Predisposition of the dark state of rhodopsin to functional changes in structure

Activation and Speciation Mechanisms in Class A GPCRs

Accurate development of allosteric modulators of GPCRs require a thorough assessment of their sequence, structure, and dynamics, toward gaining insights into their mechanisms of actions shared by family members, as well as dynamic features that distinguish subfamilies. Building on recent progress in the characterization of the signature dynamics of proteins, we analyzed here a dataset of 160 Class A GPCRs to determine their sequence similarities, structural landscape, and dynamic features across different species (human, bovine, mouse, squid, and rat), different activation states (active/inactive), and different subfamilies. The two dominant directions of variability across experimentally resolved structures, identified by principal component analysis of the dataset, shed light to cooperative mechanisms of activation, subfamily differentiation, and speciation of Class A GPCRs. The analysis reveals the functional significance of the conformational flexibilities of specific structural elements, including: the dominant role of the intracellular loop 3 (ICL3) together with the cytoplasmic ends of the adjoining helices TM5 and TM6 in enabling allosteric activation; the role of particular structural motifs at the extracellular loop 2 (ECL2) connecting TM4 and TM5 in binding ligands specific to different subfamilies; or even the differentiation of the N-terminal conformation across different species. Detailed analyses of the modes of motions accessible to the members of the dataset and their variations across members demonstrate how the active and inactive states of GPCRs obey distinct conformational dynamics. The collective fluctuations of the GPCRs are robustly defined in the active state, while the inactive conformers exhibit broad variance among members.

Uncovering GPCR and G Protein Function by Protein Structure Network Analysis

Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used for investigating structural communication in biomolecular systems. Information on the system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM-NMA). This chapter reports on selected applications of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs) and G proteins. Strategies to highlight changes in structural communication caused by mutations, ligand and protein binding are described. Conserved amino acids, sites of misfolding mutations, or ligands acting as functional switches tend to behave as hubs in the native structure networks. Densely linked regions in the protein structure graphs could be identified as playing central roles in protein stability and function. Changes in the communication pathway fingerprints depending on the bound ligand or following amino acid mutation could be highlighted as well. A bridge between misfolding and misrouting could be established in rhodopsin mutants linked to inherited blindness. The analysis of native network perturbations by misfolding mutations served to infer key structural elements of protein responsiveness to small chaperones with implications for drug discovery.

Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals.

Structure network analysis to gain insights into GPCR function

G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM–NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM–NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

Use of network model to explore dynamic and allosteric properties of three GPCR homodimers

We used an elastic network model and protein structure network to study three class A GPCR homodimers.

Structures and biogenetic analysis of lipofuscin bis-retinoids

Age-related macular degeneration (AMD) is still an incurable blinding eye disease because of complex pathogenic mechanisms and unusual diseased regions. With the use of chemical biology tools, great progress has been achieved in improving the understanding of AMD pathogenesis. The severity of AMD is, at least in part, linked to the non-degradable lipofuscin bis-retinoids in retinal pigment epithelial (RPE). This material is thought to result from the lifelong accumulation of lysosomal residual bodies containing the end products derived from the daily phagocytosis of rod outer segments by RPE cells. Here, we present previously recognized bis-retinoids with focus on structures and biosynthetic pathways. In addition to a brief discussion on the mutual conversion relationships of bis-retinoids, future perspectives and the medical relevance of such studies on these lipofuscin constituents are also highlighted.

Elastic network normal mode dynamics reveal the GPCR activation mechanism

G-protein-coupled receptors (GPCR) are a family of membrane-embedded metabotropic receptors which translate extracellular ligand binding into an intracellular response. Here, we calculate the motion of several GPCR family members such as the M2 and M3 muscarinic acetylcholine receptors, the A2A adenosine receptor, the β2 -adrenergic receptor, and the CXCR4 chemokine receptor using elastic network normal modes. The normal modes reveal a dilation and a contraction of the GPCR vestibule associated with ligand passage, and activation, respectively. Contraction of the vestibule on the extracellular side is correlated with cavity formation of the G-protein binding pocket on the intracellular side, which initiates intracellular signaling. Interestingly, the normal modes of rhodopsin do not correlate well with the motion of other GPCR family members. Electrostatic potential calculation of the GPCRs reveal a negatively charged field around the ligand binding site acting as a siphon to draw-in positively charged ligands on the membrane surface. Altogether, these results expose the GPCR activation mechanism and show how conformational changes on the cell surface side of the receptor are allosterically translated into structural changes on the inside.

