The role of hydrophobic interactions in positioning of peripheral proteins in membranes

<p>Abstract</p> <p>Background</p> <p>Three-dimensional (3D) structures of numerous peripheral membrane proteins have been determined. Biological activity, stability, and conformations of these proteins depend on their spatial positions with respect to the lipid bilayer. However, these positions are usually undetermined.</p> <p>Results</p> <p>We report the first large-scale computational study of monotopic/peripheral proteins with known 3D structures. The optimal translational and rotational positions of 476 proteins are determined by minimizing energy of protein transfer from water to the lipid bilayer, which is approximated by a hydrocarbon slab with a decadiene-like polarity and interfacial regions characterized by water-permeation profiles. Predicted membrane-binding sites, protein tilt angles and membrane penetration depths are consistent with spin-labeling, chemical modification, fluorescence, NMR, mutagenesis, and other experimental studies of 53 peripheral proteins and peptides. Experimental membrane binding affinities of peripheral proteins were reproduced in cases that did not involve a helix-coil transition, specific binding of lipids, or a predominantly electrostatic association. Coordinates of all examined peripheral proteins and peptides with the calculated hydrophobic membrane boundaries, subcellular localization, topology, structural classification, and experimental references are available through the Orientations of Proteins in Membranes (OPM) database.</p> <p>Conclusion</p> <p>Positions of diverse peripheral proteins and peptides in the lipid bilayer can be accurately predicted using their 3D structures that represent a proper membrane-bound conformation and oligomeric state, and have membrane binding elements present. The success of the implicit solvation model suggests that hydrophobic interactions are usually sufficient to determine the spatial position of a protein in the membrane, even when electrostatic interactions or specific binding of lipids are substantial. Our results demonstrate that most peripheral proteins not only interact with the membrane surface, but penetrate through the interfacial region and reach the hydrocarbon interior, which is consistent with published experimental studies.</p

Life at the border: Adaptation of proteins to anisotropic membrane environment

This review discusses main features of transmembrane (TM) proteins which distinguish them from water‐soluble proteins and allow their adaptation to the anisotropic membrane environment. We overview the structural limitations on membrane protein architecture, spatial arrangement of proteins in membranes and their intrinsic hydrophobic thickness, co‐translational and post‐translational folding and insertion into lipid bilayers, topogenesis, high propensity to form oligomers, and large‐scale conformational transitions during membrane insertion and transport function. Special attention is paid to the polarity of TM protein surfaces described by profiles of dipolarity/polarizability and hydrogen‐bonding capacity parameters that match polarity of the lipid environment. Analysis of distributions of Trp resides on surfaces of TM proteins from different biological membranes indicates that interfacial membrane regions with preferential accumulation of Trp indole rings correspond to the outer part of the lipid acyl chain region—between double bonds and carbonyl groups of lipids. These “midpolar” regions are not always symmetric in proteins from natural membranes. We also examined the hydrophobic effect that drives insertion of proteins into lipid bilayer and different free energy contributions to TM protein stability, including attractive van der Waals forces and hydrogen bonds, side‐chain conformational entropy, the hydrophobic mismatch, membrane deformations, and specific protein–lipid binding.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108308/1/pro2508.pd

Structural organization of G-protein-coupled receptors

Atomic-resolution structures of the transmembrane 7-α-helical domains of 26 G-protein-coupled receptors (GPCRs) (including opsins, cationic amine, melatonin, purine, chemokine, opioid, and glycoprotein hormone receptors and two related proteins, retinochrome and Duffy erythrocyte antigen) were calculated by distance geometry using interhelical hydrogen bonds formed by various proteins from the family and collectively applied as distance constraints, as described previously [Pogozheva et al., Biophys. J., 70 (1997) 1963]. The main structural features of the calculated GPCR models are described and illustrated by examples. Some of the features reflect physical interactions that are responsible for the structural stability of the transmembrane α-bundle: the formation of extensive networks of interhelical H-bonds and sulfur–aromatic clusters that are spatially organized as 'polarity gradients' the close packing of side-chains throughout the transmembrane domain; and the formation of interhelical disulfide bonds in some receptors and a plausible Zn2+ binding center in retinochrome. Other features of the models are related to biological function and evolution of GPCRs: the formation of a common 'minicore' of 43 evolutionarily conserved residues; a multitude of correlated replacements throughout the transmembrane domain; an Na+-binding site in some receptors, and excellent complementarity of receptor binding pockets to many structurally dissimilar, conformationally constrained ligands, such as retinal, cyclic opioid peptides, and cationic amine ligands. The calculated models are in good agreement with numerous experimental data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42965/1/10822_2004_Article_200887.pd

