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An embedded lipid in the multidrug transporter LmrP suggests a mechanism for polyspecificity

Nature Structural & Molecular Biology volume 27, pages 829–835 (2020)Cite this article

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Abstract

Multidrug efflux pumps present a challenge to the treatment of bacterial infections, making it vitally important to understand their mechanism of action. Here, we investigate the nature of substrate binding within Lactococcus lactis LmrP, a prototypical multidrug transporter of the major facilitator superfamily. We determined the crystal structure of LmrP in a ligand-bound outward-open state and observed an embedded lipid in the binding cavity of LmrP, an observation supported by native mass spectrometry analyses. Molecular dynamics simulations suggest that the anionic lipid stabilizes the observed ligand-bound structure. Mutants engineered to disrupt binding of the embedded lipid display reduced transport of some, but not all, antibiotic substrates. Our results suggest that a lipid within the binding cavity could provide a malleable hydrophobic component that allows adaptation to the presence of different substrates, helping to explain the broad specificity of this protein and possibly other multidrug transporters.

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Fig. 1: DEER spectroscopy shows limited conformational change upon substrate binding to LmrP.
Fig. 2: The structure of LmrP in complex with Hoechst 33342.
Fig. 3: A lipid within the binding cavity of LmrP stabilizes the observed structure.
Fig. 4: Native mass spectra of LmrP and the N116Y mutant reconstituted in nanodiscs made of DOPE (80%) and DOPG (20%).
Fig. 5: The S52Y, T56Y and N116Y mutations alter substrate specificity without directly interacting with Hoechst 33342.

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Data availability

The atomic coordinates and structure factors (with and without anisotropic cutoff) reported in this Article have been deposited in the Worldwide Protein Data Bank under accession code PDB 6T1Z. Source data are provided with this paper.

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Acknowledgements

We thank J.-M. Ruysschaert and A. Garcia-Pino for helpful discussions. We thank J. Ault of the Biomolecular Mass Spectrometry Facility for his support and assistance in this work and the BBSRC (BB/M012573/1) for funding. This work was supported by the Fonds de la Recherche Scientifique FRS-FNRS (grants F.4523.12, T.0057.15F, J0044.17F and T.0105.19). V.D. was a fellow of the FRIA and C.M. is postdoctoral researcher of the FRS-FNRS. C.G. is supported as a senior research associate of the FRS-FNRS. H.S.M. was supported by grant no. GM077650 from the National Institutes of Health (NIH). E.F. and J.D.F.-G. were supported by the Division of Intramural Research of NHLBI/NIH. Computational resources were in part provided by the NIH Core Facility Biowulf. E.F. and J.D.F.-G. are grateful to J. Brown for her contributions to the development of a simulation forcefield for Hoechst 33342.

Author information

Author notes

  1. Matthieu Masureel

    Present address: Department of Structural Biology, Genentech, Inc., South San Francisco, CA, USA

  2. These authors contributed equally: Vincent Debruycker, Andrew Hutchin.

Authors and Affiliations

  1. Laboratory for the Structure and Function of Biological Membranes, Center for Structural Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium

    Vincent Debruycker, Andrew Hutchin, Matthieu Masureel, Chloé Martens & Cédric Govaerts

  2. Theoretical Molecular Biophysics Laboratory, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Emel Ficici & José D. Faraldo-Gómez

  3. Synchrotron SOLEIL, L’Orme des Merisiers, Saint-Aubin 91192, Gif sur Yvette, France

    Pierre Legrand

  4. Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, USA

    Richard A. Stein & Hassane S. Mchaourab

  5. Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium

    Han Remaut

  6. Structural and Molecular Microbiology, VIB-VUB Center for Structural Biology, Brussels, Belgium

    Han Remaut

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Contributions

Experimental design was performed by V.D., M.M., A.H., H.R. and C.G. Mutagenesis, expression, activity, purification, crystallization and data collection were carried out by V.D., A.H., M.M., H.R. and C.G. DEER experiments (protein production, purification, labeling, data acquisition and interpretation) were performed by C.M., R.S. and H.M. Native mass spectrometry experiments were performed and analyzed by C.M. Structure solution, model building and refinement were carried out by P.L., C.G. and A.H. MD simulations were conducted by E.F. and J.D.F.-G. All authors participated in interpreting the data and writing the manuscript.

