Thomas G. Metzger

On the role of extracellular loops of opioid receptors in conferring ligand selectivity

Based on an analysis of results taken from site‐directed mutagenesis studies performed on opioid receptors, a role for the extracellular loops in conferring opioid subtype selectivity is proposed. It is suggested that the extracellular loop regions (which represent the region of highest sequence variability among opioid subtypes) interact with opioid ligands in a primarily non‐specific fashion. Although these interactions are non‐specific, they appear to play a discriminatory role in ligand binding and, in certain cases, prevent particular ligands from binding among receptor subtypes. We propose that selectivity may be imparted through a mechanism of exclusion, rather than specific pharmacophore recognition within the extracellular loops and N‐terminal domain. This hypothesis is supported by a careful analysis of the binding profiles of several selective and non‐selective ligands to a variety of chimeric mutants. These results, when combined with results taken from single‐point mutation experiments point to the existence of a high affinity binding pocket within the transmembrane region which may be common among the opioid subtypes.

Application of the message-address concept to the docking of naltrexone and selective naltrexone-derived opioid antagonists into opioid receptor models

A binding site model for the opioid family of G-protein coupled receptors (GPCRs) is proposed based on the message-address concept of ligand recognition. Using ligand docking studies of the universal opioid antagonist, naltrexone, the structural basis for ‘message’ recognition is explored across all three receptor types, μ, δ, and κ. The binding mode proposed and basis for selectivity are also rationalized using the naltrexone-derived ligands, naltrindole (NTI) and norbinaltorphimine (nor BNI). These ligands are docked to the receptor according to the common naltrexone core or message. The resulting orientation places key ‘address’ elements in close proximity to amino acid residues critical to selectivity among receptor types. Selectivity is explained by sequence differences in the μ, δ, and κ receptors at these recognition points. Support for the model is derived from site directed mutagenesis studies and ligand binding data for the opioid receptors and other related GPCRs.

A computational analysis of interaction energies in methane and neopentane dimer systems

The gas‐phase interaction energies of methane and neopentane dimers are calculated at various intermolecular distances and geometries using several molecular mechanics and semiempirical parameter sets. For comparisons, a set of reference calculations are also performed using the 6–311G (2d, 2 p) basis set with the inclusion of second‐order Möller‐Plesset energies (MP2) and basis set superposition corrections. These calculations are further used to examine the mechanism by which the AM1 and PM3 methods account for dispersion interactions in molecular systems. While no specific parameter(s) are included in semiempirical energy functions to capture such effects, the results indicate that both methods produce favorable interaction energies at near contact distances for the dimer systems. AM1 energies, however, show much closer agreement with the reference calculations, indicating potential deficiencies in the PM3 parameter set. Although the source of the dispersion energy could be traced to the attractive Gaussians of the core repulsion function in the AM1 Hamiltonian, a similar link could not be established for PM3. In contrast, PM3 dispersion energies apparently stem from a collection of contributions implicitly included during parameter optimization, providing no clear mechanism for correction or adjustment. Based on the analysis presented, an approach is also suggested for improving the AM1 parameter set.

4 Molecular Modeling of Opioid Receptor-Ligand Complexes

Publisher Summary This chapter describes methods to develop opioid G protein-coupled receptors (GPCR) structural models for use in molecular docking and drug design. They are characterized by seven alpha helical transmembrane (TM) domains connected by three intracellular and three extracellular loops. The extracellular (EL) N-terminus and the intracellular (IL) C-terminus are both targets for posttranslational glycosylation. Although, the widespread cloning of GPCRs and the determination of high-resolution X-ray data on rhodopsin has greatly advanced this field over the last decade, significant challenges still remain in the development of molecular models for predicting ligand binding and selectivity, function, and the structural basis to signal transduction. The models reported are developed with a primary focus on ligand binding and recognition. To some extent, current models could be viewed as “averaged” structures as a wide range of data are accounted for in the model building. As more receptor-type specific data is available, however, these models will continue to evolve to capture individual conformational preferences for the receptors leading to more reliable automated docking and virtual screens. It also reviews that one of the main applications of GPCR model building is the rationalization and development of structure-based concepts of ligand recognition. It focuses on the automated docking of naloxone and naltrexone-derived ligands. The chapter concludes that virtual screening across diverse ligand databases may be out of the reach of current model resolutions; there is little doubt that the application will be expanded as more experimental and structural data on GPCRs becomes available and integrated into structural models of ligand-receptor recognition.

An Analysis of the Conserved Residues between Halobacterial Retinal Proteins and G-Protein Coupled Receptors: Implications for GPCR Modeling

An alignment of the transmembrane domains of halobacterial retinal proteins (including bacteriorhodopsin) and G-protein coupled receptors (GPCRs) is presented based on the commonality of conserved residues between families. Due to the limited sequence homology displayed by these proteins, an alternative strategy is proposed for sequence alignment that correlates residues within secondary structure elements. The nonsequential alignment developed identifies three proline and two aspartates residues that share common positions and, in the former case, similar functions in the transmembrane domain. The alignment is further applied to model the packing of transmembrane helices 5 and 6 of the beta-adrenergic receptor based on the backbone coordinates of bacteriorhodopsin helices 3 and 2, respectively. Unlike models derived from standard sequential alignments, the approach developed here allows the key structural features conferred by the proline residues to be captured during model building. The structure described is also compared with available site directed mutagenesis results as well as existing GPCR models. In addition to the implications to model building, the commonality observed suggests a potential relationship among the GPCRs and retinal proteins.

Conformational searches for the global minimum of protein models

The conformational space of two protein structures has been examined using a stochastic search method in an effort to locate the global minimum conformation. In order to reduce this optimization problem to a tractable level, we have implemented a simplified force field representation of the protein structure that drastically reduces the degrees of freedom. The model replaces each ammo acid (containing many atoms) with a single sphere centered on the Cα position. These spheres are connected by virtual bonds, producing a “string of beads” model of the peptide chain. This model has been coupled with our stochastic search method to globally optimize the conformation of two common structural motifs found in proteins, a 22-residue α-helical hairpin and a 46-residue β-barrel. The search method described further reduces the optimization problem by taking advantage of the rotational isomerisms associated with molecular conformations and stochastically explores the energy surface using internal, torsional degrees of freedom. The approach proved to be highly efficient for globally optimizing the conformation of the α-helical hairpin and β-barrel structure on a moderately powered workstation. The results were further verified by applying variations in the search strategy that probed the low energy regions of conformational space near the suspected global minimum. Since this method also provides information regarding the low energy conformers, we have presented an analysis of the structures populated, and brief comparisons with other work. Finally, we applied the method to globally optimize the conformation of a 9-residue peptide fragment using a popular all-atom representation and successfully located the global minimum consistent with results from previous work.