Pharmacophore and receptor models for neurokinin receptors

Three neurokinin (NK) antagonist pharmacophore models (Models 1–3) accounting for hydrogen bonding groups in the `head' and `tail' of NK receptor ligands have been developed by use of a new procedure for treatment of hydrogen bonds during superimposition. Instead of modelling the hydrogen bond acceptor vector in the strict direction of the lone pair, an angle is allowed between the hydrogen bond acceptor direction and the ideal lone pair direction. This approach adds flexibility to hydrogen bond directions and produces more realistic RMS values. By using this approach, two novel pharmacophore models were derived (Models 2 and 3) and a hydrogen bond acceptor was added to a previously published NK2 pharmacophore model [Poulsen et al., J. Comput.-Aided Mol. Design, 16 (2002) 273] (Model 1). Model 2 as well as Model 3 are described by seven pharmacophore elements: three hydrophobic groups, three hydrogen bond acceptors and a hydrogen bond donor. Model 1 contains the same hydrophobic groups and hydrogen bond donor as Models 2 and 3, but only one hydrogen bond acceptor. The hydrogen bond acceptors and donor are represented as vectors. Two of the hydrophobic groups are always aromatic rings whereas the other hydrophobic group can be either aromatic or aliphatic. In Model 1 the antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. Model 2 has the same two aromatic rings in a parallel displaced conformation whereas Model 3 has the rings in an edge to face conformation. The pharmacophore models were evaluated using both a structure (NK receptor homology models) and a ligand based approach. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 21 non-peptide antagonists from several structurally diverse classes were fitted to the pharmacophore models. More antagonists could be fitted to Model 2 with a low RMS and a low conformational energy penalty than to Models 1 and 3. Pharmacophore Model 2 was also able to explain the NK1, NK2 and NK3 subtype selectivity of the compounds fitted to the model. Three NK 7TM receptor models were constructed, one for each receptor subtype. The location of the antagonist binding site in the three NK receptor models is identical. Compounds fitted to pharmacophore Model 2 could be docked into the NK1, NK2 and NK3 receptor models after adjustment of the conformation of the flexible linker connecting the head and tail. Models 1 and 3 are not compatible with the receptor models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42969/1/10822_2004_Article_5257316.pd

Design of high affinity cyclic pentapeptide ligands for Κ-opioid receptors

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73319/1/j.1399-3011.2005.00295.x.pd

Translation of structure‐activity relationships from cyclic mixed efficacy opioid peptides to linear analogues

Most opioid analgesics used in the treatment of pain are mu opioid receptor (MOR) agonists. While effective, there are significant drawbacks to opioid use, including the development of tolerance and dependence. However, the coadministration of a MOR agonist with a delta opioid receptor (DOR) antagonist slows the development of MOR‐related side effects, while maintaining analgesia. We have previously reported a series of cyclic mixed efficacy MOR agonist/DOR antagonist ligands. Here we describe the transfer of key features from these cyclic analogs to linear sequences. Using the linear MOR/DOR agonist, Tyr‐DThr‐Gly‐Phe‐Leu‐Ser‐NH 2 ( DTLES ), as a lead scaffold, we replaced Phe 4 with bulkier and/or constrained aromatic residues shown to confer DOR antagonism in our cyclic ligands. These replacements failed to confer DOR antagonism in the DTLES analogs, presumably because the more flexible linear ligands can adopt binding poses that will fit in the narrow binding pocket of the active conformations of both MOR and DOR. Nonetheless, the pharmacological profile observed in this series, high affinity and efficacy for MOR and DOR with selectivity relative to KOR, has also been shown to reduce the development of unwanted side effects. We further modified our lead MOR/DOR agonist with a C‐terminal glucoserine to improve bioavailability. The resulting ligand displayed high efficacy and potency at both MOR and DOR and no efficacy at KOR. © 2013 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 102: 107–114, 2014.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102641/1/bip22437.pd

Roles of residues 3 and 4 in cyclic tetrapeptide ligand recognition by the Κ -opioid receptor

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75723/1/j.1399-3011.2005.00220.x.pd

Novel Peptide Ligands of RGS4 from a Focused One-Bead, One-Compound Library

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65605/1/j.1747-0285.2008.00687.x.pd

OPM database and PPM web server: resources for positioning of proteins in membranes

The Orientations of Proteins in Membranes (OPM) database is a curated web resource that provides spatial positions of membrane-bound peptides and proteins of known three-dimensional structure in the lipid bilayer, together with their structural classification, topology and intracellular localization. OPM currently contains more than 1200 transmembrane and peripheral proteins and peptides from approximately 350 organisms that represent approximately 3800 Protein Data Bank entries. Proteins are classified into classes, superfamilies and families and assigned to 21 distinct membrane types. Spatial positions of proteins with respect to the lipid bilayer are optimized by the PPM 2.0 method that accounts for the hydrophobic, hydrogen bonding and electrostatic interactions of the proteins with the anisotropic water-lipid environment described by the dielectric constant and hydrogen-bonding profiles. The OPM database is freely accessible at http://opm.phar.umich.edu. Data can be sorted, searched or retrieved using the hierarchical classification, source organism, localization in different types of membranes. The database offers downloadable coordinates of proteins and peptides with membrane boundaries. A gallery of protein images and several visualization tools are provided. The database is supplemented by the PPM server (http://opm.phar.umich.edu/server.php) which can be used for calculating spatial positions in membranes of newly determined proteins structures or theoretical models