Novel computational methodologies for structural modeling of spacious ligand binding sites of G-protein-coupled receptors: development and application to human leukotriene B4 receptor

This paper describes a novel method to predict the activated structures of G-protein-coupled receptors (GPCRs) with high accuracy, while aiming for the use of the predicted 3D structures in in silico virtual screening in the future. We propose a new method for modeling GPCR thermal fluctuations, where conformation changes of the proteins are modeled by combining fluctuations on multiple time scales. The core idea of the method is that a molecular dynamics simulation is used to calculate average 3D coordinates of all atoms of a GPCR protein against heat fluctuation on the picosecond or nanosecond time scale, and then evolutionary computation including receptor-ligand docking simulations functions to determine the rotation angle of each helix of a GPCR protein as a movement on a longer time scale. The method was validated using human leukotriene B4 receptor BLT1 as a sample GPCR. Our study demonstrated that the proposed method was able to derive the appropriate 3D structure of the active-state GPCR which docks with its agonists.

Structure-based molecular modeling approaches to GPCR oligomerization

Classical structure-based drug design techniques using G-protein-coupled receptors (GPCRs) as targets focus nearly exclusively on binding at the orthosteric site of a single receptor. Dimerization and oligomerization of GPCRs, proposed almost 30 years ago, have, however, crucial relevance for drug design. Targeting these complexes selectively or designing small molecules that affect receptor-receptor interactions might provide new opportunities for novel drug discovery. In order to study the mechanisms and dynamics that rule GPCRs oligomerization, it is essential to understand the dynamic process of receptor-receptor association and to identify regions that are suitable for selective drug binding, which may be determined with experimental methods such as Förster resonance energy transfer (FRET) or Bioluminescence resonance energy transfer (BRET) and computational sequence- and structure-based approaches. The aim of this chapter is to provide a comprehensive description of the structure-based molecular modeling methods for studying GPCR dimerization, that is, protein-protein docking, molecular dynamics, normal mode analysis, and electrostatics studies.

Network analysis to uncover the structural communication in GPCRs

Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system dynamics can be provided by atomistic molecular dynamics simulations or coarse-grained Elastic Network Models paired with Normal Mode Analysis (ENM-NMA). This chapter describes the application of PSN analysis to uncover the structural communication in G protein-coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding mutations, dimerization, and activation are described. Focus is put on the ENM-NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signaling active (meta II (MII)) states, highlighting clear changes in the PSN and the centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

Identifying ligand binding conformations of the β2-adrenergic receptor by using its agonists as computational probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β(2)-adrenergic receptor (β(2)AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β(2)AR. We show that the select ligands bind preferentially to different predicted conformers of β(2)AR, and identify a role of β(2)AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance.

Elastic Network Models are Robust to Variations in Formalism

Understanding the functions of biomolecules requires insight not only from structures, but from dynamics as well. Often, the most interesting processes occur on time scales too slow for exploration by conventional molecular dynamics (MD) simulations. For this reason, alternative computational methods such as elastic network models (ENMs) have become increasingly popular. These simple, coarse-grained models represent molecules as beads connected by harmonic springs; the system's motions are solved analytically by normal mode analysis. In the past few years, many different formalisms for performing ENM calculations have emerged, and several have been optimized using all-atom MD simulations. In contrast to other studies, we have compared the various formalisms in a systematic, quantitative way. In this study, we optimize many ENM functional forms using a uniform dataset containing only long (> 1 μs) all-atom MD simulations. Our results show that all models once optimized produce spring constants for immediate neighboring residues that are orders of magnitude stiffer than more distal contacts. In addition, the statistical significance of ENM performance varied with model resolution. We also show that fitting long trajectories does not improve ENM performance due to a problem inherent in all network models tested: they underestimate the relative importance of the most concerted motions. Finally, we characterize ENMs' resilience by tessellating the parameter space to show that broad ranges of parameters produce similar quality predictions. Taken together our data reveals that choice of spring function and parameters are not vital to performance of a network model and that simple parameters can by derived "by hand" when no data is available for fitting, thus illustrating the robustness of these models.

