Unifying view of mechanical and functional hotspots across class A GPCRs

Dimeric Rhodopsin R135L Mutant-Transducin-like Complex Sheds Light on Retinitis Pigmentosa Misfunctions

Rhodopsin (RHO) is a light-sensitive pigment in the retina and the main prototypical protein of the G-protein-coupled receptor (GCPR) family. After receiving a light stimulus, RHO and its cofactor retinylidene undergo a series of structural changes that initiate an intricate transduction mechanism. Along with RHO, other partner proteins play key roles in the signaling pathway. These include transducin, a GTPase, kinases that phosphorylate RHO, and arrestin (Arr), which ultimately stops the signaling process and promotes RHO regeneration. A large number of RHO genetic mutations may lead to very severe retinal dysfunction and eventually to impaired dark adaptation disease called autosomal dominant retinitis pigmentosa (adRP). In this study, we used molecular dynamics (MD) simulations to evaluate the different behaviors of the dimeric form of wild-type RHO (WT dRHO) and its mutant at position 135 of arginine to leucine (dR135L), both in the free (noncomplexed) and in complex with the transducin-like protein (Gtl). Gtl is a heterotrimeric model composed of a mixture of human and bovine G proteins. Our calculations allow us to explain how the mutation causes structural changes in the RHO dimer and how this can affect the signal that transducin generates when it is bound to RHO. Moreover, the structural modifications induced by the R135L mutation can also account for other misfunctions observed in the up- and downstream signaling pathways. The mechanism of these dysfunctions, together with the transducin activity reduction, provides structure-based explanations of the impairment of some key processes that lead to adRP.

Ligand based conformational space studies of the μ-opioid receptor

BACKGROUND

G protein-coupled receptors (GPCRs) comprise a family of membrane proteins that can be activated by a variety of external factors. The μ-opioid receptor (MOR), a class A GPCR, is the main target of morphine. Recently, enhanced sampling molecular dynamics simulations of a constitutively active mutant of MOR in its apo form allowed us to capture the novel intermediate states of activation, as well as the active state. This prompted us to apply the same techniques to wild type MOR in complex with ligands, in order to explore their contributions to the receptor conformational changes in the activation process.

METHODS

MOR was modeled in complex with agonists (morphine, BU72), a partial agonist (naloxone benzoylhydrazone) and an antagonist (naloxone). Replica exchange with solute tempering (REST2) molecular dynamics simulations were carried out for all systems. Trajectory frames were clustered, and the activation state of each cluster was assessed by two different methods.

RESULTS

Cluster sizes and activation indices show that while agonists stabilized structures in a higher activation state, the antagonist behaved oppositely. Morphine tends to drive the receptor towards increasing R165-T279 distances, while naloxone tends to increase the NPxxYA motif conformational change.

CONCLUSIONS

Despite not observing a full transition between inactive and active states, an important conformational change of transmembrane helix 5 was observed and associated with a ligand-driven step of the process.

GENERAL SIGNIFICANCE

The activation process of GPCRs is widely studied but still not fully understood. Here we carried out a step forward in the direction of gaining more details of this process.

Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors

We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity.

Unsupervised Classification of G-Protein Coupled Receptors and Their Conformational States Using IChem Intramolecular Interaction Patterns

Over the past decade, the ever-growing structural information on G-protein coupled receptors (GPCRs) has revealed the three-dimensional (3D) characteristics of a receptor structure that is competent for G-protein binding. Structural markers are now commonly used to distinguish GPCR functional states, especially when analyzing molecular dynamics simulations. In particular, the position of the sixth helix within the seven transmembrane domains (TMs) is directly related to the coupling of the G-protein. Here, we show that the structural pattern defined by transmembrane intramolecular interactions (hydrogen bonds excluding backbone/backbone interactions, ionic bonds and aromatic interactions) is suitable for comparison of GPCR 3D structures and unsupervised distinction of the receptor states. First, we analyze a microsecond long molecular dynamic simulation of the human ß2-adrenergic receptor (ADRB2). Clustering of the 3D structures by pattern similarity identifies stable states which match the conformational classes defined by structural markers. Furthermore, the method directly spots the few state-specific interactions. Transforming pattern into graph, we extend the method to the comparison of different GPCRs. Clustering all GPCR experimentally determined structures by clique relative size first separates receptors, then their conformational states, thereby suggesting that the interaction patterns are specific of the receptor sequence and that the interaction signatures of conformational states are not shared across distant homologues.

Key residues controlling bidirectional ion movements in Na+/Ca2+ exchanger

Prokaryotic and eukaryotic Na+/Ca2+ exchangers (NCX) control Ca2+ homeostasis. NCX orthologs exhibit up to 104-fold differences in their turnover rates (kcat), whereas the ratios between the cytosolic (cyt) and extracellular (ext) Km values (Kint = KmCyt/KmExt) are highly asymmetric and alike (Kint ≤ 0.1) among NCXs. The structural determinants controlling a huge divergence in kcat at comparable Kint remain unclear, although 11 (out of 12) ion-coordinating residues are highly conserved among NCXs. The crystal structure of the archaeal NCX (NCX_Mj) was explored for testing the mutational effects of pore-allied and loop residues on kcat and Kint. Among 55 tested residues, 26 mutations affect either kcat or Kint, where two major groups can be distinguished. The first group of mutations (14 residues) affect kcat rather than Kint. The majority of these residues (10 out of 14) are located within the extracellular vestibule near the pore center. The second group of mutations (12 residues) affect Kint rather than kcat, whereas the majority of residues (9 out 12) are randomly dispersed within the extracellular vestibule. In conjunction with computational modeling-simulations and hydrogen-deuterium exchange mass-spectrometry (HDX-MS), the present mutational analysis highlights structural elements that differentially govern the intrinsic asymmetry and transport rates. The key residues, located at specific segments, can affect the characteristic features of local backbone dynamics and thus, the conformational flexibility of ion-transporting helices contributing to critical conformational transitions. The underlying mechanisms might have a physiological relevance for matching the response modes of NCX variants to cell-specific Ca2+ and Na+ signaling.

Structural dynamics is a determinant of the functional significance of missense variants

Discrimination of clinically relevant mutations from neutral mutations is of paramount importance in precision medicine and pharmacogenomics. Our study shows that current computational predictions of pathogenicity, mostly based on analysis of sequence conservation, may be improved by considering the changes in the structural dynamics of the protein due to point mutations. We introduce and demonstrate the utility of a classifier that takes advantage of efficient evaluation of structural dynamics by elastic network models.

Accurate evaluation of the effect of point mutations on protein function is essential to assessing the genesis and prognosis of many inherited diseases and cancer types. Currently, a wealth of computational tools has been developed for pathogenicity prediction. Two major types of data are used to this aim: sequence conservation/evolution and structural properties. Here, we demonstrate in a systematic way that another determinant of the functional impact of missense variants is the protein’s structural dynamics. Measurable improvement is shown in pathogenicity prediction by taking into consideration the dynamical context and implications of the mutation. Our study suggests that the class of dynamics descriptors introduced here may be used in conjunction with existing features to not only increase the prediction accuracy of the impact of variants on biological function, but also gain insight into the physical basis of the effect of missense variants.