Opioid receptors, a kind of G protein-coupled receptors
(GPCRs),
mainly mediate an analgesic response via allosterically transducing
the signal of endogenous ligand binding in the extracellular domain
to couple to effector proteins in the intracellular domain. The δ
opioid receptor (DOP) is associated with emotional control besides
pain control, which makes it an attractive therapeutic target. However,
its allosteric mechanism and key residues responsible for the structural
stability and signal communication are not completely clear. Here
we utilize the Gaussian network model (GNM) and amino acid network
(AAN) combined with perturbation methods to explore the issues. The
constructed fcfGNMMD, where the force constants are optimized
with the inverse covariance estimation based on the correlated fluctuations
from the available DOP molecular dynamics (MD) ensemble, shows a better
performance than traditional GNM in reproducing residue fluctuations
and cross-correlations and in capturing functionally low-frequency
modes. Additionally, fcfGNMMD can consider implicitly the
environmental effects to some extent. The lowest mode can well divide
DOP segments and identify the two sodium ion (important allosteric
regulator) binding coordination shells, and from the fastest modes,
the key residues important for structure stabilization are identified.
Using fcfGNMMD combined with a dynamic perturbation-response
method, we explore the key residues related to the sodium ion binding.
Interestingly, we identify not only the key residues in sodium ion
binding shells but also the ones far away from the perturbation sites,
which are involved in binding with DOP ligands, suggesting the possible
long-range allosteric modulation of sodium binding for the ligand
binding to DOP. Furthermore, utilizing the weighted AAN combined with
attack perturbations, we identify the key residues for allosteric
communication. This work helps strengthen the understanding of the
allosteric communication mechanism in δ opioid receptor and
can provide valuable information for drug design.