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

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

PDBTM: Protein Data Bank of transmembrane proteins after 8 years

The PDBTM database (available at http://pdbtm .enzim.hu), the first comprehensive and up-to-date transmembrane protein selection of the Protein Data Bank, was launched in 2004. The database was created and has been continuously updated by the TMDET algorithm that is able to distinguish between transmembrane and non-transmembrane proteins using their 3D atomic coordinates only. The TMDET algorithm can locate the spatial positions of transmembrane proteins in lipid bilayer as well. During the last 8 years not only the size of the PDBTM database has been steadily growing from ~400 to 1700 entries but also new structural elements have been identified, in addition to the well-known a-helical bundle and b-barrel structures. Numerous ‘exotic’ transmembrane protein structures have been solved since the first release, which has made it necessary to define these new structural elements, such as membrane loops or interfacial helices in the database. This article reports the new features of the PDBTM database that have been added since its first release, and our current efforts to keep the database up-to-date and easy to use so that it may continue to serve as a fundamental resource for the scientific community

Development and validation of opioid ligand–receptor interaction models: The structural basis of mu vs. delta selectivity

Opioid receptor binding conformations for two structurally related, conformationally constrained tetrapeptides, JOM-6 (µ receptor selective) and JOM-13 (δ receptor selective), were deduced using conformational analysis of these ligands and analogs with additional conformational restrictions. Docking of these ligands in their binding conformations to opioid receptor structural models, based upon the published rhodopsin X-ray structure, implicates specific structural features of the µ and δ receptor ligand binding sites as forming the basis for the µ selectivity of JOM-6 and the δ selectivity of JOM-13. In particular, the presence of E229 in the µ receptor (in place of the corresponding D210 of the δ receptor) causes an adverse electrostatic interaction with C-terminal carboxylate-containing ligands, resulting in the observed preference of ligands with an uncharged C-terminus for the µ receptor. In addition, the requirement that the Phe 3 side chain of JOM-13 assume a gauche orientation for optimal δ binding, whereas the Phe 3 side chain of JOM-6 must be in a trans orientation for high-affinity µ binding can be largely attributed to the steric effect of replacement of L300 of the δ receptor by W318 of the µ receptor. Testing this hypothesis by examining the binding of JOM-6 and several of its key analogs with specific µ receptor mutants is described. Our initial results are consistent with the proposed ligand–receptor interaction models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73528/1/j.1399-3011.2002.21061.x.pd

CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes

Sommer B, Dingersen T, Schneider S, Rubert S, Gamroth C. CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes (Conference Abstract). In: Journal of Cheminformatics. Journal of Cheminformatics. Vol 2(Suppl 1):O21. Springer Science and Business Media LLC; 2010

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

FoldGPCR: Structure prediction protocol for the transmembrane domain of G protein-coupled receptors from class A

Building reliable structural models of G protein-coupled receptors (GPCRs) is a difficult task because of the paucity of suitable templates, low sequence identity, and the wide variety of ligand specificities within the superfamily. Template-based modeling is known to be the most successful method for protein structure prediction. However, refinement of homology models within 1–3 Å CΑ RMSD of the native structure remains a major challenge. Here, we address this problem by developing a novel protocol (foldGPCR) for modeling the transmembrane (TM) region of GPCRs in complex with a ligand, aimed to accurately model the structural divergence between the template and target in the TM helices. The protocol is based on predicted conserved inter-residue contacts between the template and target, and exploits an all-atom implicit membrane force field. The placement of the ligand in the binding pocket is guided by biochemical data. The foldGPCR protocol is implemented by a stepwise hierarchical approach, in which the TM helical bundle and the ligand are assembled by simulated annealing trials in the first step, and the receptor-ligand complex is refined with replica exchange sampling in the second step. The protocol is applied to model the human Β 2 -adrenergic receptor (Β 2 AR) bound to carazolol, using contacts derived from the template structure of bovine rhodopsin. Comparison with the X-ray crystal structure of the Β 2 AR shows that our protocol is particularly successful in accurately capturing helix backbone irregularities and helix-helix packing interactions that distinguish rhodopsin from Β 2 AR. Proteins 2010. © 2010 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77435/1/22731_ftp.pd

TOPDB: topology data bank of transmembrane proteins

The Topology Data Bank of Transmembrane Proteins (TOPDB) is the most complete and comprehensive collection of transmembrane protein datasets containing experimentally derived topology information currently available. It contains information gathered from the literature and from public databases available on the internet for more than a thousand transmembrane proteins. TOPDB collects details of various experiments that were carried out to learn about the topology of particular transmembrane proteins. In addition to experimental data from the literature, an extensive collection of structural data was also compiled from PDB and from PDBTM. Because topology information is often incomplete, for each protein in the database the most probable topology that is consistent with the collected experimental constraints was also calculated using the HMMTOP transmembrane topology prediction algorithm. Each record in TOPDB also contains information on the given protein sequence, name, organism and cross references to various other databases. The web interface of TOPDB includes tools for searching, relational querying and data browsing as well as for visualization. TOPDB is designed to bridge the gap between the number of transmembrane proteins available in sequence databases and the publicly accessible topology information of experimentally or computationally studied transmembrane proteins. TOPDB is available at http://topdb.enzim.hu