A Refinement Protocol to Define the Structure, Topology and Depth of Insertion of Membrane Proteins using Hybrid Solution/Solid-state NMR Restraints

Abstract

To fully describe the folding space and ultimately the biological function of membrane proteins (MPs), it is necessary to determine their interactions with lipid membranes. This intrinsic property of MPs referring to as structural topology is not possible to resolve using x-ray crystallography or solution NMR. Here, we present a hybrid solution and solid-state NMR approach for the simultaneous determination of the structure, topology, and depth of insertion of MPs. Distance and angle restraints obtained from solution NMR of MPs solubilized in detergent micelles are combined with backbone orientational restraints (15N chemical shift anisotropy and 15N-1H dipolar couplings) derived from solid-state NMR in orientated lipid bilayers (PISEMA) into a hybrid objective function. In addition, a supplementary energy term, Ez (insertion depth potential), is used to ensure the correct positioning of helical MPs domains with respect to a virtual membrane. The hybrid objective function is optimized using a two-stage simulated annealing protocol and is implemented into XPLOR-NIH software for general use. To validate the hybrid method, the effects of chemical shift tensor orientations, principal tensor values, and dipolar constant magnitudes on the structural ensemble are determined. The hybrid method is applied to monomeric and pentameric PLN (phospholamban), a integral MP that regulates sarco(endo)plasmic reticulum Ca-ATPase (SERCA) function in cardiac muscle. The hybrid conformational ensemble defines the structure, topology and depth of insertion of PLN in lipid bilayer simultaneously. This ensemble is compared with solution NMR structures in DPC micelle obtained using conventional solution NMR data (NOEs, J-couplings) and residual dipolar coupling as orientational restraints. This research was supported by NIH Grants GM64742 and K02HL080081 to G.V. and we also thank University of Minnesota, Supercomputing Institute (MSI) for providing the computing resource.