Determinants of Neutral Antagonism and Inverse Agonism in the ß2-Adrenergic Receptor.

Free-energy profiles for the activation/deactivation of the ß2-adrenergic receptor (ADRB2) with neutral antagonist and inverse agonist ligands have been determined with well-tempered multiple-walker (MW) metadynamics simulations. The inverse agonists carazolol and ICI118551 clearly favor single inactive conformational minima in both the binary and ternary ligand-receptor-G-protein complexes, in accord with the inverse-agonist activity of the ligands. The behavior of neutral antagonists is more complex, as they seem also to affect the recruitment of the G-protein. The results are analyzed in terms of the conformational states of the well-known microswitches that have been proposed as indicators of receptor activity.

General Metadynamics Protocol To Simulate Activation/Deactivation of Class A GPCRs: Proof of Principle for the Serotonin Receptor.

We present a generally applicable metadynamics protocol for characterizing the activation free-energy profiles of class A G-protein coupled receptors and a proof-of-principle study for the 5HT1A-receptor. The almost universal A100 activation index, which depends on five inter-helix distances, is used as the single collective variable in well-tempered multiple-walker metadynamics simulations. Here, we show free-energy profiles for the serotonin receptor as binary (apo-receptor + G-protein-α-subunit and receptor + ligand) and ternary complexes with two prototypical orthosteric ligands: the full agonist serotonin and the partial agonist aripiprazole. Our results are not only compatible with previously reported experimental and computational data, but they also allow differences between active and inactive conformations to be determined in unprecedented atomic detail, and with respect to the so-called microswitches that have been suggested as determinants of activation, giving insight into their role in the activation mechanism.

Activation/Deactivation Free-Energy Profiles for the ß2-Adrenergic Receptor: Ligand Modes of Action.

We use enhanced-sampling simulations with an effective collective variable to study the activation of the ß2-adrenergic receptor in the presence of ligands with different efficacy. The free-energy profiles are computed for the ligand-free (apo) receptor and binary (apo-receptor + G-protein α-subunit and receptor + ligand) and ternary complexes. The results are not only compatible with available experiments but also allow unprecedented structural insight into the nature of GPCR conformations along the activation pathway and their role in the activation mechanism. In particular, the simulations reveal an unexpected mode of action of partial agonists such as salmeterol and salbutamol that arises already in the binary complex without the G-protein. Specific differences in the polar interactions with residues in TM5, which are required to stabilize an optimal TM6 conformation that facilitates G-protein binding and receptor activation, play a major role in differentiating them from full agonists.

Basal Histamine H4 Receptor Activation: Agonist Mimicry by the Diphenylalanine Motif.

Histamine H4 receptor (H4 R) orthologues are G-protein-coupled receptors (GPCRs) that exhibit species-dependent basal activity. In contrast to the basally inactive mouse H4 R (mH4 R), human H4 R (hH4 R) shows a high degree of basal activity. We have performed long-timescale molecular dynamics simulations and rigidity analyses on wild-type hH4 R, the experimentally characterized hH4 R variants S179M, F169V, F169V+S179M, F168A, and on mH4 R to investigate the molecular nature of the differential basal activity. H4 R variant-dependent differences between essential motifs of GPCR activation and structural stabilities correlate with experimentally determined basal activities and provide a molecular explanation for the differences in basal activation. Strikingly, during the MD simulations, F16945.55 dips into the orthosteric binding pocket only in the case of hH4 R, thus adopting the role of an agonist and contributing to the stabilization of the active state. The results shed new light on the molecular mechanism of basal H4 R activation that are of importance for other GPCRs.

Universal Activation Index for Class A GPCRs.

An index of the activation of Class A G-protein-coupled receptors (GPCRs) has been trained using interhelix distances from a series of microsecond molecular-dynamics simulations and tested for 268 published X-ray structures. In a three-class model that includes intermediate structures, 63% of the active structures are classified in agreement with the experimental assignment, 81% of the intermediate structures, and 89% of the inactives. An alternative two-state model classifies 94% of the actives and 99% of the inactives correctly. The intermediate structures are distributed 2:1 between actives and inactives. X-ray structures with protein nanobodies give good agreement between the assigned activation state and the predictions of the model, whereby many active nanobody structures are predicted to be weakly active. The five interhelix Cα-Cα distances that occur in the model relate clearly to the established activation mechanism. The model is available as a Python script or via an interactive web page. It can thus be used to classify both experimental and computational GPCR structures.

An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands.

A generally applicable metadynamics scheme for predicting the free energy profile of ligand binding to G-protein-coupled receptors (GPCRs) is described. A common and effective collective variable (CV) has been defined using the ideally placed and highly conserved Trp6.48 as a reference point for ligand-GPCR distance measurement and the common orientation of GPCRs in the cell membrane. Using this single CV together with well-tempered multiple-walker metadynamics with a funnel-like boundary allows an efficient exploration of the entire ligand binding path from the extracellular medium to the orthosteric binding site, including vestibule and intermediate sites. The protocol can be used with X-ray structures or high-quality homology models (based on a high-quality template and after thorough refinement) for the receptor and is universally applicable to agonists, antagonists, and partial and reverse agonists. The root-mean-square error (RMSE) in predicted binding free energies for 12 diverse ligands in five receptors (a total of 23 data points) is surprisingly small (less than 1 kcal mol-1). The RMSEs for simulations that use receptor X-ray structures and homology models are very similar.

Differences between G-Protein-Stabilized Agonist-GPCR Complexes and their Nanobody-Stabilized Equivalents.

Protein nanobodies have been used successfully as surrogates for unstable G-proteins in order to crystallize G-protein-coupled receptors (GPCRs) in their active states. We used molecular dynamics (MD) simulations, including metadynamics enhanced sampling, to investigate the similarities and differences between GPCR-agonist ternary complexes with the α-subunits of the appropriate G-proteins and those with the protein nanobodies (intracellular binding partners, IBPs) used for crystallization. In two of the three receptors considered, the agonist-binding mode differs significantly between the two alternative ternary complexes. The ternary-complex model of GPCR activation entails enhancement of ligand binding by bound IBPs: Our results show that IBP-specific changes can alter the agonist binding modes and thus also the criteria for designing GPCR agonists.

Metadynamics simulations of ligand binding to GPCRs.

Recent developments in metadynamics simulation techniques for ligand binding to Class A GPCRs are described and the results obtained elucidated. The computational protocol makes good use of modern massively parallel hardware, making simulations of the binding/unbinding process routine. The simulations reveal unprecedented details of the ligand-binding pathways, including multiple binding sites in many cases. Free energies of binding are reproduced very well and the simulations allow prediction of the efficacy (agonist, antagonist etc.) of ligands.