Molecular dynamics simulations in: High-resolution structures with bound Mn2+ and Cd2+ map the metal import pathway in an Nramp transporter
Wilson, Eric et al. (2022), Molecular dynamics simulations in: High-resolution structures with bound Mn2+ and Cd2+ map the metal import pathway in an Nramp transporter, Dryad, Dataset, https://doi.org/10.5061/dryad.tx95x6b2b
Transporters of the Nramp (Natural resistance-associated macrophage protein) family import divalent transition metal ions into cells of most organisms. By supporting metal homeostasis, Nramps prevent disorders related to metal insufficiency or overload. Previous studies revealed that Nramps take on a LeuT fold and identified the metal-binding site. We present high- resolution structures of Deinococcus radiodurans Nramp in three stable conformations of the transport cycle revealing that global conformational changes are supported by distinct coordination geometries of its physiological substrate, Mn2+, across conformations and conserved networks of polar residues lining the inner and outer gates. A Cd2+-bound structure highlights differences in coordination geometry for Mn2+ and Cd2+. Measurements of metal binding using isothermal titration calorimetry indicate that the thermodynamic landscape for binding and transporting physiological metals like Mn2+ is different and more robust to perturbation than for transporting the toxic Cd2+ metal.
Molecular dynamics (MD) simulations were initialized from three high-resolution structures of DraNramp: the outward-open G223W•Mn2+ structure (6BU5) with Mn2+ removed and W223 mutated back to the native glycine residue in silico, the inward-open WT•Cd2+ structure with Cd2+ removed, and the inward-occluded WT structure. Crystallographic waters were retained, and protonation states of key titratable residues were selected with PROPKA (M. H. Olsson, Sondergaard, Rostkowski, & Jensen, 2011; Sondergaard, Olsson, Rostkowski, & Jensen, 2011) assuming a pH of 5.0 for residues exposed to external solvent and a pH of 7.0 for residues exposed to cytosol, a condition under which DraNramp exhibits high activity. All structures were oriented in the membrane with the PPM web server and membrane systems were prepared with CHARMM-GUI (Jo, Kim, Iyer, & Im, 2008; Lee et al., 2016). A POPC membrane of surface area 99 x 99 Å was constructed in the XY plane around the protein (Wu et al., 2014), the system was solvated in a 100 x 100 x 100 Å3 rectangular box using TIP3 waters and electronically neutralized using potassium and chlorine ions at an overall concentration of 150 mM. The overall system size was approximately 103,000 atoms.
All-atom simulations were run using GPU-accelerated NAMD (Phillips, Stone, & Schulten, 2008) and the CHARMM36m forcefield (Huang et al., 2017). Prior to simulation, the energy of each system was minimized for 10,000 steps using a conjugate gradient and line search algorithm native to NAMD. To improve simulation stability, the system was initially equilibrated using an NVT-ensemble with harmonic restraints placed on protein and lipid heavy atoms. The harmonic restraints were then incrementally relaxed over a period of 675 ps according to established CHARMM-GUI protocols (Lee et al., 2016). The system was then simulated at a constant pressure, utilizing the Langevin piston method to maintain 1 atm at 303.15 K, from anywhere between 617 to 1176 ns depending on the starting conformation. Simulations were performed using periodic boundary conditions and a time step of 3.0 fs with all bonds to hydrogens being constrained. Large integration timesteps were enabled by employing hydrogen mass repartitioning (Hopkins, Le Grand, Walker, & Roitberg, 2015). Long-range electrostatic interactions were calculated using the particle mesh Ewald (PME) method with nonbonded interactions being cut off at 12 Å. Each simulation was performed in duplicate resulting in approximately 2 µs of total sampling for each system. Simulations are summarized in Supplementary Table 11.
Code to generate the water occupancy data and analyze the simulations is located on GitHub: https://github.com/samberry19/nramp-md.
More info on DraNramp can be found in our preprint: https://www.biorxiv.org/content/10.1101/2022.09.08.507188v1.
Can be visualized in VMD or PyMOL, but files are likely too large and should be either strided first (e.g. with catdcd) or analyzed directly, e.g. in Python, likely on a supercomputing cluster. If you want to use these simulations but do not have access to sufficient computing resources, feel free to reach out to us directly. See https://github.com/samberry19/nramp-md for how these simulations are analyzed in the manuscript.
National Science Foundation, Award: MCB-1942763
Mayo Foundation for Medical Education and Research