Identification of motions in membrane proteins by elastic network models and their experimental validation

Identifying the functional motions of membrane proteins is difficult because they range from large-scale collective dynamics to local small atomic fluctuations at different timescales that are difficult to measure experimentally due to the hydrophobic nature of these proteins. Elastic Network Models, and in particular their most widely used implementation, the Anisotropic Network Model (ANM), have proven to be useful computational methods in many recent applications to predict membrane protein dynamics. These models are based on the premise that biomolecules possess intrinsic mechanical characteristics uniquely defined by their particular architectures. In the ANM, interactions between residues in close proximity are represented by harmonic potentials with a uniform spring constant. The slow mode shapes generated by the ANM provide valuable information on the global dynamics of biomolecules that are relevant to their function. In its recent extension in the form of ANM-guided molecular dynamics (MD), this coarse-grained approach is augmented with atomic detail. The results from ANM and its extensions can be used to guide experiments and thus speedup the process of quantifying motions in membrane proteins. Testing the predictions can be accomplished through (a) direct observation of motions through studies of structure and biophysical probes, (b) perturbation of the motions by, e.g., cross-linking or site-directed mutagenesis, and (c) by studying the effects of such perturbations on protein function, typically through ligand binding and activity assays. To illustrate the applicability of the combined computational ANM-experimental testing framework to membrane proteins, we describe-alongside the general protocols-here the application of ANM to rhodopsin, a prototypical member of the pharmacologically relevant G-protein coupled receptor family.

The cytoplasmic rhodopsin-protein interface: potential for drug discovery

The mammalian dim-light photoreceptor rhodopsin is a prototypic G protein coupled receptor (GPCR), interacting with the G protein, transducin, rhodopsin kinase, and arrestin. All of these proteins interact with rhodopsin at its cytoplasmic surface. Structural and modeling studies have provided in-depth descriptions of the respective interfaces. Overlap and thus competition for binding surfaces is a major regulatory mechanism for signal processing. Recently, it was found that the same surface is also targeted by small molecules. These ligands can directly interfere with the binding and activation of the proteins of the signal transduction cascade, but they can also allosterically modulate the retinal ligand binding pocket. Because the pocket that is targeted contains residues that are highly conserved across Class A GPCRs, these findings imply that it may be possible to target multiple GPCRs with the same ligand(s). This is desirable for example in complex diseases such as cancer where multiple GPCRs participate in the disease networks.

Membrane protein dynamics from femtoseconds to seconds

Membrane proteins play a key role in energy conversion, transport, signal recognition, transduction, and other fundamental biological processes. Despite considerable progress in experimental techniques, the determination of structure and dynamics of membrane proteins still represents a great challenge. Computer simulation methods are becoming an increasingly important tool not only in the interpretation of experiments but also in the prediction of membrane protein dynamics. In the present review, we give a brief introduction to molecular modeling techniques currently used to explore protein dynamics on time scales ranging from femtoseconds to microseconds. We then describe a few recent example applications of these techniques to membrane proteins. In conclusion, we also discuss some of the newest developments in simulation methodology that have the potential to further extend the time scale accessible to explore (membrane) protein dynamics.

Validating and improving elastic network models with molecular dynamics simulations

Elastic network models (ENMs) are a class of simple models intended to represent the collective motions of proteins. In contrast to all-atom molecular dynamics simulations, the low computational investment required to use an ENM makes them ideal for speculative hypothesis-testing situations. Historically, ENMs have been validated via comparison to crystallographic B-factors, but this comparison is relatively low-resolution and only tests the predictions of relative flexibility. In this work, we systematically validate and optimize a number of ENM-type models by quantitatively comparing their predictions to microsecond-scale all-atom simulations of three different G protein coupled receptors. We show that, despite their apparent simplicity, well-optimized ENMs perform remarkably well, reproducing the protein fluctuations with an accuracy comparable to what one would expect from all-atom simulations run for several hundred nanoseconds.

