Positioning of proteins in membranes: A computational approach

Abstract

Thousands of membrane-associated proteins have been deposited in the Protein Data Bank (PDB; Berman et al. 2000), and their number is rapidly growing. However, the precise spatial positions of these proteins in membranes are unknown. Membrane proteins are unique because they function in the highly anisotropic environment of a lipid bilayer, which is characterized by complex polarity gradients and a heterogeneous molecular composition in different regions and leaflets. The positioning of proteins in the lipid matrix may affect their biological activity, folding, thermodynamic stability, and binding with surrounding macromolecules and substrates (White and Wimley 1999; Booth et al. 2001; Bowie 2001; DeGrado et al. 2003; Engelman et al. 2003; Hong and Tamm 2004; Lee 2004). Hence, the orientations of many peptides and proteins in membranes have been studied by a variety of experimental techniques including chemical modification, spin-labeling, paramagnetic or fluorescence quenching, X-ray scattering, neuron diffraction, electron cryomicroscopy, NMR, and polarized infrared spectroscopy (Frillingos et al. 1998; Hristova et al. 1999; London and Ladokhin 2002; de Planque and Killian 2003; Hubbell et al. 2003; Opella and Marassi 2004; Tatulian et al. 2005). However, since the amount of such experimental data is limited, this problem should also be addressed computationally to keep up with the expanding flow of structures in the PDB.

The arrangement of a protein with respect to the membrane can be defined by its shift along the bilayer normal (d), rotational and tilt angles (ϕ and τ), and thickness of its membrane-spanning region (D = 2z₀, Fig. ​Fig.1).1). Although the orientation of a transmembrane (TM) protein in a lipid bilayer can be assessed manually (Lee 2003), development of automated methods is necessary to provide more objective, reproducible, and accurate results. The existing computational approaches range from elaborate molecular dynamic (MD) simulations of proteins with explicit water and lipids (Ash et al. 2004; Roux et al. 2004; Gumbart et al. 2005) to simplified approaches that minimize protein transfer energy from water to a hydrophobic slab, which serves as a crude approximation of the membrane hydrocarbon core (Yeates et al. 1987; Rees et al. 1989). In the latter case, the transfer energy can be estimated by using various hydrophobicity scales of whole residues (Zucic and Juretic 2004), a normalized nonpolar accessible surface area (Tusnady et al. 2004), or atomic solvation parameters derived from partition coefficients of model organic compounds between water and nonpolar solvents (Basyn et al. 2003). One such computational method has recently been applied to create the PDB_TM database that provides an up-to-date list of all TM peptides and proteins from the PDB (Tusnady et al. 2005). However, the hydrophobic boundaries of proteins in PDB_TM were not compared with relevant experimental studies and their accuracy is uncertain.

Figure 1.

Schematic representation of a TM protein in a hydrophobic slab. Parameters that define the arrangement of a TM protein in the membrane hydrocarbon core: d, shift along the bilayer normal; τ, tilt angle; ϕ, rotational angle; and D = 2z₀, hydrophobic thickness of the protein.

In this paper, we present a new computational approach for positioning proteins in membranes that agrees better with experimental data, which are currently available for 24 TM proteins of known 3D structure. The optimal spatial arrangement of a protein is determined by minimizing its transfer energy from water to a hydrophobic slab with decadiene-like polarity. This method was developed, verified, and applied to all TM proteins from the PDB. The results are deposited in our Orientations of Proteins in Membranes (OPM) database to allow their further examination, use and testing by the scientific community (Lomize et al. 2006).

Acknowledgments

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