Method

Outward movement of TM1 occurs simultaneously with entry of the ligand into the binding pocket, and is observed most dramatically for the DIPP-NH2 peptide.

This is consistent with experimental evidence: μ and δ receptors form heterodimers with an interface at TM1 and 2. When the dimer is disrupted, morphine becomes

more effective in rats (He et al, 2011), suggesting that ag-onists may share these early steps in the binding path-way.

Consider a system where the ligand can be in one of four locations: the solvent, lipid membrane, protein surface, or binding site.

We perform an ensemble of MD simulations with several ligands in solution. A Markov state model is constructed from the results. Clusters in the model are selected for resampling if they are not hubs in the directed graph.

Performed iteratively, the method adaptively samples ligand- protein conformation space using much less simulation than tradi-tional MD.

Although none of the ligands finished binding to their crystallographic pose, they reached the ap-

proximate neighborhood of the pose. More sam-pling is currently being performed to validate the

method, but the entry pathway of each ligand into the pocket has been fairly well characterized.

Binding pathways for all the observed opioid antagonists shared a common entry point between TM1 and 2. This contrasts to paths obtained other G protein-coupled receptors, such as the β2 adrenergic receptor where li-gands enter between TM5 and 6.

At left, initial steps in the binding pathway of naltrindole to δOR are shown. Due to the larger

size of the ligand, the pathway is less sampled than for naloxone at μOR, shown left. For both ligands, entry oc-

curs on the side of the pocket, and the ligand slowly works its way fur-

ther inside. This is interesting as in crystal structures (right) the pocket appears open.

Pathway and mechanismof antagonist binding to opioid receptors

Robin M. Betz, Ron O. DrorStanford University

Opioid receptors are important drug tar-gets for pain relief. There is considerable interest in development of new drugs tar-geting these receptors without the addic-tive potential of currently available drugs.

Subtle changes on molecule substituents result in very different activity profiles at these receptors. Using all-atom molecu-lar dynamics (MD) simulation, we aim to predict both pathway and pose of ligand binding.

Using a novel adaptive sampling scheme, we simulated binding of several opioid antagonists (naloxone, naltrindole, and a bifunctional peptide DIPP-NH2) to either the μ or δ opioid receptor depending on ligand activity at each receptor.

There are crystal structures for each of the simulations, enabling validation of the fi-nal bound pose. Additionally, the binding pocket is relatively exposed.

Results

Goals

naloxone:high-affinity inverse agonist at μ, used to reverse opiate over-

doses

naltrindole:selective δ antagonist, non-peptide analog of endogenous peptide

enkephalin

DIPP-NH2:small peptide

H-Dmt-Tic-Phe-Phe-NH2, an-tagonist at δ, agonist at μ

Simulation ensemble

Markov State Model

Resampled clusters

Probable bound poseBinding pathway

TM1

TM2

PDB 4DKL

Entering pocket

In pocket