residue in glutamate transporter family.

a, GltPh protomers in the outward- (left) and inward-facing (right) conformation are shown in surface representation and viewed in membrane plane. Dashed lines represent an approximate position of the membrane hydrocarbon layer. In the inward-facing state, the transport domain (blue) is moved by 15 Å across the bilayer relative to the trimerization domain (beige). b, Schematic representation of dynamic mode-switching between stable and transient conformations. c, A single GltPh protomer is shown in cartoon representation. Cyan balls emphasize the amino acid positions at which potentially positively charged residues occur in glutamate transporter homologues. d, Occurrence frequencies of these residues at the marked positions (GltPh numbering). To obtain the frequencies, sequences were harvested from the PFAM database54 (accession code PF00375). Sequences were parsed to exclude those with over 70% identity and aligned using Clustal Omega55.

Extended Data Figure 2 Assignment of FRET efficiency states.

a, Shown are the crystal structures of GltPh trimers in symmetrical outward (OF)- and inward (IF)-facing states and a model of an asymmetric configuration with two outward- and one inward-facing protomers2,4. The structures are shown in surface representation and coloured as in Extended Data Fig. 1. Black lines connect Cα atoms of residue 378, and the corresponding distances are indicated above the structures. b, Expected FRET efficiency levels for these distances for all possible configurations of subunit pairs: outward/outward (OF/OF), outward/inward (OF/IF), inward/outward (IF/OF) and inward/inward (IF/IF)4. c, Intramolecularly stabilized 4S(COT)-maleimide Cy3 (n = 1) and Cy5 (n = 2) fluorophores used in this study synthesized as described previously17,18 with the addition of two sulfonate groups for increased solubility.

Extended Data Figure 3 Conformational state distributions of wild-type and H276,395-GltPh in proteoliposomes.

a, Examples of smFRET recordings. Top panels show raw fluorescent signals originating from donor (green) and acceptor (red) dyes. Bottom panels show changes of FRET efficiency calculated from raw data (blue). Red solid lines through the data are idealizations obtained using QuB software35. b, Contour plots and one-dimensional population histograms in the absence and presence of Na+ and aspartate in the external liposome buffers. Buffer compositions inside and outside of the vesicles are shown above the panels. Wild-type and H276,395-GltPh histograms are fitted to three and two Gaussian functions, respectively. c, Transitions density (TD) plots for the wild type (left) and H276,395-GltPh (right) in proteoliposomes in the absence of Na+ and aspartate in the external buffer. d, Means and widths (in brackets) of FRET efficiency distributions derived from Gaussian fits to proteoliposome data in comparison to detergent data.

Extended Data Figure 4 Single-molecule dynamics using different liposome-attachment strategies and with higher time-resolution.

ad, Dynamic properties of H276,395-GltPh under transport conditions using a different surface-immobilization strategy and in the presence of electrical potential. a, Surface-immobilization strategy for proteoliposomes using His-tagged lipids. b, Transition frequencies for wild-type (top) and H276,395-GltPh (bottom) trimers reconstituted into his-tagged liposomes that were site-specifically labelled in just two protomers with intramolecularly photostabilized Cy3 and Cy5 fluorophores. c, A negative inside voltage potential was established in proteoliposomes by adding valinomycin to the uptake buffer. d, Transition frequencies for wild-type (top) and H276,395-GltPh (bottom) in the presence of valinomycin. Each experiment shown includes statistics based on >250 individual molecules. The standard error in transition frequency measurements is approximately 0.015 s−1. e, f, Dynamic properties of H276,395-GltPh probed at 15 ms time resolution. Contour plots and one-dimensional population FRET efficiency histograms (e) observed for the humanized mutant in detergent solution in the absence (left) and presence (right) of 100 mM NaCl and 100 µM aspartate. Examples of single-molecule trajectories are shown in f.