Corresponding author

Correspondence to Cédric Govaerts.

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Extended data

Extended Data Fig. 1 Binding of different substrates to LmrP does not induce a large conformational change in the binding pocket.

Limited variations in distance distribution are observed when comparing LmrP in the apo state (blue) and in the presence of Hoechst 33342 (green), ethidium bromide (red), roxithromycin (cyan), TPP+ (yellow), verapamil (gray) and tetracycline (magenta), using one probe on the extracellular face and one within the binding pocket. Distributions were normalized. Interspin distance is denoted by r, with P(r) indicating the distance probability.

Extended Data Fig. 2 Selenium anomalous maps used to phase LmrP data.

Methionine residues are shown as sticks, and anomalous maps collected at the selenium edge, contoured at 3 σ, are shown in green.

Extended Data Fig. 3 Hydrogen-bonding interactions between POPG and outward-facing LmrP.

Plots indicating observed hydrogen bonds between either the phosphate group in POPG a, or the glycerol within the headgroup b, and surrounding polar side chains in LmrP, as a function of simulation time. Each dot (colored according to the residue label) reflects an observed interaction. c, A diagram of POPG highlighting the relevant phosphate and headgroup glycerol. d, A molecular dynamics simulation snapshot demonstrating the positions of each interacting residue relative to the lipid.

Extended Data Fig. 4 Molecular dynamics simulations of the LmrP-Hoechst complex with and without POPG bound.

a, View of outward-facing LmrP along the membrane plane, with structural regions of seemingly distinct dynamics color-coded. Ligands are omitted for clarity. The peripheral regions, or Set 1 (blue), comprise TM helices 3, 4 and 6 in the N-lobe and TM 9, 10 and 12 in the C-lobe. Set 2 (orange) comprises the intracellular half of TM helices 1, 2 and 5, and TM 7, 8 and 11, respectively. Set 3 (yellow) includes the remainder of these 6 helices. b) Analysis of the structural dynamics of the N-lobe in terms of the root-mean-square deviation of the protein backbone relative to the X-ray structure (after least-squares self-fit). For each of the regions defined in (a) (Sets 1, 1 + 2 or 1 + 2 + 3), the plot shows the evolution of RMSD as a function of simulation time. Left and right plots compare simulations with only Hoechst bound to LmrP, and with both Hoechst and POPG bound, respectively. For clarity, only one of the two trajectories calculated in each case is analyzed. c, Same as b, for the C-lobe. d, Summary of the RMSD time-series data, in terms of time-averages alongside the corresponding standard deviations, for two independent simulations of each system. Data are compared for simulations of LmrP bound to Hoechst 33342, and of LmrP bound to Hoechst and a POPG lipid. Red arrows indicate regions for which a significant change was observed between simulation systems. e, Dynamics of the N- and C-lobes relative to each other, in the presence or absence of bound POPG. The plot quantifies the variability in distance between the two lobes, defined as the distance between the centers-of-mass of the peripheral regions in each lobe (Set 1, blue). The histograms shown derive from the time-series of this distance, combining the two simulations calculated with and without bound POPG. The value of this distance in the LmrP crystal structure is also indicated (vertical gray dashed line).

Extended Data Fig. 5 An embedded POPG helps stabilize charged residues within the binding pocket during molecular dynamics simulations.

a, Minimum distance between D142 (atoms Oδ1, Oδ2) and K357 (atom Nz). The plot compares the value of this distance in the crystal structure (horizontal gray dashed line) with time-averages calculated from the final 100 ns of the simulations of LmrP-Hoechst and those of LmrP-Hoechst-POPG. Error bars denote the standard deviations of the time-averages. b, 3D mass-density maps of D142 and K357 from simulations with only Hoechst 33342 (magenta), and from simulations with Hoechst 33342 and POPG (teal) highlight the difference in position of D142 in the absence of POPG. The conformations of D142 and K357 at the end of each simulation are shown as a visual aid. c) In simulations with POPG present (teal and cyan) the distance between R14 and D142 is consistent with that observed in the crystal structure. When POPE is modeled instead of POPG (light orange and dark orange), the R14-D142 salt bridge breaks off, as observed when no lipid is modeled (Fig. 3d), but in the simulated timescale R14 remains within the binding cavity. Two independent trajectories for each simulation condition are shown. Source data are available online.