Progress in elucidating the structural and dynamic character of G Protein-Coupled Receptor oligomers for use in drug discovery

G Protein-Coupled Receptors (GPCRs) are the most targeted group of proteins for the development of therapeutic drugs. Until the last decade, structural information about this family of membrane proteins was relatively scarce, and their mechanisms of ligand binding and signal transduction were modeled on the assumption that GPCRs existed and functioned as monomeric entities. New crystal structures of native and engineered GPCRs, together with important biochemical and biophysical data that reveal structural details of the activation mechanism(s) of this receptor family, provide a valuable framework to improve dynamic molecular models of GPCRs with the ultimate goal of elucidating their allostery and functional selectivity. Since the dynamic movements of single GPCR protomers are likely to be affected by the presence of neighboring interacting subunits, oligomeric arrangements should be taken into account to improve the predictive ability of computer-assisted structural models of GPCRs for effective use in drug design.

Atomistic insights into rhodopsin activation from a dynamic model

Rhodopsin, the light sensitive receptor responsible for blue-green vision, serves as a prototypical G protein-coupled receptor (GPCR). Upon light absorption, it undergoes a series of conformational changes that lead to the active form, metarhodopsin II (META II), initiating a signaling cascade through binding to the G protein transducin (G(t)). Here, we first develop a structural model of META II by applying experimental distance restraints to the structure of lumi-rhodopsin (LUMI), an earlier intermediate. The restraints are imposed by using a combination of biased molecular dynamics simulations and perturbations to an elastic network model. We characterize the motions of the transmembrane helices in the LUMI-to-META II transition and the rearrangement of interhelical hydrogen bonds. We then simulate rhodopsin activation in a dynamic model to study the path leading from LUMI to our META II model for wild-type rhodopsin and a series of mutants. The simulations show a strong correlation between the transition dynamics and the pharmacological phenotypes of the mutants. These results help identify the molecular mechanisms of activation in both wild type and mutant rhodopsin. While static models can provide insights into the mechanisms of ligand recognition and predict ligand affinity, a dynamic model of activation could be applicable to study the pharmacology of other GPCRs and their ligands, offering a key to predictions of basal activity and ligand efficacy.

Influence of oligomerization on the dynamics of G-protein coupled receptors as assessed by normal mode analysis

The recently discovered impact of oligomerization on G-protein coupled receptor (GPCR) function further complicates the already challenging goal of unraveling the molecular and dynamic mechanisms of these receptors. To help understand the effect of oligomerization on the dynamics of GPCRs, we have compared the motion of monomeric, dimeric, and tetrameric arrangements of the prototypic GPCR rhodopsin, using an approximate-yet powerful-normal mode analysis (NMA) technique termed elastic network model (ENM). Moreover, we have used ENM to discriminate between putative dynamic mechanisms likely to account for the recently observed conformational rearrangement of the TM4,5-TM4,5 dimerization interface of GPCRs that occurs upon activation. Our results indicate: (1) significant perturbation of the normal modes (NMs) of the rhodopsin monomer upon oligomerization, which is mainly manifested at interfacial regions; (2) increased positive correlation among the transmembrane domains (TMs) and between the extracellular loop (EL) and TM regions of the rhodopsin protomer; (3) highest interresidue positive correlation at the interfaces between protomers; and (4) experimentally testable hypotheses of differential motional changes within different putative oligomeric arrangements.

Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors

Metabotropic glutamate receptors (mGluRs) are G protein coupled receptors that play important roles in synaptic plasticity and other neuro-physiological and pathological processes. Allosteric mGluR ligands are particularly promising drug targets because of their modulatory effects--enhancing or suppressing the response of mGluRs to glutamate. The mechanism by which this modulation occurs is not known. Here, we propose the hypothesis that positive and negative modulators will differentially stabilize the active and inactive conformations of the receptors, respectively. To test this hypothesis, we have generated computational models of the transmembrane regions of different mGluR subtypes in two different conformations. The inactive conformation was modeled using the crystal structure of the inactive, dark state of rhodopsin as template and the active conformation was created based on a recent model of the light-activated state of rhodopsin. Ligands for which the nature of their allosteric effects on mGluRs is experimentally known were docked to the modeled mGluR structures using ArgusLab and Autodock softwares. We find that the allosteric ligand binding pockets of mGluRs are overlapping with the retinal binding pocket of rhodopsin, and that ligands have strong preferences for the active and inactive states depending on their modulatory nature. In 8 out of 14 cases (57%), the negative modulators bound the inactive conformations with significant preference using both docking programs, and 6 out of 9 cases (67%), the positive modulators bound the active conformations. Considering results by the individual programs only, even higher correlations were observed: 12/14 (86%) and 8/9 (89%) for ArgusLab and 10/14 (71%) and 7/9 (78%) for AutoDock. These findings strongly support the hypothesis that mGluR allosteric modulation occurs via stabilization of different conformations analogous to those identified in rhodopsin where they are induced by photochemical isomerization of the retinal ligand--despite the extensive differences in sequences between mGluRs and rhodopsin.

Mechanism of signal propagation upon retinal isomerization: insights from molecular dynamics simulations of rhodopsin restrained by normal modes

As one of the best studied members of the pharmaceutically relevant family of G-protein-coupled receptors, rhodopsin serves as a prototype for understanding the mechanism of G-protein-coupled receptor activation. Here, we aim at exploring functionally relevant conformational changes and signal transmission mechanisms involved in its photoactivation brought about through a cis-trans photoisomerization of retinal. For this exploration, we propose a molecular dynamics simulation protocol that utilizes normal modes derived from the anisotropic network model for proteins. Deformations along multiple low-frequency modes of motion are used to efficiently sample collective conformational changes in the presence of explicit membrane and water environment, consistent with interresidue interactions. We identify two highly stable regions in rhodopsin, one clustered near the chromophore, the other near the cytoplasmic ends of transmembrane helices H1, H2, and H7. Due to redistribution of interactions in the neighborhood of retinal upon stabilization of the trans form, local structural rearrangements in the adjoining H3-H6 residues are efficiently propagated to the cytoplasmic end of these particular helices. In the structures obtained by our simulations, all-trans retinal interacts with Cys(167) on H4 and Phe(203) on H5, which were not accessible in the dark state, and exhibits stronger interactions with H5, while some of the contacts made (in the cis form) with H6 are lost.

Advances in the Development and Application of Computational Methodologies for Structural Modeling of G-Protein Coupled Receptors

BACKGROUND: Despite the large amount of experimental data accumulated in the past decade on G-protein coupled receptor (GPCR) structure and function, understanding of the molecular mechanisms underlying GPCR signaling is still far from being complete, thus impairing the design of effective and selective pharmaceuticals. OBJECTIVE: Understanding of GPCR function has been challenged even further by more recent experimental evidence that several of these receptors are organized in the cell membrane as homo- or hetero-oligomers, and that they may exhibit unique pharmacological properties. Given the complexity of these new signaling systems, researcher's efforts are turning increasingly to molecular modeling, bioinformatics and computational simulations for mechanistic insights of GPCR functional plasticity. METHODS: We review here current advances in the development and application of computational approaches to improve prediction of GPCR structure and dynamics, thus enhancing current understanding of GPCR signaling. RESULTS/CONCLUSIONS: Models resulting from use of these computational approaches further supported by experiments are expected to help elucidate the complex allosterism that propagates through GPCR complexes, ultimately aiming at successful structure-based rational drug design.

Comparison of stability predictions and simulated unfolding of rhodopsin structures

