Publisher Correction: Molecular dynamics of the histamine H3 membrane receptor reveals different mechanisms of GPCR signal transduction

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Molecular dynamics of the histamine H3 membrane receptor reveals different mechanisms of GPCR signal transduction

In this work, we studied the mechanisms of classical activation and inactivation of signal transduction by the histamine H3 receptor, a 7-helix transmembrane bundle G-Protein Coupled Receptor through long-time-scale atomistic molecular dynamics simulations of the receptor embedded in a hydrated double layer of dipalmitoyl phosphatidyl choline, a zwitterionic polysaturated ordered lipid. Three systems were prepared: the apo receptor, representing the constitutively active receptor; and two holo-receptors—the receptor coupled to the antagonist/inverse agonist ciproxifan, representing the inactive state of the receptor, and the receptor coupled to the endogenous agonist histamine and representing the active state of the receptor. An extensive analysis of the simulation showed that the three states of H3R present significant structural and dynamical differences as well as a complex behavior given that the measured properties interact in multiple and interdependent ways. In addition, the simulations described an unexpected escape of histamine from the orthosteric binding site, in agreement with the experimental modest affinities and rapid off-rates of agonists.

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

A complex view of GPCR signal transduction: Molecular dynamics of the histamine H3 membrane receptor

ABSTRACTIn this work, we study the mechanisms of classical activation and inactivation of signal transduction by the histamine H3 receptor, a 7-helix transmembrane bundle G-Protein Coupled Receptor through long-time-scale molecular dynamics simulations of the receptor embedded in a hydrated double layer of dipalmitoyl phosphatidyl choline, a zwitterionic poly-saturated ordered lipid. Three systems were prepared: the apo receptor, representing the constitutively active receptor; and two holo-receptors -the receptor coupled to the antagonist/inverse agonist ciproxifan and representing the inactive state of the receptor, and the receptor coupled to the endogenous agonist histamine and representing the active state of the receptor.An extensive analysis of the simulation shows that the three states of H3R present significant structural and dynamical differences, as well as a complex behavior given that the measured properties interact in multiple and inter-dependent ways. In addition, the simulations describe an unexpected escape of histamine from the orthosteric binding site, in agreement with the experimental modest affinities and rapid off-rates of agonists.

Engineering a model cell for rational tuning of GPCR signaling

G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between stimulus and response for each GPCR is difficult to predict due to diversity in natural signal transduction architecture and expression. Using genome engineering in yeast, we here constructed an insulated, modular GPCR signal transduction system to study how the response to stimuli can be predictably tuned using synthetic tools. We delineated the contributions of a minimal set of key components via computational and experimental refactoring, identifying simple design principles for rationally tuning the dose-response. Using four different receptors, we demonstrate how this enables cells and consortia to be engineered to respond to desired concentrations of peptides, metabolites and hormones relevant to human health. This work enables rational tuning of cell sensing, while providing a framework to guide reprogramming of GPCR-based signaling in more complex systems.

Molecular map of GNAO1-related disease phenotypes and reactions to therapy

The GNAO1 gene codes for the most commonly expressed Gα protein in the central nervous system. Pathogenic GNAO1 variants result in early-onset neurological phenotypes, sometimes with distinct epilepsy or movement disorder, and sometimes with both mani-festations in the same patient. The existing extensive knowledge about G-protein coupled receptor (GPCR) signaling provides the input needed to describe quantitatively how mutations modify the GPCR signal. This in turn allows rational interpretation of distinct phenotypes arising from mutations in GNAO1. In this work we outline a model that enables understanding of clinical phenotypes at a molecular level. The mutations affecting the catalytic pocket of GNAO1, we show, result in the improper withdrawal of the signal, and give rise to epileptic phenotypes (EPs). The converse is not true - some pure EPs are caused by mutations with no obvious impact on catalysis. Mutations close to the interface with GNAO1’s downstream effector block the signal propagation in that direction, and manifest as a movement disorder phenotype without epilepsy. Quantifying the reported reaction to therapy highlights the tendency of the latter group to be unresponsive to the therapies currently in use. We argue, however, that the majority of clinically described mutations can impact several aspects of GNAO1 function at once, resulting in the continuum of phenotypes observed in patients. The reasoning based on GNAO1 signaling model provides a precision medicine paradigm to aid clinicians in selecting effective categories of medication, and in addition, can suggest pragmatic targets for future therapies.