G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

Abstract

The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs.

Biased Receptor Signaling in Drug Discovery

“…In addition, a great deal of experimental evidence since the proposal of the extended ternary complex model and the introduction of molecular dynamics into pharmacology has provided an alternative view, namely the selection of receptor conformations from a preexisting ensemble of similar but different conformations (Boehr et al, 2009;Dror et al, 2010Dror et al, , 2011Park, 2012;Nygaard et al, 2013;Motlagh et al, 2014). This ensemble of receptor conformations forms a dynamic system (Vardy and Roth, 2013;Manglik and Kobilka, 2014;Manglik et al, 2015), which then interacts with signaling systems through a full range of allosteric linkages (Monod et al, 1965;Changeux and Edelstein, 2005); these ideas have been discussed in terms of oscillating dynamic systems of multiple conformations (Cui and Karplus, 2008;Changeux and Edelstein, 2011) that produce "fluctuating networks" operating on a real-time scale of microseconds (Ichikawa et al, 2016). Conformational selection is where the ligand preferentially binds to a preexisting R a state, and conformational induction is where the ligand binds to the R i state to convert it to the R a state.…”

Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the β2AR Receptor

“…Even though the correct conformational space may have been searched, as indicated previously (Figure A), we aimed to evaluate the quality of our free energy landscapes by comparing the fraction of the β2AR population that resides in the active state. Thanks to the availability of a multitude of functional experiments measuring the efficacy of various ligands, − we could correlate our values to the %Emax values in relation to adrenaline, which was an available data point in all studies used. Before we could analyze the free energy surfaces, we ensured that convergence had been achieved.…”

“…Correlation between experimental (Emax values relative to adrenaline along the x -axis) − and computational activities (calculated fractions of the active population basins along the y -axis). The correlation line was fitted with least-squares, and the R-value was calculated based on this fitted line.…”

Biochemical insights into structure and function of arrestins

“…MD simulations give insights into complex stability, protein-protein contacts at the interaction interface, and allosteric interaction paths. They have been successfully applied to explore the dynamics of receptor-ligand interactions, which form the basis for biased signaling [133][134][135]. The recent surge of 3D data of arr-GPCR complexes from X-ray and cryo-EM has opened up new directions for MD studies of the arrreceptor interface.…”