Extended Data Figure 5 Population changes in response to ligand binding.

a, b, TBOA binding to H276,395-GltPh measured in smFRET experiments. Contour plots and population FRET efficiency histograms in the presence of increasing concentrations of TBOA (a). Changes in low- (red) and high- (blue) FRET state populations as a function of TBOA concentration (b). Solid lines through the data correspond to the Hill equation y = ymin + (ymax − ymin)(xn/(xn + Kdn)) with Kd = 2.4 mM and n = 1. The data points shown are averages and standard errors from three independent biological replicates. c, Experimental time domain DEER data (left) and reconstructed distance distributions (right) for H276,395-GltPh (shown in colours) and wild-type transporter (black) spin-labelled on residue Cys378 in detergent solution. The data were collected in the absence of ligands (top), in the presence of 100 mM Na+ and 350 µM aspartate (middle) and in the presence of 100 mM Na+ and 480 µM TBOA (bottom). The red arrows above the distance distributions mark distances between residues 378 extracted from crystal structures of the symmetric outward- (OF/OF) and inward- (IF/IF) facing states. The data for the wild-type transporter were adapted from a published study11. The data show that in the apo transporter, outward- and inward-facing states are similarly populated. Binding of Na+ ions and aspartate favours the inward-facing state, whereas binding of TBOA favours the outward-facing state.

Extended Data Figure 6 Aspartate binding experiments.

a, FRET efficiency population contour plots determined for H276,395-GltPh in detergent micelles in the presence of 100 µM aspartate and increasing concentrations of Na+ ions (indicated above the panels). b, c, Representative aspartate binding isotherms derived from ITC experiments for the wild-type GltPh (b) and H276,395-GltPh (c) in the presence of 10 mM Na+ and 100 mM Na+, respectively. The binding of aspartate to H276,395-GltPh in the presence of 10 mM Na+ is too weak to measure (inset). Binding experiments were performed using small-volume Nano ITC (TA Instruments). Upper panels show raw data. The cell contained 30 μM (WT-GltPh) and 40 μM (H276,395-GltPh) protein buffer containing 20 mM HEPES/Tris, pH 7.4 and 0.1 mM DDM and indicated concentrations of NaCl. The syringe contained Asp at 200 μM concentration in the same buffer; every injection contained 5 μl. Data were processed and analysed using manufacturer’s software (lower panels). Solid lines through the data are fits to independent binding sites model with the following Kd, enthalpy (ΔH), and apparent number of binding sites (n): 380 nM, 15 kcal per mol and 0.65 for the wild-type transporter, and 285 nM, 16 kcal per mol and 0.68 for H276,395-GltPh.

Extended Data Figure 7 Data collection and refinement for Na+ and aspartate bound H276,395-GltPh.

a, Table showing data collection and refinement statistics. Scaling and refinement statistics were obtained after anisotropy correction by ellipsoidal truncation using high-resolution cutoffs of 4.9 Å along the a and b axis, and of 4.2 Å along the c axis. b, Stereoview of the 2FoFc electron density map for H276,395-GltPh contoured at 1.5 σ around residue Arg 395 in unlocked protomer C. Protein backbone (maroon) is shown in cartoon representations and side chains are shown as lines and colored by atom type. c, Superimposed scaffold domains of the inward-facing wild type and H276,395-GltPh are shown in cartoon representation. The labile portions are coloured cyan (wild type) and magenta (mutant). Helices bend at conserved Pro 60 and Pro 206 residues (spheres). d, Locked (left) and unlocked (right) mutant protomers viewed from the cytoplasm and shown in surface representation.