Developing a better mechanistic understanding of membrane protein folding is urgently needed because of the discovery of an increasing number of human diseases, where membrane protein instability and misfolding is involved. Towards this goal, we investigated folding and stability of 7-transmembrane (TM) helical bundles by computational methods. We compared the results of three different algorithms for predicting changes in stability of proteins against an experimental mutation dataset obtained for bacteriorhodopsin (BR) and mammalian rhodopsin and find that 61.6% and 70.6% of the mutation results can potentially be explained by known local contributors to the stability of the folded state of BR and mammalian rhodopsin, respectively. To obtain further information on the predicted folding pathway of 7-TM proteins, we conducted simulated thermal unfolding experiments of all available rhodopsin structures with resolution better than 3 angstroms using the Floppy Inclusions and Rigid Substructure Topography (FIRST) method (Jacobs, D. J., A. J. Rader, L. A. Kuhn and M. F. Thorpe [2001] Proteins 44, 150) described previously for a single mammalian rhodopsin structure (Rader et al. [2004] PNAS 101, 7246). In statistical comparison we found that structures of mammalian rhodopsin have a stability core that is characterized by long-range interactions involving amino acids close in space but distant in sequence comprising positions from both extracellular loop and TM regions. In contrast, BR-simulated unfolding does not reveal such a core but is dominated by interactions within individual and groups of TM helices, consistent with the two-stage hypothesis of membrane protein folding. Similar results were obtained for halo- and sensory rhodopsins as for BRs. However, the average folding core energies of sensory rhodopsins were in between those observed for mammalian rhodopsins and BRs hinting at a possible evolution of these structures toward a rhodopsin-like behavior. These results support the conclusion that although the two-stage model can explain the mechanisms of folding and stability of BR, it fails to account for the folding and stability of mammalian rhodopsin, even though the two proteins are structurally related.

The signaling pathway of rhodopsin

The signal-transduction mechanism of rhodopsin was studied by molecular dynamics (MD) simulations of the high-resolution, inactive structure in an explicit membrane environment. The simulations were employed to calculate equal-time correlations of the fluctuating interaction energy of residue pairs. The resulting interaction-correlation matrix was used to determine a network that couples retinal to the cytoplasmic interface, where transducin binds. Two highly conserved motifs, D(E)RY and NPxxY, were found to have strong interaction correlation with retinal. MD simulations with restraints on each transmembrane helix indicated that the major signal-transduction pathway involves the interdigitating side chains of helices VI and VII. The functional roles of specific residues were elucidated by the calculated effect of retinal isomerization from 11-cis to all-trans on the residue-residue interaction pattern. It is suggested that Glu134 may act as a "signal amplifier" and that Asp83 may introduce a threshold to prevent background noise from activating rhodopsin.

Combined solid state and solution NMR studies of alpha,epsilon-15N labeled bovine rhodopsin

Rhodopsin is the visual pigment of the vertebrate rod photoreceptor cell and is the only member of the G protein coupled receptor family for which a crystal structure is available. Towards the study of dynamics in rhodopsin, we report NMR-spectroscopic investigations of alpha,epsilon-15N-tryptophan labeled rhodopsin in detergent micelles and reconstituted in phospholipids. Using a combination of solid state 13C,15N-REDOR and HETCOR experiments of all possible 13C'(i-1) carbonyl/15N(i)-tryptophan isotope labeled amide pairs, and H/D exchange 1H,15N-HSQC experiments conducted in solution, we assigned chemical shifts to all five rhodopsin tryptophan backbone 15N nuclei and partially to their bound protons. 1H,15N chemical shift assignment was achieved for indole side chains of Trp35(1.30) and Trp175(4.65). 15N chemical shifts were found to be similar when comparing those obtained in the native like reconstituted lipid environment and those obtained in detergent micelles for all tryptophans except Trp175(4.65) at the membrane interface. The results suggest that the integrated solution and solid state NMR approach presented provides highly complementary information in the study of structure and dynamics of large membrane proteins like rhodopsin.

Retinitis pigmentosa associated with rhodopsin mutations: Correlation between phenotypic variability and molecular effects

Similar retinitis pigmentosa (RP) phenotypes can result from mutations affecting different rhodopsin regions, and distinct amino acid substitutions can cause different RP severity and progression rates. Specifically, both the R135L and R135W mutations (cytoplasmic end of H3) result in diffuse, severe disease (class A), but R135W causes more severe and more rapidly progressive RP than R135L. The P180A and G188R mutations (second intradiscal loop) exhibit a mild phenotype with regional variability (class B1) and diffuse disease of moderate severity (class B2), respectively. Computational and in vitro studies of these mutants provide molecular insights into this phenotypic variability.