Source data

Extended Data Fig. 6 Native MS identifies a specific interaction between LmrP and PG phospholipid inside the binding pocket cavity.

Mass spectra recorded at increasing energies by modulating in-source trapping voltage values for a, LmrP and b, LmrP N116Y reconstituted in PE:PG nanodiscs (80:20). c, A plot of the relative fractional intensity of the single PG-bound LmrP peak versus the LmrP apo peak over increasing source voltage values. The individual mass spectra at each voltage value were transformed using the MaxEnt 1 deconvolution algorithm and the peak intensities of both bound and unbound species were extracted. The red arrow indicates the voltage value where no PG lipid is observed on the mutant anymore.

Extended Data Fig. 7 High resolution nMS analysis differentiates lipid adducts on LmrP.

a, Zoom-in of the 11+ charge state of the high-resolution Orbitrap mass spectrum of LmrP in DOPE:DOPG MSP-based nanodiscs, obtained at 150 V (upper panel) and 200 V (lower panel) in-source voltage. The number and identity of lipid adducts is indicated for each peak, with the orange and purple ovals indicating DOPG and DOPE binding respectively. b, Deconvoluted mass spectrum of LmrP obtained at 150 V in-source voltage. The exact masses of each peak are indicated and allow identification of the exact pattern of bound lipids. c, Zoom-in of the 11+ charge state of the high-resolution Orbitrap mass spectrum of LmrP N116Y mutant in DOPE:DOPG MSP-based nanodiscs, at 150 V (upper panel) and 200 V (lower panel) in-source voltage. d, Deconvoluted mass spectrum of LmrP N116Y obtained at 150 V in-source voltage. Deconvolution was performed using the MaxEnt1 deconvolution algorithm.

Extended Data Fig. 8 Altered cell survival of L. lactis cells expressing LmrP mutants.

a, Residues 52 and 56 are positioned within the protein interior, and mutation to tyrosine (as depicted by the placement of a common tyrosine rotamer, yellow spheres) is predicted to perturb the binding of the interior lipid (which is shown by the corresponding 2Fo-Fc map at 1 σ). Bound Hoechst 33342 is shown in stick representation (green). See Fig. 4b for an equivalent panel of residue 116. b, Western blot demonstrating similar levels of expression for wild-type and mutant variants of LmrP. Resuspended membranes were prepared for each mutant at a concentration of 0.2 g /ml in buffer A, as detailed in the methods. Samples of each resuspended membrane were resolved through SDS PAGE followed by Western blot analysis. Target proteins were identified using two-step, indirect detection with murine anti-His used as primary antibodies, and anti-mouse-horseradish-peroxidase (HRP) conjugates used as a secondary antibodies. Antibody-bound proteins were revealed using a chemiluminescent HRP substrate. Uncropped images available as source data online. c, Cell survival MIC50 values (the minimum inhibitory concentration required to inhibit 50% of growth) and corresponding standard error mean, calculated by fitting a sigmoidal dose–response curve on 6 independent curves using Prism 8.0. Resultant graphs from which these are values are derived are shown in Fig. 5b–d.

Source data

Extended Data Fig. 9 MD simulation of Hoechst 33342 in solution, using the newly-developed CHARMM-compatible forcefield.

a, Comparison of the geometry of Hoechst 33342 in simulation and the experimental structure observed in complex with LmrP, in terms of the RMS difference between simulated and experimental geometries for each of the constituent chemical groups. The RMSD data is shown as histograms, derived from time-series of 100 ns. b, Evaluation of the interaction of Hoechst with water. For each snapshot in a 100-ns trajectory, water molecules in the first hydration shell were mapped onto a 3D number-density distribution (oxygen and hydrogen atoms separately), which was then time-averaged. Iso-density surfaces/meshes are shown for water oxygen (red, 0.07 σ) and hydrogen (cyan, 0.14 σ), to highlight the most persistent interaction sites and relative ligand-water orientations.

Supplementary information

Source data

Source Data Fig. 3

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 8

Full length western blot.

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Debruycker, V., Hutchin, A., Masureel, M. et al. An embedded lipid in the multidrug transporter LmrP suggests a mechanism for polyspecificity. Nat Struct Mol Biol 27, 829–835 (2020). https://doi.org/10.1038/s41594-020-0464-y

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