Extended Data Figure 8 Arg395 adapts to its environment.

a, The arginine side chain (Arg 276 in the wild type; Arg 395 in H276,395-GltPh) is seen in molecular dynamics simulations to engage in hydrogen-bonding interactions. The extent of the hydrogen bonds formation is shown as a function of simulation time in Charmm Trajectory 3 (see Extended Data Fig. 10). The main interactions of the arginine in both mutant and wild type are with water molecules, but the locations of the waters are very different. In H276,395-GltPh, the Arg 395 side chain is located 5 to 9 Å below the level of the membrane surface, so that the water molecules are those penetrating the membrane–protein interface due to remodelling of the membrane. In the wild type, the water molecules interacting with Arg 276 are in the space created inside the protein. b, The minimum distance from wild-type Met395 (top) or mutant Arg 395 (bottom) side chains to any lipid phosphate group (left) or any water molecule (right) in Charmm Trajectory 3. In H276,395-GltPh, after the initial equilibration phase, lipid phosphate groups interact with Arg 395 either directly (5 Å distance) or through water (7.5 Å distance). In the wild type, lipid head groups remain far from the hydrophobic Met 395 side chain. Water interacts constantly with Arg 395, but only occasionally with Met 395 (in protomer B, a water molecule approaches Met 395 from the inside of the protein, at the interface between transport and trimerization domains). c, The same set of distances as in b for the mutant, from a different trajectory (G54a7 Trajectory 2) obtained independently, using a different force field. The same trends are observed as in b, showing proximity to the polar environment. d, Membrane bending (blue indicates thinning, red indicates thickening) close to Arg 395 (green) which exposes its side chain to a polar environment comprised of water molecules and lipid head groups. e, Root mean square deviation (r.m.s.d.) of the Arg 395 side chain with respect to the crystal structure after alignment on the trimerization domain, calculated from Charmm Trajectory 3 and G54a7 Trajectory 2. The side chain initially samples different conformations before settling into the membrane-exposed position shown in panel d.

Extended Data Figure 9 Lipids or detergent molecules stabilize the unlocked conformation of H276,395-GltPh.

ae, Centre-of-mass distance between the transport and scaffold domains of protomers A, B, and C of H276,395-GltPh as a function of molecular dynamics simulation time. The data are from five independent simulations initiated with position restraints on the Cα atoms (later released at different time points) and with the domain interface solvated with water. The vertical green lines indicate the moment in the corresponding trajectory when position restraints were turned off. Panels a and b show two repeats of the same starting structure simulated with the Charmm force field45 and panel c with Gromos force field48. The transport domains in protomers B and C collapse onto the trimerization domain rapidly and lose their ligands in some cases (red arrows). d, A simulation, in which lipid tails partially insert into the interface spontaneously; the unlocked structure is stable much longer (note the different time scales on the time axis), and the collapse is only partial. e, The trajectory of a NAMD simulation (Charmm force field) in which lipid molecules were docked into the interface of protomers B and C at the time marked by the red arrow (3 lipids per protomer). The lipids remained in the docked region for the entire duration of the simulation and stabilized the position of the transport domain. f, g, The best scored docking poses for a detergent molecule and a POPC lipid, respectively, docked at the interface of protomer C.

Extended Data Figure 10 Simulated smFRET data recapitulate experimental observations.

ad, Simulated FRET efficiency population contour plots (left side of each panel) and cumulative population histograms (right side) for wild-type GltPh (a) and H276,395-GltPh (b), and the corresponding transition density plots (c and d), (see Fig. 2 for corresponding experimental data). As noted before9, there are fewer transitions observed between the low- and high-FRET states in the wild-type transporter than would be expected from the model. This may either be because the model does not recapitulate the noise correctly or it may reflect previously uncharacterized communication between the protomers that warrants further investigation. e, f, Dwell time distributions for the low- (left panel) and intermediate- and high-FRET states (right panels) obtained for wild-type GltPh (e) and H276,395-GltPh (f) (see Fig. 4 for corresponding experimental data).

Extended Data Table 1 FRET state assignments and populations; time constants for the slow and fast components

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akyuz, N., Georgieva, E., Zhou, Z. et al. Transport domain unlocking sets the uptake rate of an aspartate transporter. Nature 518, 68–73 (2015). https://doi.org/10.1038/nature14158

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature14158

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing