title: A Practical Implicit Membrane Potential for NMR Structure Calculations of Membrane Proteins.
abstract: The highly anisotropic environment of the lipid bilayer membrane imposes significant constraints on the structures and functions of membrane proteins. However, NMR structure calculations typically use a simple repulsive potential that neglects the effects of solvation and electrostatics, because explicit atomic representation of the solvent and lipid molecules is computationally expensive and impractical for routine NMR-restrained calculations that start from completely extended polypeptide templates. Here, we describe the extension of a previously described implicit solvation potential, eefxPot, to include a membrane model for NMR-restrained calculations of membrane protein structures in XPLOR-NIH. The key components of eefxPot are an energy term for solvation free energy that works together with other nonbonded energy functions, a dedicated force field for conformational and nonbonded protein interaction parameters, and a membrane function that modulates the solvation free energy and dielectric screening as a function of the atomic distance from the membrane center, relative to the membrane thickness. Initial results obtained for membrane proteins with structures determined experimentally in lipid bilayer membranes show that eefxPot affords significant improvements in structural quality, accuracy, and precision. Calculations with eefxPot are straightforward to implement and can be used to both fold and refine structures, as well as to run unrestrained molecular-dynamics simulations. The potential is entirely compatible with the full range of experimental restraints measured by various techniques. Overall, it provides a useful and practical way to calculate membrane protein structures in a physically realistic environment.
authors.affiliation: Sanford-Burnham Medical Research Institute, La Jolla, California; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California.
authors.forename: Ye
authors.initials: Y
authors.lastname: Tian
authors.affiliation: Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Maryland.
authors.forename: Charles D
authors.initials: CD
authors.lastname: Schwieters
authors.affiliation: Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California.
authors.forename: Stanley J
authors.initials: SJ
authors.lastname: Opella
authors.affiliation: Sanford-Burnham Medical Research Institute, La Jolla, California. Electronic address: fmarassi@sbmri.org.
authors.forename: Francesca M
authors.initials: FM
authors.lastname: Marassi
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001426:Bacterial Proteins / Q000737:chemistry; D006801:Humans; D008564:Membrane Potentials; D008565:Membrane Proteins / Q000737:chemistry*; D008969:Molecular Sequence Data
chemical_list: D001426:Bacterial Proteins; D008565:Membrane Proteins