Proposed Mode of Binding and Action of Positive Allosteric Modulators at Opioid Receptors

Abstract

Available crystal structures of opioid receptors provide a high-resolution picture of ligand binding at the primary (“orthosteric”) site, that is, the site targeted by endogenous ligands. Recently, positive allosteric modulators of opioid receptors have also been discovered, but their modes of binding and action remain unknown. Here, we use a metadynamics-based strategy to efficiently sample the binding process of a recently discovered positive allosteric modulator of the δ-opioid receptor, BMS-986187, in the presence of the orthosteric agonist SNC-80, and with the receptor embedded in an explicit lipid–water environment. The dynamics of BMS-986187 were enhanced by biasing the potential acting on the ligand–receptor distance and ligand–receptor interaction contacts. Representative lowest-energy structures from the reconstructed free-energy landscape revealed two alternative ligand binding poses at an allosteric site delineated by transmembrane (TM) helices TM1, TM2, and TM7, with some participation of TM6. Mutations of amino acid residues at these proposed allosteric sites were found to either affect the binding of BMS-986187 or its ability to modulate the affinity and/or efficacy of SNC-80. Taken together, these combined experimental and computational studies provide the first atomic-level insight into the modulation of opioid receptor binding and signaling by allosteric modulators.

Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations

“…Multiple walker metadynamics [39] increases the efficiency of metadynamics by running several copies of the system in parallel. For the recently published simulations of the binding of BMS-986187 to DOR [8], two CVs were biased: (1) the distance between the centers-of-mass of the transmembrane (TM) bundle of the protein and the heavy atoms of the ligand and (2) a measure of the polar and hydrophobic contacts formed between the ligand and receptor sidechains. CV2 was defined as: $$true\sum _{\begin{array}{c}left\text{Ligand}\\ \text{Polar}\end{array}}{\displaystyle \sum _{\begin{array}{l}\text{Receptor}\\ \text{Polar}\end{array}}\frac{1-{\left(r_{ij}/r_0\right)}^6}{1-{\left(r_{ij}/r_0\right)}^{12}}+{\displaystyle \sum _{\begin{array}{l}\text{Ligand}\\ \text{Hydroph}.\end{array}}true\sum _{\begin{array}{c}left\text{Receptor}\\ \text{Hydroph}.\end{array}}\frac{1-{\left(r_{ij}/r_0\right)}^6}{1-{\left(r_{ij}/r_0\right)}^{12}}}}$$ where r ij is the distance between the atoms of the ligand and the receptor and r 0 was set to 5 Å.…”

“…MD simulations have been used by our group [6–8] and others (e.g., [9–11]) to answer two key questions with respect to functionally selective and allosteric ligands targeting GPCRs: (1) what are the energetically preferred binding pathways and modes of ligand binding, and (2) how does the ligand transfer information from the ligand binding site to the intracellular side of the receptor? The ability of long timescale, unbiased MD simulations to predict the crystallographic binding pose of a small molecule, as well as to characterize its binding pathway to a GPCR crystal structure, was first shown for the binding of several beta blockers to the β 2 adrenergic receptor (β 2 AR) [10].…”

“…Long unbiased MD simulations have recently been used to predict poses of TRV-130 to the μ-opioid receptor (MOR) [7], allosteric ligands to the M2 muscarinic receptor (M2) [9], and ML056 to the sphingosine-1-phosphate receptor 1 (S1P 1 R) [12]. We pioneered the use of metadynamics [13] to study ligand binding to GPCRs [14] and have recently used this enhanced MD algorithm to predict the binding pose of a PAM, BMS-986187, to the δ-opioid receptor (DOR) [8], as well as kurkinorin, a G protein-biased agonist, to MOR [6]. Notably, our experimentally validated allosteric binding site of BMS-986187 at DOR [8] was not similar to any of the top-ranked poses from docking with Glide XP version 6.2 [15].…”

“…We pioneered the use of metadynamics [13] to study ligand binding to GPCRs [14] and have recently used this enhanced MD algorithm to predict the binding pose of a PAM, BMS-986187, to the δ-opioid receptor (DOR) [8], as well as kurkinorin, a G protein-biased agonist, to MOR [6]. Notably, our experimentally validated allosteric binding site of BMS-986187 at DOR [8] was not similar to any of the top-ranked poses from docking with Glide XP version 6.2 [15]. While accelerated MD (aMD) [16] was able to characterize the binding of the partial agonist arecoline and the agonist acetylcholine to the M3 muscarinic receptor [17], neither aMD [17] nor unbiased MD simulations [11] were able to recapitulate the crystallographic binding pose of the much larger and flexible antagonist tiotropium to M3, which shows that there are still limitations to these methods.…”

“…Here, we discuss the computational methods that we used to predict energetically preferred binding poses of two functionally selective ligands, TRV-130 [7] and kurkinorin [6], and an allosteric ligand, BMS-986187 [8], as well as the analysis that led us to identify interesting allosteric communication differences in MOR between the classical unbiased agonist morphine and the G protein-biased agonist TRV-130 [7]. …”